Microsoft Practice Questions, Discussions & Exam Topics by our Authors
You have an Azure Data Factory pipeline named pipeline1 that contains a data flow activity named activity1. You need to run pi...When running a Data Flow activity in Azure Data Factory, the runtime used to execute the activity depends on the type of activity and where the data resides. Let's evaluate each runtime option based on this scenario: A) Azure Integration runtime - Explanation: The Azure Integration Runtime (IR) is the default runtime for Data Flow activities in Azure Data Factory. It is used to execute data flows that are hosted on Azure and interact with cloud-based data sources. This runtime is managed by Azure and provides the necessary compute resources for executing the data flow activity in a fully managed environment. - Consideration: Since you are running a data flow activity, and Data Flows are typically processed using the Azure IR (unless you're using a self-hosted IR for on-premises data), the Azure Integration Runtime is the appropriate and most common runtime for this scenario. - Conclusion: This is the correct answer, as it is specifically designed to execute Data Flow activities in Azure Data Factory. B) Self-hosted integration runtime - Explanation: The Self-hosted Integration Runtime is primarily used for hybrid data movement, where data is transferred between on-premises data sources and Azure. It is also used fo... Author: Akash · Last updated Jul 5, 2026 |
SNAPSHOT - You have an Azure subscription that contains an Azure Synapse Analytics workspace named workspace1. Workspace1 contains a dedicated SQL pool named SQLPool1 and an Apache Spark pool named sparkpool1. Sparkpool1 contains a DataFrame named pyspark_df. You need to write the contents of pyspark_df to a table in SQLPool1 by using a PySpark noteb...Author: Lucas · Last updated Jul 5, 2026 |
You have an Azure data factory named ADF1 and an Azure Synapse Analytics workspace that contains a pipeline named SynPipeLine1. SynPipeLine1 includes a Notebook activity. You create a pipeline in ADF1 named ADFPipeline1....To invoke SynPipeLine1 from ADFPipeline1 in Azure Data Factory (ADF1), let's analyze the available options: A) Web - Explanation: The Web activity in Azure Data Factory is typically used for invoking external services via HTTP requests, such as REST APIs. It is not specifically designed for invoking other Azure Data Factory pipelines or Synapse pipelines. - Consideration: The Web activity is useful for interacting with external services or endpoints, but it is not the right choice for invoking another Azure Synapse Analytics pipeline or another ADF pipeline. - Conclusion: This option is not appropriate, as it's designed for HTTP-based external integrations, not for invoking pipelines within ADF or Synapse. B) Spark - Explanation: The Spark activity is used to execute Apache Spark jobs (such as notebooks or JAR files) within Azure Data Factory or Azure Synapse Analytics. This activity is meant for running Spark-based jobs but is not used for invoking or triggering other pipelines. - Consideration: Since you want to invoke a pipeline and not run a Spark job, the Spark activity is not suitable for this task. - Conclusion: This option is not relevant for triggering another pipeline. C) Custom - Explanation: The Custom activity in Azure Data Factory is used when you need to run custom code in a specific environment, such as running an Azure function or custom logic within a container. - Consideration: While Custom activities are powerful for custom tasks, invoking another pipeline (like SynPipeLine1) doesn't require custom logic and can be done through a simpler mechanism, l... Author: Benjamin · Last updated Jul 5, 2026 |
SNAPSHOT - You have an Azure data factory that contains the linked service shown in the following exhibit. Use the drop-down menus to select the answer choice that completes each statement based on ...Author: Siddharth · Last updated Jul 5, 2026 |
SNAPSHOT - In Azure Data Factory, you have a schedule trigger that is scheduled in Pacific Time. Pacific Time observes daylight saving time. The trigger has the following JSON file. Use the drop-down menus to select the answer choice that c...Author: Aria · Last updated Jul 5, 2026 |
You have an Azure Synapse Analytics dedicated SQL pool. You need to create a pipeline that will execute a stored procedure in the dedicated SQL pool and use the returned result set as the input for a downstream activity. The s...To create a pipeline in Azure Synapse Analytics dedicated SQL pool that executes a stored procedure and uses the returned result set as the input for a downstream activity, let's evaluate each option: A) U-SQL - Explanation: U-SQL is a data processing language used primarily in Azure Data Lake Analytics (which has now been deprecated) for processing big data. It is not directly related to the Azure Synapse Analytics dedicated SQL pool or executing stored procedures. - Consideration: U-SQL would not be suitable for executing stored procedures in a dedicated SQL pool and retrieving result sets. - Conclusion: This option is not relevant to your requirements. B) Stored Procedure - Explanation: The Stored Procedure activity in Azure Data Factory and Azure Synapse Analytics allows you to execute stored procedures in Azure Synapse SQL pools (formerly SQL Data Warehouse). The stored procedure can return result sets, and the result can be passed on to downstream activities in the pipeline. - Consideration: This is the best fit for the requirement. The Stored Procedure activity in a pipeline can directly execute a stored procedure and can handle result sets for downstream activities. It's a simple and native solution for this use case. - Conclusion: This is the correct and most efficient choice, as it allows for minimal development effort while meeting the requirements. ... Author: Aria · Last updated Jul 5, 2026 |
You have an Azure SQL database named DB1 and an Azure Data Factory data pipeline named pipeline1. From Data Factory, you configure a linked service to DB1. In DB1, you create a stored procedure named SP1. SP1 returns a single row of data that has four columns. You need to add an activity to pipeline1 to execute SP1. The solution must ensure that the values in the columns are stored as pipeline...In the scenario where we need to execute a stored procedure (SP1) from Azure Data Factory (ADF) and store the result of the execution (i.e., the values of the columns in the returned row) as pipeline variables, let's evaluate each of the available options: A) Script - Explanation: The Script activity in Azure Data Factory allows the execution of custom SQL scripts, including stored procedures. While the Script activity can run SP1, it does not have a straightforward mechanism to automatically store the result set of the stored procedure as pipeline variables. - Consideration: To extract the result set and store it as pipeline variables, additional scripting or processing would be needed. This increases complexity compared to other options. - Conclusion: This option can technically execute the stored procedure, but it's not the most efficient or recommended solution for storing the result set as pipeline variables. B) Copy - Explanation: The Copy activity is used to copy data from a source to a destination. It’s typically used for moving data between different data stores, not for executing stored procedures. The Copy activity does not support executing stored procedures directly or extracting and storing results as pipeline variables. - Consideration: The Copy activity is not designed for interacting with a result set from a stored procedure or manipulating pipeline variables. - Conclusion: This option is... Author: Scarlett · Last updated Jul 5, 2026 |
You have an Azure data factory named ADF1. You currently publish all pipeline authoring changes directly to ADF1. You need to implement version control for the changes made to pipeline artifacts. The solution must ensure that you can apply version control to the resources currently defined in the Azure Data Factory Studio for ADF1...To implement version control for the changes made to pipeline artifacts in Azure Data Factory (ADF1), you need to set up a proper version control mechanism and configure it to integrate with the current Azure Data Factory environment. Let's evaluate each of the provided options: A) From the Azure Data Factory Studio, run Publish All. - Explanation: The Publish All option in Azure Data Factory Studio is used to publish pipeline changes and other artifacts to the Data Factory service. However, this does not provide version control; it simply pushes the changes to the live environment. - Consideration: While this action publishes the changes, it does not introduce version control. Therefore, this is not a valid option for implementing version control. - Conclusion: This option is not relevant for implementing version control. B) Create an Azure Data Factory trigger. - Explanation: Triggers in Azure Data Factory are used to schedule and automate the execution of pipelines. They do not relate to version control in any way. - Consideration: Triggers help in orchestrating when a pipeline runs, but they don't manage version control or source code management. - Conclusion: This option is irrelevant for version control purposes. C) Create a Git repository. - Explanation: Creating a Git repository is a crucial step in setting up version control. Azure Data Factory integrates with Git repositories such as GitHub, Azure Repos, or Bitbucket to manage versions of pipeline artifacts. - Consideration: The creation of a Git repository is a critical part of setting up version control, as it will store all the versions of the pipeline artifacts and facilitate collaboration and version tracking. - Conclusion: This is an essential action to implement... Author: Michael · Last updated Jul 5, 2026 |
You have an Azure data factory named ADF1 that contains a pipeline named Pipeline1. Pipeline1 must execute every 30 minutes with a 15-minute offset. You need to create a trigger for Pipeline1. The trigger must meet the following requirements: * Backfill data from the beginning of the day to the current time. * If Pipeline1 fails, ensure that the pipeline can re-execute within the same 30-...To solve this problem, let's break down the requirements: 1. Backfill data from the beginning of the day to the current time: This means the trigger should have the capability to run from a specific start time (e.g., the start of the day) and continue executing at regular intervals. 2. Re-execute within the same 30-minute period if the pipeline fails: This means the pipeline needs to be re-triggered in case of failure during the execution window, ensuring that it doesn't miss the 30-minute window. 3. Ensure that only one concurrent pipeline execution can occur: This suggests that there should be no overlapping executions of the pipeline. We need to manage concurrency. 4. Minimize development and configuration effort: The solution should require minimal effort for setup, configuration, and maintenance. Now, let's analyze each trigger option: A) Schedule Trigger - A schedule trigger allows you to specify a recurring time-based schedule (e.g., every 30 minutes). - However, this type of trigger does not provide built-in backfilling from the start of the day and does not address the requirement to re-trigger the pipeline in case of failure. - Also, it can lead to multiple concurrent executions unless configured carefully, which may not meet the requirement of only allowing one concurrent execution. B) Event-based Trigger - An event-based trigger is used to trigger pipelines based on events like the arrival of a file in a storage location. - While event-based triggers are useful for certain scenarios, they do not meet the requirements rela... Author: Aditya · Last updated Jul 5, 2026 |
You have an Azure Data Lake Storage Gen2 account named account1 and an Azure event hub named Hub1. Data is written to account1 by using Event Hubs Capture. You plan to query account by using an Apache Spark pool in Azure Synapse Analytics. You need to create a notebook and ingest the data from account1. The solution must meet the following requirements: ...To meet the requirements for querying data from Azure Data Lake Storage Gen2 and ingesting it into an Apache Spark pool in Azure Synapse Analytics, let's break down the factors involved in selecting the right data format: Requirements: 1. Retrieve multiple rows of records in their entirety: This means we want a data format that allows for efficient reading of multiple records, ideally in a way that optimizes Spark processing. 2. Minimize query execution time: The data format should be optimized for fast read operations, minimizing the time it takes to execute queries. 3. Minimize data processing: The data format should require minimal transformations and decoding during query execution, reducing the need for processing. Analysis of Data Formats: A) Parquet - ORC, Avro - Parquet is a columnar storage format, highly optimized for reading and querying data in big data environments, such as with Spark and Synapse Analytics. It allows for efficient compression and minimizes disk I/O by storing data in columnar format, which significantly speeds up query execution and reduces data processing overhead. - ORC (Optimized Row Columnar) is also a columnar format that provides similar benefits to Parquet. It has more optimized compression techniques and is widely used in the Hadoop ecosystem. - Avro is a row-based storage format that is more suitable for scenarios where data schema evolution and row-level access are critical. While Avro is compact and fast for row-level access, it doesn’t provide the same level of performance and query optimization for large-scale data analysis like columnar formats (Parquet or ... Author: Madison · Last updated Jul 5, 2026 |
You have an Azure Blob Storage account named blob1 and an Azure Data Factory pipeline named pipeline1. You need to ensure that pipeline1 runs when a file is deleted from a container in blob1. Th...To meet the requirement of triggering Azure Data Factory pipeline1 when a file is deleted from a container in Azure Blob Storage, we need to choose the appropriate trigger based on the scenario and minimize development effort. Analysis of Available Trigger Options: A) Schedule Trigger - A schedule trigger runs the pipeline at specified intervals (e.g., daily, hourly). This type of trigger is not suitable for the specific requirement of responding to file deletion events in real time. - It would not minimize the development effort in this case because it would require checking the file deletion status periodically, which is inefficient and doesn't directly respond to the deletion event. - Rejected: Not event-driven and inefficient for this scenario. B) Storage Event Trigger - Storage event triggers are designed specifically to trigger actions in response to events in Azure Storage, such as file creation, modification, or deletion. - This trigger can listen for Blob storage events like BlobDeleted events, which are generated when a file is deleted from a container in Blob Storage. - This is a perfect fit for the requirement because it directly responds to the event of file deletion without the need for polling or manual checks. - Selected: I... Author: Rahul · Last updated Jul 5, 2026 |
SNAPSHOT - You have Azure Data Factory configured with Azure Repos Git integration. The collaboration branch and the publish branch are set to the default values. You have a pipeline named pipeline1. You build a new version of pipeline1 in a branch named feature1. From the Data Factory Studio, you select Publish. The source code of which branch will be built, and which branch will cont...Author: Matthew · Last updated Jul 5, 2026 |
DRAG DROP - You have an Azure subscription that contains an Azure data factory. You are editing an Azure Data Factory activity JSON. The script needs to copy a file from Azure Blob Storage to multiple destinations. The solution must ensure that the source and destination files have consistent folder paths. How should you complete the script? To answer, drag the appropriate values to the correct targets. Each...Author: Aarav · Last updated Jul 5, 2026 |
You are building a data flow in Azure Data Factory that upserts data into a table in an Azure Synapse Analytics dedicated SQL pool. You need to add a transformation to the data flow. The transformation must specify logic indicating when a row from th...In this scenario, where you need to add logic to specify when a row from the input data must be upserted into a sink (Azure Synapse Analytics dedicated SQL pool), the appropriate transformation type is Alter Row. Reasoning: A) Join: - The Join transformation is typically used to combine data from two or more sources based on a common key. While joins are essential for combining datasets, they do not inherently provide a mechanism to control upsert behavior. Therefore, this option is not relevant for upserting rows into a sink. - Use case: Typically used when combining multiple datasets, not for conditional upsert logic. B) Alter Row: - The Alter Row transformation is specifically designed to handle row-level operations such as insert, update, and delete. This transformation allows you to apply conditional logic based on the values of the incoming data, defining whether a row should be inserted, updated, or ignored based on your requirements. - For an upsert scenario, you can use Alter Row to define the conditions under which data should be inserted or updated in the sink. This gives you full control over the upsert logic, making it the id... Author: Ryan · Last updated Jul 5, 2026 |
You have an on-premises database named db1 and a set-hosted integration runtime. You have an Azure subscription that contains an Azure Data Lake Storage account named dl1. You need to develop four data pipeline projects that will use Microsoft Power Query to copy data from db1 to dl1. The solution must meet the following requirements: * All pipelines must use th...In this case, the most appropriate option to meet the requirements is C) Azure Data Factory. Reasoning: A) Azure Synapse Analytics: - Azure Synapse Analytics is a comprehensive analytics service that integrates big data and data warehousing. While it can work with on-premises data sources and copy data to various Azure destinations, its main strength is in data analytics and data warehousing, rather than in building data pipeline projects specifically for Power Query integration. - Synapse may not be the most suitable solution for simple data movement and transformation tasks, especially when you need separate Git repositories for each project. - Use case: Typically used for big data analytics and data warehousing scenarios. B) Azure Logic Apps: - Azure Logic Apps is used for building workflows that automate tasks and integrate services. While it is excellent for orchestrating workflows between various services (e.g., triggering actions based on events), it is not primarily designed for creating data pipelines that involve copying and transforming data from an on-premises database to Azure Data Lake Storage using Power Query. - Use case: Ideal for process automation and integrating different services but not for data transformation and storage in data lakes. C) Azure Data Factory: - Azure Data Factory is specifically designed for building data pipelines. It supports various transformations and can connect to both cloud and on-premises data sources. - Self-hosted integration runtime in Azure Data Factory allows you to securely connect to on-premises data sources like db1. - Power Query integ... Author: Aria · Last updated Jul 5, 2026 |
You have the Azure Synapse Analytics pipeline shown in the following exhibit. You need to add a set variable activity to the pipeline to ensure that after the pipeline's completion, the status of the ...To ensure that the Azure Synapse Analytics pipeline completes successfully, regardless of any failure in its activities, you need to configure the Set Variable activity in a way that forces the pipeline to end successfully. Reasoning: A) A skipped dependency on the Upon Failure activity: - This option involves setting a skipped dependency on an activity that runs in case of failure. If you use a skipped dependency on a failure condition, the pipeline might skip certain actions, which does not ensure that the pipeline will complete successfully. In this scenario, if the pipeline fails and the "Upon Failure" activity is skipped, it does not force a successful completion. - Use case: Skipped dependencies typically allow an activity to be skipped under certain conditions, but this does not guarantee a successful pipeline outcome. B) A skipped dependency on the Upon Success activity: - Setting a skipped dependency on the Upon Success activity would allow the pipeline to skip a task or action if the pipeline is successful. However, this would not ensure the pipeline is forced to complete successfully if any errors occur in the activities. A skipped dependency on a success condition might inadvertently skip important tasks and won't address a failure scenario. - Use case: This would be useful when skipping a task if the pipeline is successful, but not for guaranteeing success in case of failure. C) A success dependency on the Business Activity That Fails activity: - A success de... Author: Sam · Last updated Jul 5, 2026 |
You have an on-premises Linux server that contains a database named DB1. You have an Azure subscription that contains an Azure data factory named ADF1 and an Azure Data Lake Storage account named ADLS1. You need to create a pipeline in ADF1 that wi...To copy data from the on-premises Linux server (DB1) to Azure Data Lake Storage (ADLS1) using Azure Data Factory (ADF1), you should use a self-hosted integration runtime. Reasoning: A) Self-hosted integration runtime: - Self-hosted integration runtime (IR) is specifically designed to bridge the gap between on-premises data sources (like DB1) and Azure services (like ADLS1). This integration runtime allows Azure Data Factory to securely access and transfer data from on-premises sources to cloud destinations. - In this scenario, since DB1 is located on an on-premises Linux server, the self-hosted IR is required to securely access and read data from the on-premises database, and then copy it to Azure Data Lake Storage (ADLS1). - The self-hosted IR can be installed on a machine in your on-premises environment, which would allow it to connect to DB1 and perform the data transfer to ADLS1. - Use case: Used when the data source is on-premises (in this case, a Linux server) and the destination is in the cloud (Azure Data Lake Storage). B) Azure integration runtime: - Azure integration runtime (IR) is used for moving data between cloud-based data stores or services within Azure. It does not support access to on-premises data sourc... Author: Daniel · Last updated Jul 5, 2026 |
DRAG DROP - You have an Azure Data Lake Storage account named account1. You use an Azure Synapse Analytics serverless SQL pool to access sales data stored in account1. You need to create a bar chart that displays sales by product. The solution must minimize development effort. In which order should ...Author: Nia · Last updated Jul 5, 2026 |
DRAG DROP - You have an Azure Synapse Analytics dedicated SQL pool. You need to create a copy of the data warehouse and make the copy available for 28 days. The solution must minimize costs. Which three actions should you perform in sequence? To answer...Author: Victoria · Last updated Jul 5, 2026 |
SNAPSHOT - You have an Azure Synapse Analytics workspace that contains an Apache Spark pool named Pool1. You need to read data from a CSV file and write the data to a Delta table by using Pool1. How should you complete the PySpark code? To a...Author: John · Last updated Jul 5, 2026 |
SNAPSHOT - You have an Azure Data Lake Storage account that contains one CSV file per hour for January 1, 2020, through January 31, 2023. The files are partitioned by using the following folder structure. You need to query the files by using an Azure Synapse Analytics serverless SQL pool. The solution must return the row count of each ...Author: Siddharth · Last updated Jul 5, 2026 |
SNAPSHOT - You have an Azure Synapse Analytics dedicated SQL pool named Pool1 that contains an external table named Sales. Sales contains sales data. Each row in Sales contain data on a single sale, including the name of the salesperson. You need to implement row-level security (RLS). The solution must ensure that the salespeople can access only their...Author: Zara1234 · Last updated Jul 5, 2026 |
You have an Azure Data Factory pipeline named P1. You need to schedule P1 to run at 10:15 AM, 12:15 PM, 2:15 PM, and 4:15 PM every day. Which freq...To schedule the Azure Data Factory pipeline P1 to run at specific times (10:15 AM, 12:15 PM, 2:15 PM, and 4:15 PM) every day, you should select the appropriate frequency and interval for the trigger. Reasoning: A) Frequency: Month - Interval: 1: - The Monthly frequency with an interval of 1 would cause the pipeline to run every month, not every day. This option doesn't meet the requirement to run multiple times a day. - Use case: This is used when you want to schedule a pipeline to run monthly, not daily. B) Frequency: Day - Interval: 1: - The Daily frequency with an interval of 1 is the correct option. This setup ensures that the pipeline will run once every day. However, the times you specified (10:15 AM, 12:15 PM, 2:15 PM, and 4:15 PM) suggest multiple runs per day. While this option enables daily scheduling, you still need to specify the times for the trigger. - Use case: This is ideal for daily schedules, but you would also need to define the specific times for the schedule (e.g., 10:15 AM, 12:15 PM, etc.) under the trigger’s advanced schedule settings. C) Frequency: Minute - Interval: 60: - The Minute frequency with an interval of 60 would trig... Author: Sofia2021 · Last updated Jul 5, 2026 |
You are creating an Azure Data Factory pipeline. You need to add an activity to the pipeline. The activity must execute a Transact-SQL stored procedure that has the following characteristics: * Returns the number of sales invoi...When working with Azure Data Factory (ADF) and needing to execute a Transact-SQL stored procedure that meets the specified criteria, you must choose the correct type of activity based on the nature of the stored procedure and its requirements. Let’s analyze each option in detail: Option A: Stored Procedure - Description: The Stored Procedure activity in ADF is designed to execute a stored procedure in a database. This activity allows for the execution of a Transact-SQL stored procedure, and it can be configured to run a procedure that doesn’t take any parameters or returns results. - Why it fits: Since the stored procedure returns the number of sales invoices for a current date and does not require input parameters, the Stored Procedure activity is the most appropriate choice. You can directly run this stored procedure, and it’s suited for executing T-SQL procedures that return values or perform other operations. - Conclusion: This option directly matches the task requirements. Option B: Get Metadata - Description: The Get Metadata activity is used to retrieve metadata information about a specific data source, such as tables, files, or columns. It can be used to check the existence of data or to get properties like row count, file size, etc. - Why it’s rejected: The Get Metadata activity is used for metadata retrieval, not for executing stored procedures. This activity cannot execute SQL queries or procedures, so it’s not suitable for the scenario. - Conclusion: This option is irrelevant to the task. Option C: Append Variable - Description: The ... Author: Scarlett · Last updated Jul 5, 2026 |
SNAPSHOT - You have an Azure Synapse Analytics workspace that contains three pipelines and three triggers named Trigger1, Trigger2, and Trigger3. Trigger3 has the following definition. For each of the following statements, selec...Author: VenomousSerpent42 · Last updated Jul 5, 2026 |
DRAG DROP - You have an Azure Databricks deployment and a local file named /tmp/file1 that contains the following code. You need to read /tmp/file1 into a data frame by using Scala. How should you complete the code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once...Author: Isabella · Last updated Jul 5, 2026 |
You have an Azure subscription that contains a Microsoft Purview account. You need to search the Microsoft Purview Data Catalog to identify assets that have a...To search the Microsoft Purview Data Catalog for assets that have an assetType property of Table or View, it’s important to understand how queries are constructed in Purview's search engine. Let’s analyze each option: Option A: assetType IN ('Table', 'View') - Description: The IN operator is typically used in query languages to filter results where a field matches any one of the listed values. This is a common and effective way to check if a field (like assetType) is either "Table" or "View". - Why it fits: The IN operator is the correct syntax to match multiple values in a single query. Using this format ensures you are querying assets where the assetType is either "Table" or "View", which aligns with the requirement. - Conclusion: This option is syntactically correct and matches the task perfectly. Option B: assetType:Table OR assetType:view - Description: This option uses the OR operator to combine two conditions: assetType:Table and assetType:view. - Why it’s rejected: In Purview queries, the use of OR between conditions is valid, but the proper syntax requires matching the exact field and value format. assetType:Table is valid, but assetType:view is not because the correct capitalization for assetType values in Purview is "View" (case-sensitive). - Conclusion: This option is incorrect due to ... Author: StarlightBear · Last updated Jul 5, 2026 |
You have an Azure subscription that contains an Azure Synapse Analytics account. The account is integrated with an Azure Repos repository named Repo1 and contains a pipeline named Pipeline1. Repo1 contains the branches shown in the following table. From featu...To publish changes to a pipeline in Azure Synapse Analytics that is integrated with an Azure Repos repository, the workflow must follow proper version control practices within the context of the repository and the environment. Let's break down each option: Option A: From featuredev, create a pull request. - Description: A pull request (PR) is used to merge changes from one branch to another. If you're working in the featuredev branch and you need to publish changes, the typical approach would be to create a pull request to merge your featuredev branch changes into the main branch or another designated branch, assuming the changes have been tested and are ready for integration. - Why it's rejected: While creating a pull request from featuredev to main is a common practice when you're ready to merge changes, you cannot directly publish changes from featuredev without switching to the appropriate environment first (live mode, for example). This step of creating a pull request itself is part of the workflow, but it's not sufficient to publish the changes to the live environment. - Conclusion: This option is partially correct but doesn't address the first step needed to publish. Option B: From main, create a pull request. - Description: In this option, you'd create a pull request from the main branch to another branch (e.g., featuredev or another feature branch). - Why it's rejected: Creating a pull request from main to another branch is not a correct approach to publish changes because the main branch typically contains the stable production code. If you're working in a featuredev branch, you would usually want to merge featuredev into main. So, starting from main in this context isn't the right approach. - Conclusion: This option doesn't align with the goal of publishing changes from a development branch to the main or production branch. Option C: Ad... Author: RadiantPhoenixX · Last updated Jul 5, 2026 |
DRAG DROP - You have an Azure subscription that contains an Azure Data Factory account named ADF1 and an Azure Data Lake Storage Gen2 account named storage1. ADF1 contains the objects shown in the following table. You need to configure DailyIngestion to perform the following actions: * Ingest 2,000 small files into storage1 once every 24 hours. * Output one large file once every 24 hours. ...Author: Kunal · Last updated Jul 5, 2026 |
SNAPSHOT - You have an Azure Synapse Analytics pipeline named pipeline1 that has concurrency set to 1. To run pipeline1, you create a new trigger as shown in the following exhibit. Use the drop-down menus to select the answer choice that completes eac...Author: Oliver · Last updated Jul 5, 2026 |
SNAPSHOT - You have an Azure Data Factory pipeline that has the logic flow shown in the following exhibit. For each of the following statements, select Yes if the statement is...Author: Ava · Last updated Jul 5, 2026 |
DRAG DROP - You have an Azure subscription that contains an Azure Synapse Analytics workspace named Workspace1. Workspace1 contains an Apache Spark pool named spark1 and a pipeline named Pipeline1. You need to add an activity to Pipeline1 that will run a notebook. The solution must ensure that the activity overrides the value of a variable named inputFile when the notebook runs. Which five acti...Author: RadiantJaguar56 · Last updated Jul 5, 2026 |
You have an Azure subscription that contains an Azure SQL database named SQLDB1 and an Azure Synapse Analytics dedicated SQL pool named Pool1. You need to replicate data from SQLDB1 to Pool1. The solution must meet the following requirements: * Minimize performance i...Let's review the options and analyze which one best meets the requirements for replicating data from an Azure SQL Database (SQLDB1) to an Azure Synapse Analytics Dedicated SQL Pool (Pool1) while minimizing performance impact, supporting near-real-time (NRT) analytics, and minimizing administrative effort. Option A: Azure Synapse Link - Description: Azure Synapse Link is specifically designed to integrate Azure SQL databases with Azure Synapse Analytics for near-real-time analytics. It allows you to seamlessly replicate data from Azure SQL Database to a dedicated SQL pool in Synapse Analytics, without requiring complex data movement or transformations. Data is replicated with minimal performance impact and typically in near-real-time. - Why it fits: - Minimizes performance impact: Azure Synapse Link is designed to be efficient and optimized, minimizing the performance impact on the source database (SQLDB1). - Supports near-real-time (NRT) analytics: This is one of the primary benefits of using Synapse Link—data is replicated almost instantly into the Synapse dedicated SQL pool. - Minimizes administrative effort: Once set up, Azure Synapse Link is relatively hands-off in terms of administration, requiring little manual intervention after the initial configuration. - Conclusion: This option is the best fit for the requirements since it is designed to replicate data from SQLDB1 to Pool1 with minimal impact and in near-real-time. Option B: Azure Private Link - Description: Azure Private Link provides private connectivity between Azure services over the Azure backbone network. It allows you to access Azure services like Azure SQL Database or Synapse Analytics securely and privately. - Why it’s rejected: While Azure Private Link is beneficial for secure communication between services, it is not a data replication solution. It does not provide the mechanism to move or replicate data between SQLDB1 and Pool1. - Conclusion: This option does not meet the requirement for replicating data and is irrelevant to the scenario. Option C: SQL Data Sync for Azure - Description: SQL Data ... Author: Maya · Last updated Jul 5, 2026 |
DRAG DROP - You have an Azure Active Directory (Azure AD) tenant that contains a security group named Group1. You have an Azure Synapse Analytics dedicated SQL pool named dw1 that contains a schema named schema1. You need to grant Group1 read-only permissions to all the tables and views in schema1. The solution must use the principle of least privilege. Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of acti...Author: Olivia Johnson · Last updated Jul 5, 2026 |
SNAPSHOT - You have an Azure subscription that contains a logical Microsoft SQL server named Server1. Server1 hosts an Azure Synapse Analytics SQL dedicated pool named Pool1. You need to recommend a Transparent Data Encryption (TDE) solution for Server1. The solution must meet the following requirements: * Track the usage of encryption keys. Maintain the access of client apps to Pool1 in the event of an Azure datacenter outage that affects the availabili...Author: Sofia · Last updated Jul 5, 2026 |
You plan to create an Azure Synapse Analytics dedicated SQL pool. You need to minimize the time it takes to identify queries that return confidential information as defined by the company's data privacy regulations and the users who executed the queues. Which two components should you i...To address the scenario where you need to minimize the time it takes to identify queries that return confidential information in an Azure Synapse Analytics dedicated SQL pool, and the users who executed the queries, let's analyze the given options: Option A: Sensitivity-classification labels applied to columns that contain confidential information - Description: Sensitivity labels are used to classify sensitive data in a database, including columns that contain confidential or sensitive information. These labels help identify which data is sensitive, making it easier to track and enforce compliance with data privacy regulations. - Why it fits: - Identifying confidential data: Applying sensitivity-classification labels to columns ensures that you can quickly identify which parts of the database contain sensitive information, such as personally identifiable information (PII) or financial data. This is critical for minimizing the time to identify confidential information. - Compliance: Labels help ensure compliance with regulations by providing an auditable trail of where confidential data is stored. - Conclusion: This option is highly relevant and effective for identifying confidential information. Option B: Resource tags for databases that contain confidential information - Description: Resource tags in Azure are metadata labels that help organize and manage resources. They can be applied to Azure resources like databases to track their purpose or classification (e.g., sensitive data or compliance). - Why it’s rejected: - Not directly related to query execution or data identification: While resource tags can help with organizing and managing resources, they do not provide a mechanism for identifying or tracking confidential data at the column level or for tracking which queries return sensitive data. - Limited use in this context: Resource tags alone cannot help identify confidential data in the context of query execution or logging user activity. - Conclusion: This option is not as effective as other options for the specific requirement of identifying confidential data returned by queries. Option C: Audit logs sent to a Log Analytics workspace - Description: ... Author: FrozenWolf2022 · Last updated Jul 5, 2026 |
You are designing an enterprise data warehouse in Azure Synapse Analytics that will contain a table named Customers. Customers will contain credit card information. You need to recommend a solution to provide salespeople with the ability to view all the entries in Customers. The solution must pr...To meet the requirements of allowing salespeople to view the entries in the Customers table while preventing them from viewing or inferring credit card information, let's evaluate the options provided: Option A: Data Masking - Description: Dynamic Data Masking (DDM) is a feature in Azure Synapse Analytics that automatically masks sensitive data in query results. It can be used to prevent unauthorized users from viewing sensitive data, such as credit card numbers, while still allowing authorized users to see the full data. - Why it fits: - Prevents unauthorized viewing: DDM allows you to apply a mask to sensitive data like credit card information, showing masked values (e.g., `XXXX-XXXX-XXXX-1234`) to salespeople and only revealing the full data to authorized users. - Simple to implement: This is an efficient solution to prevent salespeople from viewing or inferring credit card information. - Conclusion: Data Masking is a suitable choice for this scenario, as it prevents salespeople from seeing credit card information while still allowing them to view the rest of the customer data. Option B: Always Encrypted - Description: Always Encrypted is a feature in SQL Server and Azure SQL Database that encrypts sensitive data in the database and ensures that the data is never visible in an unencrypted form, even to database administrators. The encryption keys are stored outside of the database. - Why it’s rejected: - Complexity: While Always Encrypted is a strong solution for data encryption, it is more suited for protecting data at rest or during transmission. It would be overkill for this specific scenario where the primary goal is to prevent unauthorized access to a specific column (credit card data) while still allowing access to other columns. - Access control: This solution does not allow for the same flexibility as Dynamic Data Masking, where data can be partially visible based on user roles. - Conclusion: Always Encrypted is more complex and might not be the most appropriate solution for this use case, especially when the goal is to provide a simpler means of preventing unauthorized access to credit card information. Option C: ... Author: StarryEagle42 · Last updated Jul 5, 2026 |
You develop data engineering solutions for a company. A project requires the deployment of data to Azure Data Lake Storage. You need to implement role-based access control (RBAC) so that project members can manage the Azure Data Lake Storage resources. Which three actions s...To implement Role-Based Access Control (RBAC) for Azure Data Lake Storage (ADLS) so that project members can manage resources, let's analyze each option and determine which actions should be taken: Option A: Create security groups in Azure Active Directory (Azure AD) and add project members - Description: Creating security groups in Azure Active Directory (Azure AD) allows you to group users based on roles or teams. Once these groups are created, project members can be added to these groups. - Why it fits: - RBAC relies on associating users or groups with specific roles. By creating Azure AD security groups and adding project members, you can manage access at the group level. This simplifies access management and ensures that the correct permissions are granted to all members of the project. - This is a foundational step in implementing RBAC. - Conclusion: This option is a correct and necessary action in implementing RBAC for Azure Data Lake Storage. Option B: Configure end-user authentication for the Azure Data Lake Storage account - Description: End-user authentication typically refers to methods like Azure AD authentication or managed identities to authenticate users when they access data in Azure Data Lake Storage. - Why it’s rejected: - While authentication is necessary for users to access the Data Lake, the focus of the question is on implementing RBAC, which deals with authorization (granting permissions). Authentication ensures that users are who they say they are, but RBAC controls what those authenticated users can do. This is an important step, but it's not directly related to RBAC configuration. - Conclusion: Authentication is a prerequisite for RBAC, but it is not the central task for setting up RBAC in this context. It’s not one of the primary actions required for RBAC implementation. Option C: Assign Azure AD security groups to Azure Data Lake Storage - Description: Assigning security groups to Azure Data Lake Storage is a core part of implementing RBAC. This involves granting specific roles to the security groups, which will define what actions members of those groups can perform on the Data Lake resources. - Why it fits: - RBAC is about assigning roles to users or groups. By assigning Azure AD security groups to Azure Data Lake Storage, you are controlling who can access the storage and what they can do with it (e.g., read, write, manage). - This directly relates to the... Author: Isabella · Last updated Jul 5, 2026 |
You have an Azure Data Factory version 2 (V2) resource named Df1. Df1 contains a linked service. You have an Azure Key vault named vault1 that contains an encryption key nam...To address the need to encrypt Azure Data Factory (ADF) version 2 (V2) resource Df1 using an encryption key from Azure Key Vault (key1), we need to determine the best approach based on Azure's encryption and access control mechanisms. Let's evaluate each option: Option A: Add a private endpoint connection to vault1 - Description: A private endpoint connection allows a secure connection between an Azure resource (in this case, Azure Data Factory) and an Azure Key Vault over a private network. It restricts access to the Key Vault to only resources that are connected to the private endpoint. - Why it’s rejected: - While a private endpoint connection provides secure access to a Key Vault, it’s not the first necessary step to enable encryption for Azure Data Factory using the key from the Key Vault. - The need to encrypt Azure Data Factory with a key requires proper Key Vault access configuration, but adding a private endpoint is more about network configuration and doesn't directly address the first step in securing ADF with the Key Vault encryption key. - Conclusion: This is not the first action to take for encryption, but it could be useful in securing access if needed later. Option B: Enable Azure role-based access control (RBAC) on vault1 - Description: Azure RBAC controls access to Azure resources by assigning roles to users, groups, or managed identities. In this case, enabling RBAC on vault1 would ensure that Azure Data Factory can access key1 within the Key Vault. - Why it fits: - RBAC is essential for granting Azure Data Factory permissions to access the Key Vault and use its keys for encryption. This is the first step in ensuring that Azure Data Factory can interact with the Key Vault to use the encryption key. Without proper RBAC configurations, Azure Data Factory wouldn't be able to access the Key Vault and use the encryption key. - RBAC roles such as "Key Vault Reader" and "Key Vault Crypto Operator" would grant the required permissions. - Conclusion: This opt... Author: Benjamin · Last updated Jul 5, 2026 |
You are designing an Azure Synapse Analytics dedicated SQL pool. You need to ensure that you can audit access to Personally Identifiable ...When designing an Azure Synapse Analytics dedicated SQL pool to audit access to Personally Identifiable Information (PII), the goal is to ensure that access to PII data is properly controlled and monitored. Let’s evaluate each of the options: A) Column-Level Security - Description: Column-level security (CLS) in Azure Synapse allows restricting access to specific columns in a database table. It is useful when you need to prevent users from accessing certain sensitive data columns (such as PII columns) unless they have the required permissions. - Pros: Effective for preventing unauthorized access to sensitive columns. - Cons: While it can restrict access, it does not provide auditing capabilities. CLS is more about controlling access to data but not about tracking or auditing access. - Use case: CLS can be used when you need to enforce access control to specific columns containing PII. However, it is not sufficient on its own for auditing access. - Conclusion: Not ideal for auditing access to PII as it doesn’t directly support auditing. B) Dynamic Data Masking (DDM) - Description: Dynamic Data Masking (DDM) hides sensitive data in the result sets of queries, applying a mask to PII data. It’s used to obfuscate data for non-privileged users while allowing authorized users to view unmasked data. - Pros: Useful for protecting data privacy at the query level by masking sensitive columns. - Cons: DDM is not an auditing solution; it is focused on data visibility and privacy. It doesn’t provide detailed logs or records of who accessed sensitive data. - Use case: Ideal for ensuring that PII data is masked for unauthorized users, but it doesn’t support audit tracking of access to PII. - Conclusion: While useful for protecting data privacy, it is not an appropriate solution for auditing access to PII. C) Row-Level Security (RLS) - Description: Row-Level Security (RLS) allows you to control access to rows in a table based on the characteristics of the user executing a query. For example, RLS can be used to limit access to specific rows of data, which is... Author: Elijah · Last updated Jul 5, 2026 |
SNAPSHOT - You are designing an Azure Synapse Analytics dedicated SQL pool. Groups will have access to sensitive data in the pool as shown in the following table. You have policies for the sensitive data. The policies vary be region as shown in the following table. You have a table of patients for each region. The tables contain the following potentially sensitive columns. You are designing dynamic data masking...Author: Max · Last updated Jul 5, 2026 |
DRAG DROP - You have an Azure Synapse Analytics SQL pool named Pool1 on a logical Microsoft SQL server named Server1. You need to implement Transparent Data Encryption (TDE) on Pool1 by using a custom key named key1. Which five actions should you perform in sequence? To answer, move t...Author: Ishaan · Last updated Jul 5, 2026 |
You have a data warehouse in Azure Synapse Analytics. You need to ensure that the data in the data warehouse...When ensuring that data in a data warehouse in Azure Synapse Analytics is encrypted at rest, the most appropriate option should provide built-in encryption to protect data stored within the system. Let’s evaluate each option: A) Advanced Data Security for this database - Description: Advanced Data Security provides features like vulnerability assessments, data discovery, and advanced threat protection. While it enhances security by identifying potential threats and securing your environment, it does not directly handle encryption of data at rest. - Pros: Useful for overall security management and proactive threat detection. - Cons: Does not directly address the need for encryption at rest. It’s more about security monitoring and threat protection, not data encryption. - Use case: Advanced Data Security is ideal for managing and improving the overall security posture of your database but not for encryption at rest. - Conclusion: Not suitable for the specific requirement of encrypting data at rest. B) Transparent Data Encryption (TDE) - Description: Transparent Data Encryption (TDE) automatically encrypts SQL data files at rest, providing encryption for data stored on disk. This encryption is transparent to the application and users, meaning no changes are needed to access the data. - Pros: TDE is the recommended solution for encrypting data at rest in Azure Synapse Analytics. It ensures that all data stored in the data warehouse is encrypted, fulfilling the requirement for encryption at rest. - Cons: It does not encrypt data in transit or manage access control. - Use case: TDE is the appropriate solution for data encryption at rest in Azure Synapse Analytics. It provides seamless encryption for data stored within the system, making it the best fit for the given scenario. - Conclusion: Ideal for ensuring data is encrypted at rest in Azur... Author: SilverBear · Last updated Jul 5, 2026 |
You are designing a streaming data solution that will ingest variable volumes of data. You need to ensure that you can change the partition count a...When designing a streaming data solution that will ingest variable volumes of data and require the ability to change the partition count after creation, it's important to consider the scalability, flexibility, and partitioning mechanisms of each service. Here's a breakdown of each option: A) Azure Event Hubs Dedicated - Key Features: Azure Event Hubs allows you to ingest real-time data at massive scale, including the ability to adjust partition counts after creation. - Partition Count Flexibility: Event Hubs Dedicated provides more control over partitioning and allows you to change the partition count dynamically as your data volume grows or shrinks. However, this flexibility is a feature of Event Hubs Dedicated (as opposed to Event Hubs Standard), which is an essential factor in your requirement. - Use Case: Best for scenarios with high ingestion rates and variable data volumes where partition count scalability is a necessity. - Rejection Reason for Other Options: - Azure Stream Analytics doesn't allow dynamic partition scaling like Event Hubs. - Azure Data Factory and Azure Synapse Analytics are better suited for batch data processing or ETL pipelines, not real-time streaming ingestion. B) Azure Stream Analytics - Key Features: Azure Stream Analytics is a real-time analytics service designed to process and analyze data streams. - Partition Count Flexibility: Stream Analytics does not directly manage partitions. It works with data sources like Event Hubs or IoT Hub for data ingestion, bu... Author: Liam · Last updated Jul 5, 2026 |
You are designing a date dimension table in an Azure Synapse Analytics dedicated SQL pool. The date dimension table will be used by all the fact tables. Which distribu...When designing a date dimension table in an Azure Synapse Analytics dedicated SQL pool, the goal is to minimize data movement during queries, especially since the date dimension will be referenced by multiple fact tables. To ensure efficient query performance, understanding the different distribution types and their behavior is critical. Here's a breakdown of each option: A) HASH Distribution - Key Features: With hash distribution, data is distributed across multiple distributions based on a hash function applied to a specified column (often a foreign key). This ensures that rows with the same hash key end up on the same distribution. - Use Case: Best used when you want to distribute data based on the values of a column that is commonly used in join or query conditions. However, date dimension tables are typically not large enough to require hash distribution. Moreover, fact tables often join on the foreign key, but a date dimension table will be used by all fact tables, and the distribution strategy needs to minimize cross-distribution joins, which hash distribution doesn’t optimize for in this scenario. - Rejection Reason: Hash distribution is best suited for large tables with a specific join key, like fact tables with a foreign key. Date dimension tables tend to be smaller and are commonly referenced across multiple fact tables, making hash distribution not the ideal choice in this case. B) REPLICATE Distribution - Key Features: Replicate distribution duplicates the entire table on every distribution node. This means that the entire date dimension table is available locally on each distribution, making it extremely efficient for queries involving the date dimension. - Use Case: Best suited for smaller tables, like dimension tables, that are frequently joined with fact tables. In this case, since the date dimension is often small (a few rows per y... Author: Arjun · Last updated Jul 5, 2026 |
SNAPSHOT - You develop a dataset named DBTBL1 by using Azure Databricks. DBTBL1 contains the following columns: * SensorTypeID * GeographyRegionID * Year * Month * Day * Hour * Minute * Temperature * WindSpeed * Other You need to store the data to support daily incremental load pipelines that vary for each GeographyRegionID. The solution must minimize stor...Author: Charlotte · Last updated Jul 5, 2026 |
You are designing a security model for an Azure Synapse Analytics dedicated SQL pool that will support multiple companies. You need to ensure that users from each company can view only the data of their respective company. Which two objects should you include in ...When designing a security model in Azure Synapse Analytics dedicated SQL pool to ensure that users from each company can view only their respective company data, it’s important to use appropriate mechanisms to control data access. Below is an analysis of each option: A) Security Policy - Key Features: A security policy in Azure Synapse Analytics can help enforce data access rules, but it does not directly solve the issue of restricting access to data based on the company of the user. Security policies are typically used for row-level security, which could be used for restricting access to rows in a table based on conditions (e.g., company identifier). - Use Case: While a security policy could be useful for row-level security, it needs to be implemented in conjunction with a predicate function that applies specific conditions (e.g., a company ID) for restricting access. - Rejection Reason: While important in securing data, it alone isn’t enough to limit data access by company. It needs additional components like a predicate function to implement the logic. B) Custom Role-Based Access Control (RBAC) Role - Key Features: Custom RBAC roles in Azure can be used to grant or restrict access to specific resources. While RBAC is typically used to control access to Azure resources (e.g., databases, storage), it does not inherently support row-level security within the database to filter data by a specific column (such as company). - Use Case: Custom RBAC roles are useful for granting permissions to resources at the Azure level but not for data-level security within a SQL pool. - Rejection Reason: RBAC is more for resource-level access control rather than data-level access control within the database. It can't be directly used to filter data based on the company. C) Predicate Function - Key Features: A predicate function can be used in conjunction with row-level security (RLS) policies to restrict access to specific rows of data. The predicate function can enforce rules, such as filtering out rows that belong to other companies, based o... Author: Daniel · Last updated Jul 5, 2026 |
You have a SQL pool in Azure Synapse that contains a table named dbo.Customers. The table contains a column name Email. You need to prevent nonadministrative users from seeing the full email addresses in the Email...In this scenario, the goal is to prevent nonadministrative users from seeing the full email addresses in the Email column while allowing them to view the values in a masked format (e.g., [email protected]). Let’s review the options and their suitability: A) From Microsoft SQL Server Management Studio, set an email mask on the Email column. - Key Features: SQL Server Management Studio (SSMS) allows you to define Dynamic Data Masking (DDM) on a column. This feature can be used to mask sensitive data, and the mask will automatically apply to nonadministrative users querying the column. For example, you can mask the email so that users see a pattern like [email protected] instead of the full email. - Use Case: This is the correct solution because Dynamic Data Masking works at the database level to restrict sensitive data visibility without affecting the underlying data. The masked value is only visible to users who do not have specific permissions (like administrative roles). - Conclusion: This option is ideal because it ensures that users only see a masked version of the email address, fulfilling the requirement. B) From the Azure portal, set a mask on the Email column. - Key Features: While you can set Dynamic Data Masking through the Azure portal, it is typically done using T-SQL commands or SSMS. The Azure portal UI is not directly used to configure masks for individual columns like this. - Rejection Reason: The Azure portal doesn't provide a straightforward way to apply a dynamic mask directly to the column itself in this case. It's more of a management interface for the overall Azure Synapse environment, and applying a mask is typically done via SQL commands or SSMS. ... Author: Lucas · Last updated Jul 5, 2026 |
You have an Azure Data Lake Storage Gen2 account named adls2 that is protected by a virtual network. You are designing a SQL pool in Azure Synapse that will ...When designing a SQL pool in Azure Synapse that will use an Azure Data Lake Storage Gen2 (ADLS2) account as a source, the best authentication method is D) a managed identity. Let’s go through each of the options and explain why D) managed identity is the best choice, and why the other options are rejected. A) Azure Active Directory (Azure AD) user - Explanation: Azure AD users can authenticate and access Azure resources using their credentials. However, an Azure AD user is typically used for accessing resources via Azure AD-based applications or for administrative tasks. - Why rejected: In the context of SQL pool integration with ADLS2, authentication using an Azure AD user requires explicitly managing access and assigning roles. This approach is more cumbersome and less seamless for service-to-service authentication. It is better suited for individual or administrative access, not for a SQL pool using a managed service like Synapse. B) Shared key - Explanation: A shared key provides direct access to Azure Storage accounts by using a storage account key. This is generally used for programmatic access or when direct access to the storage is needed by external systems. - Why rejected: Using a shared key is not the recommended approach because it involves handling keys manually, which presents security risks and management challenges (e.g., rotating keys). Moreover, it doesn’t integrate well with managed services and doesn't provide the granularity of control that a managed identity offers in Azure. C) Shared Access Signature (SAS) - Explanation: A SAS token is a URL-encoded string that provides restricted access to Azure resources. It can define permissions (e.g., read, write) and an expiration date for a specific resource in the sto... Author: Andrew · Last updated Jul 5, 2026 |
SNAPSHOT - You have an Azure Synapse Analytics SQL pool named Pool1. In Azure Active Directory (Azure AD), you have a security group named Group1. You need to control the access of Group1 to specific columns and rows in a table in Pool1. Which Transact-SQL commands should you use?...Author: IceDragon2023 · Last updated Jul 5, 2026 |