DE December 2023
You design an Azure Data Factory pipeline that has a data flow activity named Move to Synapse and an append variable activity named Upon failure. Upon failure runs upon the failure of Move to Synapse. You notice that if the Move to Synapse activity fails, the pipeline status is successful. You need to ensure that if Move to Synapse fails, the pipeline status is failed. The solution must ensure that Upon Failure executes when Move to Synapse fails. What should you do?
Adding a new activity with a Success predecessor to Move to Synapse will ensure that the pipeline is marked as failed when the data flow fails. Changing the precedence for Upon Failure to Completion will still succeed whether Move to Synapse fails. Changing the precedence for Upon Failure to Success will not trigger Upon Failure when the data flow fails. Adding a new activity with a Failure predecessor to Upon Failure will not change the result.
CHECK THIS QUESTION: You have a Delta Lake solution that contains a table named table1. You need to roll back the contents of table1 to 24 hours ago. Which command should you run?
CHECK THIS QUESTION: RESTORE TABLE employee TO TIMESTAMP AS OF current_timestamp() - INTERVAL '24' HOUR; VACUUM employee RETAIN 24; COPY INTO employee1 restores the table to 24 hours ago. moves unused files from the delta folder. copies data to a new table.
You are implementing an application that queries a table named Purchase in an Azure Synapse Analytics Dedicated SQL pool. The application must show data only for the currently signed-in user. You use row-level security (RLS), implement a security policy, and implement a function that uses a filter predicate. Users in the marketing department report that they cannot see their data. What should you do to ensure that the marketing department users can see their data?
Grant the SELECT permission on the Purchase table to the Marketing users.
You have an Azure Stream Analytics solution that receives data from multiple thermostats in a building. You need to write a query that returns the average temperature per device every five minutes for readings within that same five minute period. Which two windowing functions could you use?
In Azure Stream Analytics, when you need to write a query that returns the average temperature per device every five minutes for readings within that same five-minute period, you can use two windowing functions: Tumbling Windows and Hopping Windows. Here's an explanation of both: Tumbling Windows: Tumbling windows have a defined fixed time period (e.g., 5 minutes) and can aggregate all events that fall within that same time period. In your scenario, you can use a Tumbling Window with a duration of 5 minutes to group all the temperature readings that occur within each 5-minute interval. The Tumbling Window will reset every 5 minutes, ensuring that events within each 5-minute period are aggregated together. Hopping Windows: Hopping windows, like Tumbling windows, also have a defined time period (e.g., 5 minutes). However, they can aggregate events for a potentially different time period within the window. For example, you can use a Hopping Window with a duration of 5 minutes and a hop of 5 minutes. This means that it will create a new window every 5 minutes and aggregate events that occurred within the last 5 minutes. Hopping Windows can be useful if you want to create overlapping windows or if you have varying event arrival times and still need to group them within specific time intervals.
REDO Q2
REDO Q2
You have an Azure Synapse Analytics workspace named workspace1. You plan to write new data and update existing rows in workspace1. You create an Azure Synapse Analytics sink to write the processed data to workspace1. You need to configure the writeBehavior parameter for the sink. The solution must minimize the number of pipelines required. What should you use?
In Azure Synapse Analytics, to write new data and update existing rows in a sink while minimizing the number of pipelines required, you should configure the writeBehavior parameter of the sink to use the "Upsert" syntax. Here's why this is the correct choice: Upsert (Insert or Update) Behavior: The term "Upsert" combines "Insert" and "Update." When you configure the sink with Upsert behavior, it allows you to perform both insertions of new data and updates of existing rows using a single pipeline. This is achieved by checking if a row with the same key already exists in the target table. If it exists, the row is updated; otherwise, a new row is inserted. Minimizing Pipelines: By using Upsert, you can minimize the number of pipelines required for your data integration process. You don't need separate pipelines for inserts and updates; a single pipeline can handle both scenarios. Efficient Data Synchronization: This approach is particularly useful when you want to synchronize data between source and destination, ensuring that the target table reflects the latest changes from the source. Simplified ETL Process: Using Upsert behavior simplifies your ETL (Extract, Transform, Load) process by reducing complexity and eliminating the need to manage multiple pipelines for different data operations.
You have an Azure subscription that contains an Azure Synapse Analytics Dedicated SQL pool named Pool1. Pool1 hosts a table named Table1. You receive JSON data from an external data source. You need to store the external data in Table1. Which T-SQL element should you use?
In summary, when you need to store JSON data from an external source in an Azure Synapse Analytics Dedicated SQL pool table, OPENJSON is the appropriate T-SQL command to convert and map the JSON data into a tabular format that can be stored in your database.
You have an Azure Stream Analytics job named Job1. Job1 runs continuously and executes non-parallelized queries. You need to minimize the impact of Azure node updates on Job1. The solution must minimize costs. To what should you increase the Scale Units (SUs)?
Increasing the SUs to 12 still uses two nodes. In Azure Stream Analytics, when you want to minimize the impact of Azure node updates on a job and ensure its continuous operation, you can increase the Scale Units (SUs). Scale Units determine the number of underlying compute nodes allocated to your job. By increasing SUs, you can achieve the following benefits: Increased Redundancy: When you increase SUs, Azure Stream Analytics provisions multiple nodes to process your job's queries. This redundancy allows your job to continue running even when one of the nodes undergoes maintenance or updates. High Availability: With multiple nodes, if one node requires maintenance or experiences an update, the other nodes can continue processing data, ensuring high availability and minimal disruption to your job. Load Distribution: The workload can be distributed across multiple nodes, which can lead to better performance and reduced processing times, especially for large or complex queries. The provided answer, "Increasing the SUs to 12 still uses two nodes," implies that by increasing the SUs to 12, you are effectively provisioning two nodes. This configuration ensures redundancy and minimizes the impact of node updates. On the other hand, if you were to keep a lower number of SUs (e.g., 1 SU), your job would run on a single node. In the event of node updates or maintenance, the job could experience downtime or interruptions. Therefore, increasing the Scale Units to 12 (or a higher value that suits your needs) is the correct approach to minimize the impact of Azure node updates on Job1, ensuring continuous operation and high availability while still optimizing costs by avoiding a single-node setup that might be more susceptible to interruptions.
You are writing a data import task in Azure Data Factory. You need to increase the number of rows per call to the REST sink What should you change?
To increase the number of records per batch, we need to increase the writeBatchSize. The default value for this parameter is 10,000, so to increase this we need to use a value that is higher than the default. In Azure Data Factory, when you want to increase the number of rows per call to a REST sink, you need to change the writeBatchSize parameter in the sink configuration. The writeBatchSize parameter specifies the number of records or rows that are sent to the REST endpoint in each batch or call. By increasing this value, you can improve the efficiency of your data import task by reducing the number of HTTP requests made to the REST API. Here's how it works: Default Value: The default value for writeBatchSize in many sink configurations is often set to a conservative number like 10,000 to ensure data reliability and avoid overloading the REST API endpoint with a large number of records in a single request. Increasing Efficiency: By increasing the writeBatchSize to a value higher than the default, you allow Azure Data Factory to bundle more records together in each HTTP request. This can be beneficial when dealing with large volumes of data because it reduces the overhead associated with making many individual HTTP requests. Considerations: While increasing writeBatchSize can improve performance, you should be mindful of the REST API's capacity and any limitations or rate limits imposed by the API provider. Setting it too high could potentially overwhelm the REST API or result in rejected requests. Therefore, it's important to test and tune the writeBatchSize parameter based on the capabilities and requirements of the specific REST API you are working with.
You have a solution that upserts data to a table in an Azure Synapse Analytics database. You need to write a single T-SQL statement to upsert the data. Which T-SQL command should you run?
To upsert data (i.e., insert new values or update existing values) into a table in an Azure Synapse Analytics database, you should use the MERGE statement. Here's why MERGE is the correct choice and how it works: MERGE Statement: The MERGE statement in T-SQL is designed for performing upsert operations. It allows you to combine multiple data modification operations (INSERT, UPDATE, DELETE) into a single statement based on a specified condition. INSERT only inserts new values. It does not update existing values. UPDATE only updates values, it does not insert new values. SELECT INTO only allows you to INSERT values.
You have an ELT solution that uses an Azure Storage account named datastg, an Azure HDInsight cluster, and an Azure Data Factory resource. You need to run the script as an activity in the Data Factory pipeline. The solution must write the output of the script to a folder named devices in a container named data in the storage account. What should you add to the Output value in the JSON file?
wasb://[email protected]/devices/ wasb://: This is a protocol identifier that stands for "Windows Azure Storage Blob." It indicates that you are accessing resources in Azure Blob Storage. [email protected]: This part represents the connection information to the Azure Blob Storage account. It includes the container name, "datastg," and the Azure Blob Storage endpoint, "datastg.blob.core.windows.net." /devices/: This is the path within the container where you want to write the output. It specifies the "devices" folder as the destination for your script's output. However, it's worth noting that in Azure Data Factory, when configuring activities, you typically work with JSON-based configuration rather than specifying the path as a complete URL. The JSON-based configuration provides a structured way to define the output settings, as shown in my previous response. The JSON-based configuration makes it easier to integrate with Data Factory's pipeline activities, linked services, and other components of the data orchestration process. The URL format you provided can be translated into the structured JSON configuration for use in an Azure Data Factory pipeline.
