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| Section | Objectives |
|---|---|
| Topic 1: Monitor and optimize operational resources | - Monitor database performance
|
| Topic 2: Implement a secure environment | - Implement data protection
|
| Topic 3: Plan and implement a high availability and disaster recovery environment | - Backup and restore
|
| Topic 4: Optimize query performance | - Analyze query performance
|
| Topic 5: Perform automation | - Automate database tasks
|
| Topic 6: Plan and implement data platform resources | - Plan and configure security for the data platform
|
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NEW QUESTION # 158
You have an Azure SQL database named DB1 that contains two tables named Table1 and Table2. Both tables contain a column named a Column1. Column1 is used for joins by an application named App1.
You need to protect the contents of Column1 at rest, in transit, and in use.
How should you protect the contents of Column1? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/sql/relational-databases/security/encryption/always-encrypted-database-engine
NEW QUESTION # 159
You have 20 Azure SQL databases provisioned by using the vCore purchasing model.
You plan to create an Azure SQL Database elastic pool and add the 20 databases.
Which three metrics should you use to size the elastic pool to meet the demands of your workload? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,C,D
Explanation:
Explanation
CE: Estimate the vCores needed for the pool as follows:
For vCore-based purchasing model: MAX(<Total number of DBs X average vCore utilization per DB>,
<Number of concurrently peaking DBs X Peak vCore utilization per DB>
A: Estimate the storage space needed for the pool by adding the number of bytes needed for all the databases in the pool.
Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/database/elastic-pool-overview
NEW QUESTION # 160
You have an Azure Data Lake Storage Gen2 container.
Data is ingested into the container, and then transformed by a data integration application. The data is NOT modified after that. Users can read files in the container but cannot modify the files.
You need to design a data archiving solution that meets the following requirements:
New data is accessed frequently and must be available as quickly as possible.
Data that is older than five years is accessed infrequently but must be available within one second when requested.
Data that us older than seven years is NOT accessed. After seven years, the data must be persisted at the lowest cost possible.
Costs must be minimized while maintaining the required availability.
How should you manage the data? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blob-storage-tiers
NEW QUESTION # 161
You need to design an analytical storage solution for the transactional dat a. The solution must meet the sales transaction dataset requirements.
What should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 162
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have an Azure Data Lake Storage account that contains a staging zone.
You need to design a daily process to ingest incremental data from the staging zone, transform the data by executing an R script, and then insert the transformed data into a data warehouse in Azure Synapse Analytics.
Solution: You use an Azure Data Factory schedule trigger to execute a pipeline that executes mapping data flow, and then inserts the data into the data warehouse.
Does this meet the goal?
Answer: A
Explanation:
If you need to transform data in a way that is not supported by Data Factory, you can create a custom activity, not a mapping flow,5 with your own data processing logic and use the activity in the pipeline. You can create a custom activity to run R scripts on your HDInsight cluster with R installed.
Reference:
https://docs.microsoft.com/en-US/azure/data-factory/transform-data
Basic Concept: This question tests task automation for database administration, including which Azure or SQL mechanism can run repeatable maintenance and configuration work.
Why B is Correct: No is related to operational monitoring or tuning, but it must match the exact signal needed: query history, resource utilization, wait/blocking detail, or automatic remediation. The scenario asks for: Each question in the series contains a unique solution that might meet the stated goals. That makes No the option that satisfies the required Azure SQL layer and operational outcome.
Why A is Wrong: Yes is related to operational monitoring or tuning, but it must match the exact signal needed: query history, resource utilization, wait/blocking detail, or automatic remediation. It does not expose the required metric, query history, wait/blocking signal, or tuning mechanism; using it would not give the administrator the evidence requested.
NEW QUESTION # 163
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