DP-201 Designing an Azure Data Solution - Practice Questions

Question 7 (Mixed Questions)

You have a line-of-business (LOB) app that reads files from and writes files to Azure Blob storage in an Azure Storage account.

You need to recommend changes to the storage account to meet the following requirements:

Provide the highest possible availability. Minimize potential data loss.

Which three changes should you recommend? Each correct answer presents part of the solution.

NOTE: Each correct selection is worth one point.

Select all that apply, then click Submit answer.

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From the app, query the LastSyncTime of the storage account.
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From the storage account, enable soft deletes.
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From the storage account, enable read-access geo-redundancy storage (RA-GRS).
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From the app, add retry logic to the storage account interactions.
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From the storage account, enable a time-based retention policy.

Reference / correct answer:

From the storage account, enable soft deletes.

From the storage account, enable read-access geo-redundancy storage (RA-GRS).

From the storage account, enable a time-based retention policy.

Soft delete protects blob data from being accidentally or erroneously modified or deleted. When soft delete is enabled for a storage account, blobs, blob versions (preview), and snapshots in that storage account may be recovered after they are deleted, within a retention period that you specify.

Geo-redundant storage (with GRS or GZRS) replicates your data to another physical location in the secondary region to protect against regional outages. However, that data is available to be read only if the customer or Microsoft initiates a failover from the primary to secondary region. When you enable read access to the secondary region, your data is available to be read if the primary region becomes unavailable. For read access to the secondary region, enable readaccess geo-redundant storage (RA-GRS) or read-access geo-zone-redundant storage (RAGZRS).

Reference: https://docs.microsoft.com/en-us/azure/storage/blobs/soft-delete-overview

https://docs.microsoft.com/en-us/azure/storage/common/storage-redundancy#read-access-todata-in-the-secondary-region

Question 8 (Mixed Questions)

You plan to deploy an Azure SQL Database instance to support an application. You plan to use the DTU-based purchasing model.

Backups of the database must be available for 30 days and point-in-time restoration must be possible.

You need to recommend a backup and recovery policy.

What are two possible ways to achieve the goal? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

Select all that apply, then click Submit answer.

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Use the Premium tier and the default backup retention policy.
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Use the Basic tier and the default backup retention policy.
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Use the Standard tier and the default backup retention policy.
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Use the Standard tier and configure a long-term backup retention policy.
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Use the Premium tier and configure a long-term backup retention policy.

Question 9 (Mixed Questions)

You manage a process that performs analysis of daily web traffic logs on an HDInsight cluster.

Each of the 250 web servers generates approximately 10megabytes (MB) of log data each day. All log data is stored in a single folder in Microsoft Azure Data Lake Storage Gen 2.

You need to improve the performance of the process.

Which two changes should you make? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

Select all that apply, then click Submit answer.

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Combine the daily log files for all servers into one file
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Increase the value of the mapreduce.map.memory parameter
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Move the log files into folders so that each day’s logs are in their own folder
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Increase the number of worker nodes
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Increase the value of the hive.tez.container.size parameter

Reference / correct answer:

Combine the daily log files for all servers into one file

Move the log files into folders so that each day’s logs are in their own folder

A: Typically, analytics engines such as HDInsight and Azure Data Lake Analytics has a per-five overhead. If you store your data as many small files, this can negatively affect performance. In general, organize your data into larger sized files for better performance (256MB to 100GB in size). Some engines and applications might have trouble efficiently processing files that are greater than 100GB in size.

C: For Hive workloads, partition pruning of time-series data can help some queries read only a subset of the data which improves performance.

Those pipelines that ingest time-series data, often place their files with a very structured naming for files and folders. Below is a very common example we see for data is structured by date: \DataSet\YYYY\MM\DD\datafile_YYYY_MM_DD.tsv

Notice that the datetime information appears both as folders and in the filename.

Reference: https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-performance-tuningguidance

DP-201 – Designing an Azure Data Solution