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Microsoft Data Engineering on Microsoft Azure Sample Questions (Q167-Q172):

NEW QUESTION # 167
You are responsible for providing access to an Azure Data Lake Storage Gen2 account.
Your user account has contributor access to the storage account, and you have the application ID and access key.
You plan to use PolyBase to load data into an enterprise data warehouse in Azure Synapse Analytics.
You need to configure PolyBase to connect the data warehouse to storage account.
Which three components should you create in sequence? To answer, move the appropriate components from the list of components to the answer area and arrange them in the correct order.

Answer:

Explanation:

Explanation:

NEW QUESTION # 168
The following code segment is used to create an Azure Databricks cluster.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Reference:
https://adatis.co.uk/databricks-cluster-sizing/
https://docs.microsoft.com/en-us/azure/databricks/jobs
https://docs.databricks.com/administration-guide/capacity-planning/cmbp.html
https://docs.databricks.com/delta/index.html

NEW QUESTION # 169
You have an on-premises data warehouse that includes the following fact tables. Both tables have the following columns: DateKey, ProductKey, RegionKey. There are 120 unique product keys and 65 unique region keys.

Queries that use the data warehouse take a long time to complete.
You plan to migrate the solution to use Azure Synapse Analytics. You need to ensure that the Azure-based solution optimizes query performance and minimizes processing skew.
What should you recommend? 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/sql-data-warehouse/sql-data-warehouse-tables-distribute

NEW QUESTION # 170
You are designing an Azure Data Lake Storage solution that will transform raw JSON files for use in an analytical workload.
You need to recommend a format for the transformed files. The solution must meet the following requirements:
* Contain information about the data types of each column in the files.
* Support querying a subset of columns in the files.
* Support read-heavy analytical workloads.
* Minimize the file size.
What should you recommend?

A. Apache Avro
B. Apache Parquet
C. CSV
D. JSON

Answer: B

Explanation:
Explanation
Parquet, an open-source file format for Hadoop, stores nested data structures in a flat columnar format.
Compared to a traditional approach where data is stored in a row-oriented approach, Parquet file format is more efficient in terms of storage and performance.
It is especially good for queries that read particular columns from a "wide" (with many columns) table since only needed columns are read, and IO is minimized.
Reference: https://www.clairvoyant.ai/blog/big-data-file-formats

NEW QUESTION # 171
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 after creation.
Which service should you use to ingest the data?

A. Azure Synapse Analytics
B. Azure Data Factory
C. Azure Stream Analytics
D. Azure Event Hubs Dedicated

Answer: C

Explanation:
Explanation
You can't change the partition count for an event hub after its creation except for the event hub in a dedicated cluster.
Reference:
https://docs.microsoft.com/en-us/azure/event-hubs/event-hubs-features

NEW QUESTION # 172
......

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