DP-600 Online Practice Questions and Answers

Correct Answer: B

Explanation: To identify whether throttling is occurring, you should use the Monitoring hub (B). This provides a centralized place where you can monitor and manage the health, performance, and reliability of your data estate, and see if the compute resources are being throttled. References = The use of the Monitoring hub for performance management and troubleshooting is detailed in the Azure Synapse Analytics documentation.

Correct Answer: C

Explanation: You should set Repeat for the schedule to Weekly (C). This allows you to specify the pipeline to run on specific days of the week, in this case, every four hours on Mondays and Fridays. References = Scheduling options for data pipelines are available in the Azure Data Factory documentation, which includes details on configuring recurring triggers.

Correct Answer: B

Explanation: For the Copy data activity, from the Destination tab, setting Table action to Overwrite (B) will ensure that Table1 is replaced with the schema and rows of the external data, meeting the requirements of replacing both the schema and data of the destination table. References = Information about Copy data activity and table actions in Azure Data Factory, which can be applied to data pipelines in Fabric, is available in the Azure Data Factory documentation.

Correct Answer: D

Explanation: The correct DMV to identify what is preventing the report query from completing is sys.dm_pdw_exec_requests (D). This DMV is specific to Microsoft Analytics Platform System (previously known as SQL Data Warehouse), which is the environment assumed to be used here. It provides information about all queries and load commands currently running or that have recently run. References = You can find more about DMVs in the Microsoft documentation for Analytics Platform System.

Correct Answer:

A replica of dbo.Sales is created in the test schema by copying the metadata only. - No Additional schema changes to dbo.FactSales will also apply to test.FactSales. - No Additional data changes to dbo.FactSales will also apply to test.FactSales. - Yes The CREATE TABLE AS CLONE statement creates a copy of an existing table, including its data and any associated data structures, like indexes. Therefore, the statement does not merely copy metadata; it also copies the data. However, subsequent schema changes to the original table do not automatically propagate to the cloned table. Any data changes in the original table after the clone operation will not be reflected in the clone unless explicitly updated. References = CREATE TABLE AS SELECT (CTAS) in SQL Data Warehouse

Correct Answer:

When creating a shortcut to an Azure Data Lake Storage Gen2 account in a lakehouse, you should use the abfss (Azure Blob File System Secure) connection string and the dfs (Data Lake File System) endpoint. The abfss is used for secure

access to Azure Data Lake Storage, and the dfs endpoint indicates that the Data Lake Storage Gen2 capabilities are to be used.

Correct Answer:

The results will form a hierarchy of folders for each partition key. - Yes The resulting file partitions can be read in parallel across multiple nodes. - Yes The resulting file partitions will use file compression. - No

Partitioning data by columns such as year, month, and day, as shown in the DataFrame write operation, organizes the output into a directory hierarchy that reflects the partitioning structure. This organization can improve the performance of read operations, as queries that filter by the partitioned columns can scan only the relevant directories. Moreover, partitioning facilitates parallelism because each partition can be processed independently across different nodes in a distributed system like Spark. However, the code snippet provided does not explicitly specify that file compression should be used, so we cannot assume that the output will be compressed without additional context. References = DataFrame write partitionBy Apache Spark optimization with partitioning

Correct Answer:

The relationship between OrderItem and Product must be based on: Both the CompanyID and the ProductID columns The Company entity must be: Denormalized into the Customer and Product entities

In a dimensional model, the relationships are typically based on foreign key constraints between the fact table (OrderItem) and dimension tables (Product, Customer, Date). Since CompanyID is present in both the OrderItem and Product tables, it acts as a foreign key in the relationship. Similarly, ProductID is a foreign key that relates these two tables. To enable analysis by date, product, and customer, the Company entity would need to be denormalized into the Customer and Product entities to ensure that the relevant company information is available within those dimensions for querying and reporting purposes.

References = Dimensional modeling Star schema design

Correct Answer:

The Spark engine will read only the 'SalesOrderNumber', 'OrderDate', 'CustomerName', 'UnitPrice' columns from Sales_raw.csv. - Yes Removing the partition will reduce the execution time of the query. - No Adding inferSchema='true' to the options will increase the execution time of the query. - Yes

The code specifies the selection of certain columns, which means only those columns will be read into the DataFrame. Partitions in Spark are a way to optimize the execution of queries by organizing the data into parts that can be processed in parallel. Removing the partition could potentially increase the execution time because Spark would no longer be able to process the data in parallel efficiently. The inferSchema option allows Spark to automatically detect the column data types, which can increase the execution time of the initial read operation because it requires Spark to read through the data to infer the schema.

Correct Answer:

The correct order for the DAX expression would be:

DEFINE VAR _SalesSince = DATE ( 2023, 12, 01 )

EVALUATE

FILTER (

SUMMARIZE ( Store, Store[Name], Store[OpenDate] ),

Store[OpenDate] >= _SalesSince )

In this DAX query, you're defining a variable _SalesSince to hold the date from which you want to filter the stores. EVALUATE starts the definition of the query. The FILTER function is used to return a table that filters another table or

expression. SUMMARIZE creates a summary table for the stores, including the Store[Name] and Store[OpenDate] columns, and the filter expression Store[OpenDate] >= _SalesSince ensures only stores opened on or after December 1,

2023, are included in the results.

References =

DAX FILTER Function DAX SUMMARIZE Function