Dumps Microsoft DP-600 Collection & DP-600 Study Center

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Microsoft DP-600 Exam Syllabus Topics:

Topic	Details
Topic 1	Explore and analyze data: It also deals with performing exploratory analytics. Moreover, the topic delves into query data by using SQL.
Topic 2	Implement and manage semantic models: The topic delves into designing and building semantic models, and optimizing enterprise-scale semantic models.
Topic 3	Plan, implement, and manage a solution for data analytics: Planning a data analytics environment, implementing and managing a data analytics environment are discussed in this topic. It also focuses on managing the analytics development lifecycle.
Topic 4	Prepare and serve data: In this topic, questions about creating objects in a lakehouse or warehouse, copying data, transforming data, and optimizing performance appear.

Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q17-Q22):

NEW QUESTION # 17
You have a Fabric tenant that contains a lakehouse.
You are using a Fabric notebook to save a large DataFrame by using the following code.

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:

Explanation:
* 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

NEW QUESTION # 18
You have a Fabric tenant that contains a lakehouse named lakehouse1. Lakehouse1 contains an unpartitioned table named Table1.
You plan to copy data to Table1 and partition the table based on a date column in the source data.
You create a Copy activity to copy the data to Table1.
You need to specify the partition column in the Destination settings of the Copy activity.
What should you do first?

A. From the Destination tab, select the partition column,
B. From the Destination tab, set Mode to Overwrite.
C. From the Source tab, select Enable partition discovery
D. From the Destination tab, set Mode to Append.

Answer: A

Explanation:
Before specifying the partition column in the Destination settings of the Copy activity, you should set Mode to Append (A). This will allow the Copy activity to add data to the table while taking the partition column into account. References = The configuration options for Copy activities and partitioning in Azure Data Factory, which are applicable to Fabric dataflows, are outlined in the official Azure Data Factory documentation.

NEW QUESTION # 19
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 a Fabric tenant that contains a semantic model named Model1.
You discover that the following query performs slowly against Model1.

You need to reduce the execution time of the query.
Solution: You replace line 4 by using the following code:

Does this meet the goal?

A. Yes
B. No

Answer: B

NEW QUESTION # 20
You are analyzing the data in a Fabric notebook.
You have a Spark DataFrame assigned to a variable named df.
You need to use the Chart view in the notebook to explore the data manually.
Which function should you run to make the data available in the Chart view?

A. write
B. show
C. display
D. displayMTML

Answer: C

Explanation:
The display function is the correct choice to make the data available in the Chart view within a Fabric notebook. This function is used to visualize Spark DataFrames in various formats including charts and graphs directly within the notebook environment. Reference = Further explanation of the display function can be found in the official documentation on Azure Synapse Analytics notebooks.

NEW QUESTION # 21
You need to ensure the data loading activities in the AnalyticsPOC workspace are executed in the appropriate sequence. The solution must meet the technical requirements.
What should you do?

A. Create a pipeline that has dependencies between activities and schedule the pipeline.
B. Create and schedule a Spark job definition.
C. Create a dataflow that has multiple steps and schedule the dataflow.
D. Create and schedule a Spark notebook.

Answer: A

Explanation:
To meet the technical requirement that data loading activities must ensure the raw and cleansed data is updated completely before populating the dimensional model, you would need a mechanism that allows for ordered execution. A pipeline in Microsoft Fabric with dependencies set between activities can ensure that activities are executed in a specific sequence. Once set up, the pipeline can be scheduled to run at the required intervals (hourly or daily depending on the data source).

NEW QUESTION # 22
......

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