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Microsoft DP-600 Exam Syllabus Topics:

Topic	Details
Topic 1	Maintain a data analytics solution: This section is all about implementing security and governance. In this topic, you also get information about maintaining the analytics development lifecycle.
Topic 2	Implement and manage semantic models: The topic delves into designing and building semantic models, and optimizing enterprise-scale semantic models.
Topic 3	Prepare 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 (Q99-Q104):

NEW QUESTION # 99
You have a Fabric tenant that contains a semantic model. The model uses Direct Lake mode.
You suspect that some DAX queries load unnecessary columns into memory.
You need to identify the frequently used columns that are loaded into memory.
What are two ways to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.

A. Query the $system.discovered_STORAGE_TABLE_COLUMN-iN_SEGMeNTS dynamic management view (DMV).
B. Use the Analyze in Excel feature.
C. Query the discover_hehory6Rant dynamic management view (DMV).
D. Use the Vertipaq Analyzer tool.

Answer: A,D

Explanation:
The Vertipaq Analyzer tool (B) and querying the
$system.discovered_STORAGE_TABLE_COLUMNS_IN_SEGMENTS dynamic management view (DMV) (C) can help identify which columns are frequently loaded into memory. Both methods provide insights into the storage and retrieval aspects of the semantic model. References = The Power BI documentation on Vertipaq Analyzer and DMV queries offers detailed guidance on how to use these tools for performance analysis.

NEW QUESTION # 100
You have a Fabric tenant that contains a lakehouse named Lakehouse1
Readings from 100 loT devices are appended to a Delta table in Lakehouse1. Each set of readings is approximately 25 KB. Approximately 10 GB of data is received daily.
All the table and SparkSession settings are set to the default.
You discover that queries are slow to execute. In addition, the lakehouse storage contains data and log files that are no longer used.
You need to remove the files that are no longer used and combine small files into larger files with a target size of 1 GB per file.
What should you do? To answer, drag the appropriate actions to the correct requirements. Each action may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 101
You are analyzing customer purchases in a Fabric notebook by using PySpanc You have the following DataFrames:

You need to join the DataFrames on the customer_id column. The solution must minimize data shuffling. You write the following code.

Which code should you run to populate the results DataFrame?

Answer: B

Explanation:
The correct code to populate the results DataFrame with minimal data shuffling is Option A. Using the broadcast function in PySpark is a way to minimize data movement by broadcasting the smaller DataFrame (customers) to each node in the cluster. This is ideal when one DataFrame is much smaller than the other, as in this case with customers. Reference = You can refer to the official Apache Spark documentation for more details on joins and the broadcast hint.

NEW QUESTION # 102
You have a Fabric notebook that has the Python code and output shown in the following exhibit.

Which type of analytics are you performing?

A. diagnostic
B. descriptive
C. predictive
D. prescriptive

Answer: B

Explanation:
https://azure.microsoft.com/es-es/blog/answering-whats-happening-whys-happening-and-what- will-happen-with-iot-analytics/

NEW QUESTION # 103
You have a Fabric tenant that contains a lakehouse named Lakehouse1.
You need to prevent new tables added to Lakehouse1 from being added automatically to the default semantic model of the lakehouse.
What should you configure?

A. the SQL analytics endpoint settings
B. the semantic model settings
C. the workspace settings
D. the Lakehouse1 settings

Answer: A

Explanation:
You need to prevent new tables added to Lakehouse1 from being added automatically to the default semantic model of the lakehouse.
https://learn.microsoft.com/en-us/fabric/data-warehouse/semantic-models but "Sync the default Power BI semantic model".

NEW QUESTION # 104
......

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