DP-600 PRACTICE MATERIALS & DP-600 ACTUAL EXAM & DP-600 TEST PREP

DP-600 Practice Materials & DP-600 Actual Exam & DP-600 Test Prep

DP-600 Practice Materials & DP-600 Actual Exam & DP-600 Test Prep

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Tags: Latest DP-600 Dumps, DP-600 Sample Exam, Exam DP-600 Simulator Online, New DP-600 Test Book, Valid Braindumps DP-600 Sheet

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Microsoft DP-600 Exam Syllabus Topics:

TopicDetails
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
  • Prepare data: In this topic, questions about creating objects in a lakehouse or warehouse, copying data, transforming data, and optimizing performance appear.
Topic 3
  • Implement and manage semantic models: The topic delves into designing and building semantic models, and optimizing enterprise-scale semantic models.

Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q78-Q83):

NEW QUESTION # 78
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. show
  • B. display
  • C. write
  • D. displayMTML

Answer: B


NEW QUESTION # 79
You have a Fabric tenant tha1 contains a takehouse named Lakehouse1. Lakehouse1 contains a Delta table named Customer.
When you query Customer, you discover that the query is slow to execute. You suspect that maintenance was NOT performed on the table.
You need to identify whether maintenance tasks were performed on Customer.
Solution: You run the following Spark SQL statement:
EXPLAIN TABLE customer
Does this meet the goal?

  • A. Yes
  • B. No

Answer: B

Explanation:
No, the EXPLAIN TABLE statement does not identify whether maintenance tasks were performed on a table.
It shows the execution plan for a query. References = The usage and output of the EXPLAIN command can be found in the Spark SQL documentation.


NEW QUESTION # 80
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?

  • A.
  • B.
  • C.
  • D.

Answer: A

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. References = You can refer to the official Apache Spark documentation for more details on joins and the broadcast hint.


NEW QUESTION # 81
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. prescriptive
  • B. descriptive
  • C. predictive
  • D. diagnostic

Answer: B

Explanation:
The Python code and output shown in the exhibit display a histogram, which is a representation of the distribution of data. This kind of analysis is descriptive analytics, which is used to describe or summarize the features of a dataset. Descriptive analytics answers the question of "what has happened" by providing insight into past data through tools such as mean, median, mode, standard deviation, and graphical representations like histograms.


NEW QUESTION # 82
You have a Fabric workspace named Workspace 1 that contains a dataflow named Dataflow1. Dataflow! has a query that returns 2.000 rows. You view the query in Power Query as shown in the following exhibit.

What can you identify about the pickupLongitude column?

  • A. All the table rows are profiled.
  • B. The column has duplicate values.
  • C. The column has missing values.
  • D. There are 935 values that occur only once.

Answer: A

Explanation:
The pickupLongitude column has duplicate values. This can be inferred because the 'Distinct count' is 935 while the 'Count' is 1000, indicating that there are repeated values within the column. References = Microsoft Power BI documentation on data profiling could provide further insights into understanding and interpreting column statistics like these.


NEW QUESTION # 83
......

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