NEW DP-600 TEST TESTKING & DP-600 RELIABLE EXAM MATERIALS

New DP-600 Test Testking & DP-600 Reliable Exam Materials

New DP-600 Test Testking & DP-600 Reliable Exam Materials

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Tags: New DP-600 Test Testking, DP-600 Reliable Exam Materials, New DP-600 Mock Test, Valid DP-600 Test Blueprint, DP-600 Free Learning Cram

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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.

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q49-Q54):

NEW QUESTION # 49
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. predictive
  • C. diagnostic
  • D. descriptive

Answer: D

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.
References: Descriptive analytics and the use of histograms as a way to visualize data distribution are basic concepts in data analysis, often covered in introductory analytics and Python programming resources.


NEW QUESTION # 50
You have a Fabric tenant that contains a new semantic model in OneLake.
You use a Fabric notebook to read the data into a Spark DataFrame.
You need to evaluate the data to calculate the min, max, mean, and standard deviation values for all the string and numeric columns.
Solution: You use the following PySpark expression:
df .sumary ()
Does this meet the goal?

  • A. No
  • B. Yes

Answer: B

Explanation:
Yes, the df.summary() method does meet the goal. This method is used to compute specified statistics for numeric and string columns. By default, it provides statistics such as count, mean, stddev, min, and max. Reference = The PySpark API documentation details the summary() function and the statistics it provides.


NEW QUESTION # 51
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. No
  • B. Yes

Answer: A


NEW QUESTION # 52
Hotspot Question
You have a Fabric warehouse that contains two tables named DimDate and Trips.
DimDate contains the following fields.

Trips contains the following fields.

You need to compare the average miles per trip for statutory holidays versus non-statutory holidays.
How should you complete the T-SQL statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 53
You have a Microsoft Power Bl semantic model that contains measures. The measures use multiple calculate functions and a filter function.
You are evaluating the performance of the measures.
In which use case will replacing the filter function with the keepfilters function reduce execution time?

  • A. when the filter function uses a nested calculate function
  • B. when the filter function references a column from a single table that uses Import mode
  • C. when the filter function references columns from multiple tables
  • D. when the filter function references a measure

Answer: A

Explanation:
The KEEPFILTERS function modifies the way filters are applied in calculations done through the CALCULATE function. It can be particularly beneficial to replace the FILTER function with KEEPFILTERS when the filter context is being overridden by nested CALCULATE functions, which may remove filters that are being applied on a column. This can potentially reduce execution time because KEEPFILTERS maintains the existing filter context and allows the nested CALCULATE functions to be evaluated more efficiently.


NEW QUESTION # 54
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