MICROSOFT DP-600 WEB-BASED PRACTICE EXAM QUESTIONS

Microsoft DP-600 Web-Based Practice Exam Questions

Microsoft DP-600 Web-Based Practice Exam Questions

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There are various individuals who have never shown up for the Implementing Analytics Solutions Using Microsoft Fabric certification test as of now. They know close to nothing about the Implementing Analytics Solutions Using Microsoft Fabric exam model and how to attempt the requests. Microsoft DP-600 Dumps give an unequivocal thought of the last preliminary of the year model and how a promising rookie ought to attempt the solicitation paper to score well.

Microsoft DP-600 Exam Syllabus Topics:

TopicDetails
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
  • Prepare and serve data: In this topic, questions about creating objects in a lakehouse or warehouse, copying data, transforming data, and optimizing performance appear.
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
  • 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 (Q27-Q32):

NEW QUESTION # 27
You have a Fabric tenant that contains a data warehouse.
You need to load rows into a large Type 2 slowly changing dimension (SCD). The solution must minimize resource usage. Which T-SQL statement should you use?

  • A. MERGE
  • B. CREATE TABLE AS SELECT
  • C. update and insert
  • D. TRUNCATE TABLE and INSERT

Answer: A


NEW QUESTION # 28
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. Reference = You can refer to the official Apache Spark documentation for more details on joins and the broadcast hint.


NEW QUESTION # 29
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.explain()
Does this meet the goal?

  • A. No
  • B. Yes

Answer: A


NEW QUESTION # 30
You have a Fabric tenant that contains a semantic model named Model1. Model1 uses Import mode. Model1 contains a table named Orders. Orders has 100 million rows and the following fields.

You need to reduce the memory used by Model! and the time it takes to refresh the model. Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct answer is worth one point.

  • A. Convert Quantity into the Text data type.
  • B. Replace TotalQuantity with a calculated column.
  • C. Split OrderDateTime into separate date and time columns.
  • D. Replace TotalSalesAmount with a measure.

Answer: B,D

Explanation:
To reduce memory usage and refresh time, splitting the OrderDateTime into separate date and time columns (A) can help optimize the model because date/time data types can be more memory-intensive than separate date and time columns. Moreover, replacing TotalSalesAmount with a measure (D) instead of a calculated column ensures that the calculation is performed at query time, which can reduce the size of the model as the value is not stored but calculated on the fly. References = The best practices for optimizing Power BI models are detailed in the Power BI documentation, which recommends using measures for calculations that don't need to be stored and adjusting data types to improve performance.


NEW QUESTION # 31
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 # 32
......

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