EXAM MICROSOFT DP-600 PRICE - LATEST DP-600 DUMPS QUESTIONS

Exam Microsoft DP-600 Price - Latest DP-600 Dumps Questions

Exam Microsoft DP-600 Price - Latest DP-600 Dumps Questions

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Tags: Exam DP-600 Price, Latest DP-600 Dumps Questions, Reliable DP-600 Dumps Questions, DP-600 Exam Braindumps, DP-600 PDF VCE

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Implementing Analytics Solutions Using Microsoft Fabric exam practice questions play a crucial role in Implementing Analytics Solutions Using Microsoft Fabric DP-600 exam preparation and give you insights Implementing Analytics Solutions Using Microsoft Fabric exam view. You are aware of the Implementing Analytics Solutions Using Microsoft Fabric DP-600 exam topics, structure, and a number of the questions that you will face in the upcoming Implementing Analytics Solutions Using Microsoft Fabric DP-600 Exam. You can evaluate your Salesforce Implementing Analytics Solutions Using Microsoft Fabric exam preparation performance and work on the weak topic areas. But here is the problem where you will get Implementing Analytics Solutions Using Microsoft Fabric exam questions.

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
  • 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.

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q52-Q57):

NEW QUESTION # 52
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 lakehouse 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: DESCRIBE HISTORY customer Does this meet the goal?

  • A. Yes
  • B. No

Answer: A


NEW QUESTION # 53
You have source data in a folder on a local computer.
You need to create a solution that will use Fabric to populate a data store. The solution must meet the following requirements:
Support the use of dataflows to load and append data to the data store.
Ensure that Delta tables are V-Order optimized and compacted automatically.
Which type of data store should you use?

  • A. a lakehouse
  • B. a KQL database
  • C. an Azure SQL database
  • D. a warehouse

Answer: A

Explanation:
To meet the requirements of supporting dataflows to load and append data to the data store while ensuring that Delta tables are V-Order optimized and compacted automatically, you should use a lakehouse in Fabric as your solution.


NEW QUESTION # 54
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:

Explanation:
* Remove the files: Run the VACUUM command on a schedule.
* Combine the files: Set the optimizeWrite table setting. or Run the OPTIMIZE command on a schedule.
To remove files that are no longer used, the VACUUM command is used in Delta Lake to clean up invalid files from a table. To combine smaller files into larger ones, you can either set the optimizeWrite setting to combine files during write operations or use the OPTIMIZE command, which is a Delta Lake operation used to compact small files into larger ones.


NEW QUESTION # 55
You have a Fabric tenant that contains a data pipeline.
You need to ensure that the pipeline runs every four hours on Mondays and Fridays.
To what should you set Repeat for the schedule?

  • A. Hourly
  • B. Weekly
  • C. Daily
  • D. By the minute

Answer: B

Explanation:
You should set Repeat for the schedule to Weekly (C). This allows you to specify the pipeline to run on specific days of the week, in this case, every four hours on Mondays and Fridays. References = Scheduling options for data pipelines are available in the Azure Data Factory documentation, which includes details on configuring recurring triggers.


NEW QUESTION # 56
You have a Fabric tenant that contains customer churn data stored as Parquet files in OneLake.
The data contains details about customer demographics and product usage.
You create a Fabric notebook to read the data into a Spark DataFrame. You then create column charts in the notebook that show the distribution of retained customers as compared to lost customers based on geography, the number of products purchased, age, and customer tenure.
Which type of analytics are you performing?

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

Answer: D

Explanation:
Descriptive analytics tells what happened in the past, presenting it as numbers and visuals in reports and dashboards.
Diagnostic analytics gives the reason why something happened.
Predictive analytics determines the potential outcomes of present and past actions and trends.
Prescriptive analytics offers decision support for the best course of action.
Given the scenario in the question where data is read into a Spark DataFrame and column charts are created to show the distribution of retained customers compared to lost customers based on various factors, this falls under the definition of descriptive analytics. No future predictions or prescriptions are made, nor are reasons for the past events provided.


NEW QUESTION # 57
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