IMPLEMENTING DATA ENGINEERING SOLUTIONS USING MICROSOFT FABRIC EXAM LAB QUESTIONS & DP-700 VALID VCE TEST & IMPLEMENTING DATA ENGINEERING SOLUTIONS USING MICROSOFT FABRIC EXAM SIMULATOR ONLINE

Implementing Data Engineering Solutions Using Microsoft Fabric Exam Lab Questions & DP-700 valid VCE test & Implementing Data Engineering Solutions Using Microsoft Fabric Exam Simulator Online

Implementing Data Engineering Solutions Using Microsoft Fabric Exam Lab Questions & DP-700 valid VCE test & Implementing Data Engineering Solutions Using Microsoft Fabric Exam Simulator Online

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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q21-Q26):

NEW QUESTION # 21
You have a Fabric workspace named Workspace1 that contains a notebook named Notebook1.
In Workspace1, you create a new notebook named Notebook2.
You need to ensure that you can attach Notebook2 to the same Apache Spark session as Notebook1.
What should you do?

  • A. Enable high concurrency for notebooks.
  • B. Enable dynamic allocation for the Spark pool.
  • C. Increase the number of executors.
  • D. Change the runtime version.

Answer: A

Explanation:
To ensure that Notebook2 can attach to the same Apache Spark session as Notebook1, you need to enable high concurrency for notebooks. High concurrency allows multiple notebooks to share a Spark session, enabling them to run within the same Spark context and thus share resources like cached data, session state, and compute capabilities. This is particularly useful when you need notebooks to run in sequence or together while leveraging shared resources.


NEW QUESTION # 22
You have a Fabric workspace that contains a semantic model named Model1.
You need to dynamically execute and monitor the refresh progress of Model1.
What should you use?

  • A. Monitoring hub
  • B. dynamic management views in Azure Data Studio
  • C. dynamic management views in Microsoft SQL Server Management Studio
  • D. a semantic link in a notebook

Answer: D


NEW QUESTION # 23
You have a Google Cloud Storage (GCS) container named storage1 that contains the files shown in the following table.

You have a Fabric workspace named Workspace1 that has the cache for shortcuts enabled. Workspace1 contains a lakehouse named Lakehouse1. Lakehouse1 has the shortcuts shown in the following table.

You need to read data from all the shortcuts.
Which shortcuts will retrieve data from the cache?

  • A. Products only
  • B. Stores and Products only
  • C. Stores only
  • D. Trips only
  • E. Products and Trips only
  • F. Products, Stores, and Trips

Answer: B

Explanation:
When reading data from shortcuts in Fabric (in this case, from a lakehouse like Lakehouse1), the cache for shortcuts helps by storing the data locally for quick access. The last accessed timestamp and the cache expiration rules determine whether data is fetched from the cache or from the source (Google Cloud Storage, in this case).
Products: The ProductFile.parquet was last accessed 12 hours ago. Since the cache has data available for up to 12 hours, it is likely that this data will be retrieved from the cache, as it hasn't been too long since it was last accessed.
Stores: The StoreFile.json was last accessed 4 hours ago, which is within the cache retention period. Therefore, this data will also be retrieved from the cache.
Trips: The TripsFile.csv was last accessed 48 hours ago. Given that it's outside the typical caching window (assuming the cache has a maximum retention period of around 24 hours), it would not be retrieved from the cache. Instead, it will likely require a fresh read from the source.


NEW QUESTION # 24
You have a Fabric workspace named Workspace1 that contains a warehouse named Warehouse1.
You plan to deploy Warehouse1 to a new workspace named Workspace2.
As part of the deployment process, you need to verify whether Warehouse1 contains invalid references. The solution must minimize development effort.
What should you use?

  • A. a deployment pipeline
  • B. a Python script
  • C. a T-SQL script
  • D. a database project

Answer: B

Explanation:
A deployment pipeline in Fabric allows you to deploy assets like warehouses, datasets, and reports between different workspaces (such as from Workspace1 to Workspace2). One of the key features of a deployment pipeline is the ability to check for invalid references before deployment. This can help identify issues with assets, such as broken links or dependencies, ensuring the deployment is successful without introducing errors. This is the most efficient way to verify references and manage the deployment with minimal development effort.


NEW QUESTION # 25
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 KQL database that contains two tables named Stream and Reference. Stream contains streaming data in the following format.

Reference contains reference data in the following format.

Both tables contain millions of rows.
You have the following KQL queryset.

You need to reduce how long it takes to run the KQL queryset.
Solution: You add the make_list() function to the output columns.
Does this meet the goal?

  • A. No
  • B. Yes

Answer: A

Explanation:
Adding an aggregation like make_list() would require additional processing and memory, which could make the query slower.


NEW QUESTION # 26
......

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