Latest DP-700 Exam Labs - DP-700 Standard Answers

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Microsoft DP-700 Exam Syllabus Topics:

Topic	Details
Topic 1	Implement and manage an analytics solution: This section of the exam measures the skills of Microsoft Data Analysts regarding configuring various workspace settings in Microsoft Fabric. It focuses on setting up Microsoft Fabric workspaces, including Spark and domain workspace configurations, as well as implementing lifecycle management and version control. One skill to be measured is creating deployment pipelines for analytics solutions.
Topic 2	Ingest and transform data: This section of the exam measures the skills of Data Engineers that cover designing and implementing data loading patterns. It emphasizes preparing data for loading into dimensional models, handling batch and streaming data ingestion, and transforming data using various methods. A skill to be measured is applying appropriate transformation techniques to ensure data quality.
Topic 3	Monitor and optimize an analytics solution: This section of the exam measures the skills of Data Analysts in monitoring various components of analytics solutions in Microsoft Fabric. It focuses on tracking data ingestion, transformation processes, and semantic model refreshes while configuring alerts for error resolution. One skill to be measured is identifying performance bottlenecks in analytics workflows.

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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q56-Q61):

NEW QUESTION # 56
DRAG DROP
You have a Fabric eventhouse that contains a KQL database. The database contains a table named TaxiDat a. The following is a sample of the data in TaxiData.

You need to build two KQL queries. The solution must meet the following requirements:
One of the queries must partition RunningTotalAmount by VendorID.
The other query must create a column named FirstPickupDateTime that shows the first value of each hour from tpep_pickup_datetime partitioned by payment_type.
How should you complete each query? To answer, drag the appropriate values the correct targets. Each value 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:

NEW QUESTION # 57
You have two Fabric workspaces named Workspace1 and Workspace2.
You have a Fabric deployment pipeline named deployPipeline1 that deploys items from Workspace1 to Workspace2. DeployPipeline1 contains all the items in Workspace1.
You recently modified the items in Workspaces1.
The workspaces currently contain the items shown in the following table.

Items in Workspace1 that have the same name as items in Workspace2 are currently paired.
You need to ensure that the items in Workspace1 overwrite the corresponding items in Workspace2. The solution must minimize effort.
What should you do?

A. Delete all the items in Workspace2, and then run deployPipeline1.
B. Run deployPipeline1 without modifying the items in Workspace2.
C. Back up the items in Workspace2, and then run deployPipeline1.
D. Rename each item in Workspace2 to have the same name as the items in Workspace1.

Answer: B

Explanation:
When running a deployment pipeline in Fabric, if the items in Workspace1 are paired with the corresponding items in Workspace2 (based on the same name), the deployment pipeline will automatically overwrite the existing items in Workspace2 with the modified items from Workspace1. There's no need to delete, rename, or back up items manually unless you need to keep versions. By simply running deployPipeline1, the pipeline will handle overwriting the existing items in Workspace2 based on the pairing, ensuring the latest version of the items is deployed with minimal effort.

NEW QUESTION # 58
You are building a data loading pattern for Fabric notebook workloads.
You have the following code segment:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 59
You have a Fabric workspace that contains a lakehouse named Lakehousel. Lakehousel contains a table named Status_Target that has the following columns:
* Key
* Status
* LastModified
The data source contains a table named Status.Source that has the same columns as Status_Target. Status.Source is used to populate Status_Target. In a notebook name Notebook!, you load Status_Source to a DataFrame named sourceDF and Status_Target to a DataFrame named targetDF. You need to implement an incremental loading pattern by using Notebook-!. The solution must meet the following requirements:
* For all the matching records that have the same value of key, update the value of LastModified in Status_Target to the value of LastModified in Status_Source.
* Insert all the records that exist in Status_Source that do NOT exist in Status_Target.
* Set the value of Status in Status_Target to inactive for all the records that were last modified more than seven days ago and that do NOT exist in Status.Source.
How should you complete the statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 60
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, Stores, and Trips
B. Products only
C. Stores and Products only
D. Products and Trips only
E. Stores only
F. Trips only

Answer: C

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 # 61
......

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LATEST DP-700 EXAM LABS - DP-700 STANDARD ANSWERS

Latest DP-700 Exam Labs - DP-700 Standard Answers