Microsoft DP-700 Exams Training | DP-700 Certification Exam

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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 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 (Q39-Q44):

NEW QUESTION # 39
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. Trips only
B. Products and Trips only
C. Products, Stores, and Trips
D. Products only
E. Stores and Products only
F. Stores only

Answer: E

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 # 40
You have a Fabric workspace that contains a warehouse named Warehouse1. Data is loaded daily into Warehouse1 by using data pipelines and stored procedures.
You discover that the daily data load takes longer than expected.
You need to monitor Warehouse1 to identify the names of users that are actively running queries.
Which view should you use?

A. queryinsights.frequently_run_queries
B. queryinsights.long_running_queries
C. sys.dm_exec_connections
D. sys.dm_exec_sessions
E. sys.dm_exec_requests

Answer: D

Explanation:
sys.dm_exec_sessions provides real-time information about all active sessions, including the user, session ID, and status of the session. You can filter on session status to see users actively running queries.

NEW QUESTION # 41
You need to ensure that usage of the data in the Amazon S3 bucket meets the technical requirements.
What should you do?

A. Create a shortcut and ensure that caching is enabled for the workspace.
B. Create a workspace identity and use the identity in a data pipeline.
C. Create a workspace identity and enable high concurrency for the notebooks.
D. Create a shortcut and ensure that caching is disabled for the workspace.

Answer: D

Explanation:
To ensure that the usage of the data in the Amazon S3 bucket meets the technical requirements, we must address two key points:
Minimize egress costs associated with cross-cloud data access: Using a shortcut ensures that Fabric does not replicate the data from the S3 bucket into the lakehouse but rather provides direct access to the data in its original location. This minimizes cross-cloud data transfer and avoids additional egress costs.
Prevent saving a copy of the raw data in the lakehouses: Disabling caching ensures that the raw data is not copied or persisted in the Fabric workspace. The data is accessed on-demand directly from the Amazon S3 bucket.

NEW QUESTION # 42
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 # 43
You have a Fabric warehouse named DW1 that loads data by using a data pipeline named Pipeline1. Pipeline1 uses a Copy data activity with a dynamic SQL source. Pipeline1 is scheduled to run every 15 minutes.
You discover that Pipeline1 keeps failing.
You need to identify which SQL query was executed when the pipeline failed.
What should you do?

A. From Real-time hub, select Fabric events, and then review the details of Microsoft. Fabric.ItemUpdateFailed.
B. From Monitoring hub, select the latest failed run of Pipeline1, and then view the input JSON.
C. From Monitoring hub, select the latest failed run of Pipeline1, and then view the output JSON.
D. From Real-time hub, select Fabric events, and then review the details of Microsoft.Fabric.ItemReadFailed.

Answer: B

Explanation:
The input JSON contains the configuration details and parameters passed to the Copy data activity during execution, including the dynamically generated SQL query.
Viewing the input JSON for the failed pipeline run provides direct insight into what query was executed at the time of failure.

NEW QUESTION # 44
......

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Microsoft DP-700 Exams Training | DP-700 Certification Exam

Microsoft DP-700 Exam Syllabus Topics:

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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q39-Q44):

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