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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 (Q32-Q37):

NEW QUESTION # 32
You have a Fabric workspace.
You are debugging a statement and discover the following issues:
You need to resolve the issues. The solution must ensure that the data types of the results are retained. The results can contain blank cells.
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 # 33
HOTSPOT
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:

Explanation:

NEW QUESTION # 34
HOTSPOT
You have an Azure Event Hubs data source that contains weather data.
You ingest the data from the data source by using an eventstream named Eventstream1. Eventstream1 uses a lakehouse as the destination.
You need to batch ingest only rows from the data source where the City attribute has a value of Kansas. The filter must be added before the destination. The solution must minimize development effort.
What should you use for the data processor and filtering? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 35
HOTSPOT
You have a Fabric workspace that contains a warehouse named DW1. DW1 contains the following tables and columns.

You need to create an output that presents the summarized values of all the order quantities by year and product. The results must include a summary of the order quantities at the year level for all the products.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 36
You have a Fabric eventstream that ingests events from an Azure Event Hubs source named input1. You compute an aggregate by using a 60-second tumbling window.
You need to ensure that the window meets the following requirements:
* Provides updated results every five seconds.
* Ensures that each update reflects a rolling, 60-second history of events.
What should you do?

A. Keep the 60-second tumbling window and add a filter that only emits rows when the event timestamp falls on a five-second boundary.
B. Change the tumbling window size to five seconds.
C. Replace the tumbling window with a hopping window that has a 60-second window size and a five- second hop.
D. Change the tumbling window size to 65 seconds to account for late-arriving events and increase the output frequency.

Answer: C

Explanation:
A hopping window is the correct window type when an aggregation must cover a fixed historical duration while producing results at a shorter recurring interval. In this case, the required historical range is 60 seconds , while a new calculation must be produced every five seconds . Therefore, configuring a hopping window with a 60-second window size and five-second hop creates overlapping 60-second windows whose start points are five seconds apart. Each output consequently reflects the most recent 60-second period while being recalculated every five seconds.
A tumbling window behaves differently. Tumbling windows divide the event stream into fixed, non- overlapping intervals . A 60-second tumbling window therefore does not provide a rolling result every five seconds. Reducing it to five seconds would increase output frequency but would calculate only five seconds of history.
Microsoft ' s Eventstream SQL documentation demonstrates HoppingWindow specifically for rolling-window scenarios, such as evaluating a five-minute interval every minute. Microsoft Learn Filtering timestamps does not change the aggregation window semantics, and increasing the tumbling duration to 65 seconds does not increase output frequency.
Study Guide reference: Ingest and transform data # Real-Time Intelligence # Eventstreams # windowed aggregation # tumbling and hopping windows.

NEW QUESTION # 37
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