Latest Microsoft DP-700 Exam Tips - New DP-700 Exam Topics

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Microsoft DP-700 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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.
Topic 3
  • 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.

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The Microsoft DP-700 certification exam is an industrial-recognized Implementing Data Engineering Solutions Using Microsoft Fabric (DP-700) certificate that is designed to validate candidates' skills, experience, and knowledge quickly. The Implementing Data Engineering Solutions Using Microsoft Fabric (DP-700) certification exam has been inspiring Microsoft professionals since its beginning. Now this DP-700 certification exam has become solid proof of certain skills set and knowledge.

Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q146-Q151):

NEW QUESTION # 146
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 move the filter to line 02.
Does this meet the goal?

Answer: A

Explanation:
Moving the filter to line 02: Filtering the Stream table before performing the join operation reduces the number of rows that need to be processed during the join. This is an effective optimization technique for queries involving large datasets.


NEW QUESTION # 147
You have a Fabric workspace that contains a data pipeline named Pipeline! as shown in the exhibit.
(Click the Exhibit tab.) What will occur the next time Pipelinel tuns?

Answer: D


NEW QUESTION # 148
You plan to use Apache Spark to process the data in Lakehouse1.
You need to configure the workspace to ensure that users can share Spark cluster resources across notebooks.
The solution must ensure that variables and code execution in one notebook remain isolated from those in other notebooks.
What should you do?

Answer: B

Explanation:
The correct configuration is high concurrency mode . High concurrency enables multiple compatible Fabric notebooks to share the resources of an existing Spark application rather than requiring a separate Spark application for every notebook. This improves Spark resource utilization and substantially reduces session startup overhead.
Critically, sharing the Spark application does not mean that notebook execution state is merged. Microsoft Fabric assigns each workload its own REPL core within the shared Spark application. Each REPL core maintains isolated execution state, which prevents local variables or code execution in one notebook from overwriting the execution state of another notebook. This directly satisfies the requirement for both resource sharing and notebook-level isolation. Microsoft Learn For notebooks to share a high-concurrency session, Microsoft specifies compatibility requirements such as the same user boundary, default lakehouse, Spark compute configuration, and appropriate library configuration.
Microsoft Learn
Autoscaling changes the number of executors allocated according to workload demand but does not provide notebook session sharing. The native execution engine is intended to improve Spark execution performance rather than enable concurrent notebook isolation. Per-notebook environment configuration likewise does not provide the required shared Spark session architecture.
Study Guide reference: Monitor and optimize an analytics solution # Configure and optimize Spark # high concurrency sessions # Spark resource utilization and workload isolation.


NEW QUESTION # 149
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 # 150
You have a Fabric warehouse named DW1 that contains a Type 2 slowly changing dimension (SCD) dimension table named DimCustomer. DimCustomer contains 100 columns and 20 million rows. The columns are of various data types, including int, varchar, date, and varbinary.
You need to identify incoming changes to the table and update the records when there is a change. The solution must minimize resource consumption.
What should you use to identify changes to attributes?

Answer: C


NEW QUESTION # 151
......

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