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Microsoft DP-700 Exam Syllabus Topics:

TopicDetails
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
  • 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
  • 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.

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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q49-Q54):

NEW QUESTION # 49
You have a Fabric workspace named Workspace1.
You need to implement workspace monitoring for Workspace1. The solution must meet the following requirements:
* Ensure that the logs and metrics of the items in Workspace1 are collected automatically.
* Minimize administrative effort.
What should you do?

Answer: D


NEW QUESTION # 50
You have a Fabric workspace named Workspace1 that contains a lakehouse named Lakehouse1.
You need to automate a PySpark-based data transformation process. The solution must meet the following requirements:
* Ensure that users can edit and test the PySpark code interactively.
* Ensure that the code can be operationalized for automated execution.
Which three actions should you perform in sequence?

Answer:

Explanation:

Explanation:
The correct sequence is Create a notebook # Attach the notebook to Lakehouse1 # Create a data pipeline
.
First, create a Fabric notebook because notebooks provide the interactive development environment required for authoring, executing, debugging, and testing PySpark code. Microsoft describes Fabric notebooks as a primary code item for developing Apache Spark workloads and specifically supports their use for data ingestion, preparation, and transformation.
Second, attach the notebook to Lakehouse1 . Attaching a lakehouse establishes the notebook ' s lakehouse context, allowing PySpark code to access the lakehouse ' s Files and Tables through Spark and OneLake. This enables developers to interactively validate the transformation against the required Fabric data.
Third, create a data pipeline and use a Notebook activity to operationalize the notebook. Fabric Data Factory explicitly supports running Fabric notebooks as pipeline activities for automated data transformation workflows. Pipelines can subsequently be run manually, scheduled, or incorporated into larger orchestration processes.
Converting the notebook to an Apache Spark job definition is unnecessary; Spark job definitions are separate executable Fabric items. Publishing the notebook is also not required for pipeline execution.
Study Guide reference: Ingest and transform data # Transform data with Apache Spark # Fabric notebooks # Orchestrate processes and schedules with Data Factory pipelines.


NEW QUESTION # 51
You are building a Fabric notebook named MasterNotebookl in a workspace. MasterNotebookl contains the following code.

You need to ensure that the notebooks are executed in the following sequence:
1. Notebook_03
2. Notebook.Ol
3. Notebook_02
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

Answer: B,F


NEW QUESTION # 52
HOTSPOT
You need to troubleshoot the ad-hoc query issue.
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:

Explanation:
A screenshot of a computer Description automatically generated

SELECT last_run_start_time, last_run_command: These fields will help identify the execution details of the long-running queries.
FROM queryinsights.long_running_queries: The correct solution is to check the long-running queries using the queryinsights.long_running_queries view, which provides insights into queries that take longer than expected to execute.
WHERE last_run_total_elapsed_time_ms > 7200000: This condition filters queries that took more than 2 hours to complete (7200000 milliseconds), which is relevant to the issue described.
AND number_of_failed_runs > 1: This condition is key for identifying queries that have failed more than once, helping to isolate the problematic queries that cause failures and need attention.


NEW QUESTION # 53
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?

Answer: A

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


NEW QUESTION # 54
......

It-Tests DP-700 practice test simulates the real Microsoft DP-700 exam environment. This situation boosts the candidate's performance and enhances their confidence. After attempting the DP-700 practice exams, candidates become more familiar with a real Implementing Data Engineering Solutions Using Microsoft Fabric DP-700 Exam environment and develop the stamina to sit for several hours consecutively to complete the DP-700 exam. This way, the actual Implementing Data Engineering Solutions Using Microsoft Fabric DP-700 exam becomes much easier for them to handle.

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