DP-700 exam collection，Microsoft DP-700 actual test

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Microsoft DP-700 Exam Overview:

Certification Vendor:	Microsoft
Exam Name:	Implementing Data Engineering Solutions Using Microsoft Fabric
Exam Number:	DP-700
Real Exam Qty:	40-60
Exam Price:	Varies by region (approx. $165 USD)
Exam Duration:	100-120
Certificate Validity Period:	1 year (renewable)
Exam Format:	Scenario-based questions, Multiple choice, Case studies, Multiple response
Related Certifications:	Microsoft Certified: Fabric Analytics Engineer Associate Microsoft Certified: Azure Data Engineer Associate
Passing Score:	700/1000
Available Languages:	Japanese, Portuguese (Brazil), Spanish, English, Simplified Chinese, French, Korean, German
Recommended Training:	Microsoft Fabric Documentation Microsoft Learn DP-700 Learning Path
Exam Registration:	Pearson VUE Microsoft Exams Microsoft Learn Certification Exam Page
Sample Questions:	Microsoft DP-700 Sample Questions
Exam Way:	Online proctored via Pearson VUE or in-person at authorized testing centers
Pre Condition:	No formal prerequisites, but recommended experience with Azure data services and basic data engineering concepts
Official Syllabus URL:	https://learn.microsoft.com/credentials/certifications/exams/dp-700/

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Microsoft DP-700 Exam Syllabus Topics:

Topic	Details
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	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 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.

Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q105-Q110):

NEW QUESTION # 105
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 # 106
You have a Fabric workspace that contains a large table named Table1. Table1 contains 2 billion rows.
You have a data source that generates a data file every 30 minutes The file contains only changes that occurred since the last file was generated.
You plan to deploy a data pipeline that will process each data file when it is generated and load the contents into Table1. You need to recommend which loading pattern to use for the following operations:
* Create new records.
* Delete existing records.
The solution must support the versioning of existing records.
What should you recommend for each operation? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

NEW QUESTION # 107
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 workspace identity and enable high concurrency for the notebooks.
B. Create a workspace identity and use the identity in a data pipeline.
C. Create a shortcut and ensure that caching is disabled for the workspace.
D. Create a shortcut and ensure that caching is enabled for the workspace.

Answer: C

Explanation:
To ensure that the usage of the data in the Amazon S3 bucket meets the technical requirements, we must address two key points:

NEW QUESTION # 108
You have a Fabric .
In Warehouse1, you create a table named DimCustomer by running the following statement.

You need to set the Customerkey column as a primary key of the DimCustomer table.
Which three code segments should you run in sequence? To answer, move the appropriate code segments from the list of code segments to the answer area and arrange them in the correct order.

Answer:

Explanation:

Explanation:

NEW QUESTION # 109
You have a Fabric F32 capacity that contains a workspace. The workspace contains a warehouse named DW1 that is modelled by using MD5 hash surrogate keys.
DW1 contains a single fact table that has grown from 200 million rows to 500 million rows during the past year.
You have Microsoft Power BI reports that are based on Direct Lake. The reports show year-over-year values.
Users report that the performance of some of the reports has degraded over time and some visuals show errors.
You need to resolve the performance issues. The solution must meet the following requirements:
Provide the best query performance.
Minimize operational costs.
Which should you do?

A. Increase the capacity.C Enable V-Order
B. Change the MD5 hash to SHA256.
C. Create views.
D. Modify the surrogate keys to use a different data type.

Answer: C

Explanation:
In this case, the key issue causing performance degradation likely stems from the use of MD5 hash surrogate keys. MD5 hashes are 128-bit values, which can be inefficient for large datasets like the 500 million rows in your fact table. Using a more efficient data type for surrogate keys (such as integer or bigint) would reduce the storage and processing overhead, leading to better query performance. This approach will improve performance while minimizing operational costs because it reduces the complexity of querying and indexing, as smaller data types are generally faster and more efficient to process.

NEW QUESTION # 110
......

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