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IT 職員のそれぞれは昇進または高給のために頑張っています。これも現代社会が圧力に満ちている一つの反映です。そのためにMicrosoftのDP-600認定試験に受かる必要があります。適当なトレーニング資料を選んだらこの試験はそんなに難しくなくなります。It-PassportsのMicrosoftのDP-600「Implementing Analytics Solutions Using Microsoft Fabric」試験トレーニング資料は最高のトレーニング資料で、あなたの全てのニーズを満たすことができますから、速く行動しましょう。
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It-PassportsのDP-600トレーニングテストの利点の1つは、無料の販売前体験をユーザーに提供できることです。DP-600学習資料ページはサンプルの質問モジュールを提供します。 Microsoft購入する前に、ユーザーはさらにDP-600のImplementing Analytics Solutions Using Microsoft Fabric試験準備を使用します。 同時に、提供するサンプルユーザーがPDFデモを無料でダウンロードできる方が便利なため、販売前の体験は他に類を見ません。 そのため、DP-600学習教材の効率性を把握し、間違いなく選択することを決定できます。
質問 # 25
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 Fabric tenant that contains a semantic model named Model1.
You discover that the following query performs slowly against Model1.
You need to reduce the execution time of the query.
Solution: You replace line 4 by using the following code:
Does this meet the goal?
正解：B
質問 # 26
You have a Microsoft Power Bl semantic model that contains measures. The measures use multiple calculate functions and a filter function.
You are evaluating the performance of the measures.
In which use case will replacing the filter function with the keepfilters function reduce execution time?
正解：B
解説：
The KEEPFILTERS function modifies the way filters are applied in calculations done through the CALCULATE function. It can be particularly beneficial to replace the FILTER function with KEEPFILTERS when the filter context is being overridden by nested CALCULATE functions, which may remove filters that are being applied on a column. This can potentially reduce execution time because KEEPFILTERS maintains the existing filter context and allows the nested CALCULATE functions to be evaluated more efficiently.
質問 # 27
You have a Fabric workspace named Workspace1.
Workspace1 contains multiple semantic models, including a model named Model1. Model1 is updated by using an XMLA endpoint.
You need to increase the speed of the write operations of the XMLA endpoint.
What should you do?
正解：C
解説：
When using XMLA endpoints to manage and update semantic models in Microsoft Fabric, the performance of write operations (such as processing, structural changes, or metadata deployments from Tabular Editor) is directly influenced by the storage format and how the model is persisted.
Why Option A is Correct
* By default, Fabric semantic models use the Small semantic model storage format.
* To improve write operations performance through XMLA, you must change the workspace setting to use the Large semantic model storage format.
* The large format uses more efficient storage techniques, supports partitioning, and handles larger models with optimized write capabilities.
* This setting is applied at the workspace level and impacts all semantic models within that workspace, including Model1.
This is explicitly documented in Microsoft's guidance: Large semantic model storage format is required when using XMLA write operations for large or frequently updated models.
Why the Other Options Are Incorrect
B). Configure Model1 to use the Direct Lake storage format.
* Direct Lake mode is designed for query performance (reading data directly from OneLake in delta format without import/duplication).
* It improves query latency and freshness but does not improve XMLA write operations, which deal with model metadata and structural updates.
C). Delete any unused semantic models from Workspace1.
* Deleting unused semantic models helps manage capacity and storage but does not increase the speed of XMLA endpoint write operations.
* Workspace storage overhead does not directly impact the write throughput of XMLA operations.
D). Delete any unused columns from Model1.
* Removing unused columns reduces the memory footprint and can improve query performance.
* However, it does not directly improve the speed of XMLA write operations. The bottleneck in XMLA writes is tied to the storage format, not the model size alone.
Summary
To increase the speed of XMLA write operations on semantic models, you must enable the Large semantic model storage format at the workspace level. This setting ensures better handling of writes and metadata operations via the XMLA endpoint.
References
* Large models in Power BI and Microsoft Fabric
* Use the XMLA endpoint in Microsoft Fabric
* Manage capacities in Microsoft Fabric
質問 # 28
You have a Fabric tenant that contains a machine learning model registered in a Fabric workspace. You need to use the model to generate predictions by using the predict function in a fabric notebook. Which two languages can you use to perform model scoring? Each correct answer presents a complete solution. NOTE:
Each correct answer is worth one point.
正解：A、C
解説：
The two languages you can use to perform model scoring in a Fabric notebook using the predict function are Spark SQL (option C) and PySpark (option D). These are both part of the Apache Spark ecosystem and are supported for machine learning tasks in a Fabric environment. References = You can find more information about model scoring and supported languages in the context of Fabric notebooks in the official documentation on Azure Synapse Analytics.
質問 # 29
You have a Microsoft Power Bl report and a semantic model that uses Direct Lake mode. From Power Bl Desktop, you open Performance analyzer as shown in the following exhibit.
Use the dropdown menus to select the answer choice that completes each statement based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
質問 # 30
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