あなたへの紹介よりあなたに自分で体験させたほうがいいと思います。弊社のIt-Passportsで無料でMicrosoftのDP-750ソフトのデモを直ちにダウンロードできます。MicrosoftのDP-750ソフトを利用してこのソフトはあなたの愛用するものになることを信じています。MicrosoftのDP-750ソフトはあなたにITという職業での人材に鳴らせます。
| Section | Weight | Objectives |
|---|---|---|
| Deploy and manage data pipelines and workloads | 30-35% | - Pipeline design and orchestration
|
| Prepare and process data | 30-35% | - Data quality and validation
|
| Secure and govern data using Unity Catalog | 15-20% | - Access control and policies
|
| Configure and manage Azure Databricks environments | 15-20% | - Workspace and compute configuration
|
ほとんどの時間インターネットにアクセスできない場合、どこかに行く必要がある場合はオフライン状態ですが、DP-750試験のために学習したい場合。心配しないでください、私たちの製品はあなたの問題を解決するのに役立ちます。最新のDP-750試験トレントは、能力を強化し、試験に合格し、認定を取得するのに非常に役立つと確信しています。嫌がらせから抜け出すために、DP-750学習教材は高品質で高い合格率を備えています。だから、今すぐ行動しましょう！ DP-750クイズ準備を使用してください。
質問 # 59
You have an Azure Databricks workspace that contains a Delta table named Customer.
A job named Job1 performs frequent upserts into Customer.
You discover that Job1 has created many small Parquet files in Customer, and the small files are degrading query performance.
You need to improve query performance for the current data already stored in Customer. The solution must not affect the travel for the Customer table.
What should you do?
正解：A
解説：
The OPTIMIZE command performs bin-packing compaction, combining the Customer table's existing small Parquet files into fewer, larger files. This reduces file-open overhead and improves data-skipping efficiency without changing the logical table contents. Enabling optimized writes affects future write operations but does not compact the small files already stored. VACUUM removes unreferenced data files that are older than the configured retention threshold; it does not reorganize active small files and can restrict time-travel availability. Reducing delta.deletedFileRetentionDuration changes how long obsolete files remain available and can directly reduce the time-travel window, contrary to the requirement. OPTIMIZE is therefore the correct operation for improving the physical layout of current data while preserving Delta table semantics and history. Microsoft Learn
質問 # 60
Your company has Sales, Finance, and HR departments.
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement Unity Catalog to meet the following requirements:
* Access to Unity Catalog for each department must be managed independently from that of the other departments.
* The data assets of each department must be logically isolated from those of the other departments.
* The solution must support centralized governance.
What should you do for each department?
正解：D
解説：
Create a separate catalog for each department, such as Sales, Finance, and HR. A catalog is the primary unit of data organization and logical isolation in Unity Catalog. Each catalog can contain its own schemas, tables, views, volumes, functions, and models. Catalog-level ownership and permissions can be assigned independently, allowing each department's access to be administered without granting access to the other departments' assets. Microsoft Learn All three catalogs remain registered in the same Unity Catalog metastore, preserving centralized governance, auditing, lineage, discovery, and access-control administration. A separate storage account is unnecessary because the requirement specifies logical rather than physical isolation. Dynamic views provide row- or column-level filtering but do not create an independent organizational and permission boundary for all departmental assets. Creating separate workspaces would add administrative complexity and is unnecessary because catalogs provide the required isolation within a centrally governed metastore.
Therefore, creating one catalog for each department provides independent access management, logical data separation, and centralized governance with the least administrative overhead.
質問 # 61
Hotspot Question
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named db1.sales_orders.
db1.sales_orders is updated nightly and has change data feed (CDF) enabled.
You need to ingest all the changes from the db1.sales_orders table, including inserts, updates, and deletes, into a downstream pipeline.
How should you complete the PsySpark code segment? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
質問 # 62
You have an Azure Databricks workspace that contains a Delta table named Table 1. Table 1 has accumulated obsolete files.
You need to reduce storage costs. The solution must preserve 30 days of time travel history. Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
正解：C、E
解説：
The correct answers are B and E.
The goal is to reduce storage costs while preserving 30 days of time travel. Two actions are needed:
Set delta.deletedFileRetentionDuration to a value aligned with the 30-day requirement (Option B). Note: the answer option states '10 days' which would be insufficient for 30-day time travel - in practice this property should be set to at least 30 days. This property defines the retention floor: VACUUM will not delete any file newer than this threshold.
Run VACUUM on Table1 (Option E). VACUUM physically removes unreferenced data files older than the retention duration from storage. Without running VACUUM, obsolete files accumulate indefinitely regardless of the retention property setting - the property tells VACUUM what to keep; VACUUM is what does the actual cleanup.
Option C (OPTIMIZE) compacts small files for better query performance but never deletes anything. Option D (logRetentionDuration) keeps the transaction log for time travel but doesn't free up the data file storage.
Reference: https://learn.microsoft.com/en-us/azure/databricks/sql/language-manual/delta-vacuum
質問 # 63
You have an Azure Databricks workspace that is enabled for Unity Catalog You have an Apache Spark Structured Streaming job that writes data to a Delta table.
After the cluster restarts, the streaming job reprocesses previously ingested data You need to prevent the streaming job from reprocessing the data after the cluster restarts.
What should you do?
正解：C
解説：
The correct answer is B - configure a checkpoint location.
A checkpoint is the Structured Streaming mechanism for fault tolerance. Databricks writes the committed offset (i.e., how far through the source stream the job has successfully read and processed) to a durable path in ADLS Gen2 or DBFS after each micro-batch. When the cluster restarts, the engine reads that offset and resumes from the next unprocessed record - nothing is reprocessed, nothing is skipped.
Option A (increase trigger interval) affects how frequently micro-batches run but does nothing to record progress between runs. Option C (watermark) handles late-arriving events in event-time windows but doesn't control source offset tracking. Option D (enable CDF on the target table) tracks changes made to a Delta table for downstream consumers - it has no bearing on the streaming job's own fault tolerance or offset management.
Checkpointing is a required configuration for any production streaming job. Without it, every cluster restart triggers a full replay from the source.
Reference: https://learn.microsoft.com/en-us/azure/databricks/structured-streaming/query-recovery
質問 # 64
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DP-750関連復習問題集: https://www.it-passports.com/DP-750.html