ユーザーが知識構造の完全なシステムを形成できるようにするためのDP-750スタディガイド、テスト解釈の資格DP-750試験、および有機的で合理的な取り決めをサポートするコースの練習、DP-750新しいカリキュラムのセクションは、DP-750試験準備を使用して論理的フレームワークの知識を構築して良好な状態を作成するユーザー向けに、問題を解決する方法を通じて統合し、結束とリンクの間の各セクションを密接にリンクできます。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Secure and govern data using Unity Catalog | 15-20% | - Data governance fundamentals
|
| Topic 2: Prepare and process data | 30-35% | - Data ingestion
|
| Topic 3: Deploy and manage data pipelines and workloads | 30-35% | - Lakehouse architecture operations
|
| Topic 4: Configure and manage Azure Databricks environments | 15-20% | - Security and authentication setup
|
DP-750認定を取得することは多くの人にとって簡単ではないことがわかっていますが、良いニュースをお伝えできることを嬉しく思います。当社の教材は、短時間でDP-750認定を取得するのに役立ちます。 DP-750練習問題を詳細にご紹介します。貴重な時間を割いてDP-750試験の質問をご覧ください。私たちはあなたを失望させないと信じてください。ウェブ上のDP-750トレーニングガイドのデモを無料でダウンロードして、優れた品質を知ることができます。
質問 # 15
Hotspot Question
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to implement a data lifecycle and expiration solution that meets the following requirements:
- Transaction logs and deleted data files that are older than 90 days
must be removed from Delta tables to reclaim storage.
- All the tables must remain available for querying during the cleanup
process.
- Administrative effort must be minimized.
What should you do for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
質問 # 16
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You plan to create a job in Lakeflow Jobs named Job1 that:
* Ingests data from cloud storage
* Runs two independent transformation tasks
The transformation tasks must run only after the ingestion completes and must run in parallel.
You need to design the task logic for Job1.
What should you configure?
正解：B
解説：
Job1 should contain one ingestion task that acts as the common upstream dependency for two separate transformation tasks. Once ingestion succeeds, Lakeflow Jobs can start both downstream tasks concurrently because neither transformation depends on the other. This design represents the actual workflow, avoids duplicated ingestion, and reduces total execution time through parallelism. Creating two ingestion tasks would repeat the same source processing and could introduce inconsistent results or unnecessary costs. A single sequential task would prevent parallel transformation and make failures harder to isolate and retry. Defining three independent tasks without dependencies could allow transformations to start before ingestion has completed. An explicit directed task graph therefore provides the required execution order while preserving parallelism for independent downstream processing.
質問 # 17
You have an Azure Databricks workspace.
You are creating a Lakeflow Spark Declarative Pipelines (SDP) pipeline that scales automatically.
You need to configure compute for the pipeline. The solution must minimize operational costs and effort.
What should you use?
正解：D
解説：
The best option for a Lakeflow Spark Declarative Pipelines (SDP) pipeline that scales automatically while keeping costs and administrative effort low is a job cluster that uses autoscaling.
Lowest Costs: Job clusters (also called automated compute) are billed at a significantly lower Data Processing Unit (DBU) rate compared to all-purpose clusters. By enabling autoscaling, Databricks dynamically allocates or removes worker nodes based on real-time pipeline demand, ensuring you never pay for unutilized resources.
Low Administrative Effort: While Databricks generally recommends Serverless compute as the absolute ideal for zero-admin pipelines, when selecting from classic compute options, a job cluster automatically handles its own lifecycle. It deploys when the pipeline starts executing and terminates automatically when processing is finished.
Incorrect:
[Not A]
Databricks SQL warehouses are designed to run standalone materialized views and streaming tables via standard SQL. They are not the native compute vehicle for running a fully automated, dedicated Lakeflow Spark Declarative Pipelines (SDP) deployment framework.
[Not B]
All-purpose compute is meant for interactive development, debugging, and ad-hoc analysis. It is billed at a much higher DBU rate, which violates the requirement to keep costs low.
[Not D]
Aside from the higher billing rate of all-purpose compute, a single-node configuration does not scale horizontally. This directly conflicts with your requirement to build a pipeline that scales automatically.
Reference:
https://docs.databricks.com/gcp/en/ldp/auto-scaling
質問 # 18
You need to configure resiliency for a job in Lakeflow Jobs named Job1 to meet the pipeline deployment and operation requirements.
What should you do?
正解：B
解説：
Task-level retries allow the ingestion task to recover automatically from transient failures without rerunning unrelated tasks or restarting the complete workflow. Downstream tasks remain governed by their dependencies and start only after ingestion succeeds. This provides focused failure recovery and reduces unnecessary compute consumption. Disabling retries and relying on manual execution directly contradicts the requirement for resilient, automated pipeline operation. Setting the retry count to zero also prevents automatic retry behavior. Restarting the workflow from the first task whenever any task fails would repeat completed processing, increase costs, and potentially reingest data unnecessarily. Lakeflow Jobs supports individual retry policies for tasks, including the number of retries and delay between attempts, making option D the most controlled and operationally efficient configuration. Microsoft Learn
質問 # 19
You have an Azure Databticks workspace that is enabled for Unity Catalog and contains a catalog named catalog1.
You have a group named group!
You plan to create a schema named schema1 in catalog1.
You need to ensure that group1 meets the following requirements:
* Can create tables in schema1
* Can modify and query tables
* Cannot grant permissions for the schema and its objects
How should you complete the SQL statements? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
The correct SQL grants group1 the ability to work within the schema without delegating that ability to anyone else:
GRANT USE SCHEMA ON schema1 TO group1 - required as a prerequisite to access any object inside the schema.
GRANT CREATE TABLE ON SCHEMA schema1 TO group1 - allows creating new tables.
GRANT SELECT, MODIFY ON SCHEMA schema1 TO group1 - SELECT for queries, MODIFY for INSERT/UPDATE/DELETE operations.
Crucially, MANAGE is NOT granted. In Unity Catalog, MANAGE is what allows a principal to grant and revoke privileges on the schema and its objects. Leaving it out means group1 can do all the data work but cannot redistribute those permissions - precisely what the requirement 'Cannot grant permissions for the schema and its objects' demands.
Reference: https://learn.microsoft.com/en-us/azure/databricks/data-governance/unity-catalog/manage- privileges/privileges
質問 # 20
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DP-750関連日本語版問題集: https://www.topexam.jp/DP-750_shiken.html