あなたはDP-750試験資料をよく勉強する限り、きっと短い時間で有難いDP-750認定試験資格証明書を取得できます。その後、あなたの生活もきっと大きく変わります。そして、いい友達ができ、いい生活を送ります。躊躇しないで、未来は本当に美しいです！DP-750試験資料有効であるかどうか分からない場合、Microsoftウエブサイトで、DP-750試験資料のデモを無料でダウンロードしてください。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deploy and manage data pipelines and workloads | 30-35% | - Pipeline design and orchestration
|
| Topic 2: Secure and govern data using Unity Catalog | 15-20% | - Access control and policies
|
| Topic 3: Configure and manage Azure Databricks environments | 15-20% | - Workspace and compute configuration
|
| Topic 4: Prepare and process data | 30-35% | - Data ingestion
|
Implementing Data Engineering Solutions Using Azure Databricksテストの準備は、主に当社のクライアントは、DP-750試験に合格するのを助けると認証を得ることができます。この認証は、クライアントに大きなメリットをもたらします。クライアントは大企業に参入し、高給を稼ぐことができます。 DP-750試験に合格すると、給与を2倍にすることができます。認定資格を所有している場合、DP-750クイズトレントを十分にマスターし、優れた能力を所有していることを証明し、会社または工場で尊敬されます。あなたの仕事を変えたいなら、それはあなたにとっても良いことです。
質問 # 68
You have an Azure Databricks workspace that contains an all-purpose cluster named Cluster1.
You discover that out of- memory (OOM) errors intermittently cause jobs running on Cluster1 to fail.
You need to identify the root cause of the failures by analyzing the runtime execution behavior. What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Diagnosing OOM errors requires analysing actual runtime execution behaviour. The Spark UI is the primary tool - it's accessible from the cluster detail page and captures rich per-stage and per-task metrics without any extra setup.
In the Executors tab, look at storage memory used, execution memory used, memory spill to disk, and GC time per executor. An executor showing high memory spill is a strong indicator that it's processing more data than it can hold in memory - often caused by data skew, where one partition is far larger than the others.
The Stages tab shows task distribution - if one task in a stage is processing 10x more data than its peers, that's data skew causing memory pressure on that specific executor. Ganglia (available on older runtimes) provides node-level OS metrics like heap usage over time, which can confirm whether memory pressure is sustained or spiky.
These built-in tools give a complete picture of the root cause before making any configuration changes.
Reference: https://learn.microsoft.com/en-us/azure/databricks/compute/monitor-cluster
質問 # 69
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You need to ingest data from the CSV files stored in an external Azure Data Lake Storage Gen2 container into a managed Delta table. The solution must meet the following requirements:
* Load data incrementally as new files arrive.
* Minimize custom code and operational effort.
How should you complete the SQL statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
COPY INTO loads files from a cloud-storage location into an existing Delta table. It is retriable and idempotent: Azure Databricks records which source files have already been processed and skips them during subsequent executions. Consequently, the statement can be run repeatedly to load newly arrived CSV files without reloading previously processed files. This provides incremental ingestion with minimal custom tracking code or operational logic. Microsoft Learn The FROM clause specifies the source ADLS Gen2 path containing the CSV files. CREATE TABLE defines a new table but does not incrementally load arriving files into the existing main.sales.orders table. CREATE TABLE AS SELECT creates and populates a new table from a query result and is not the correct recurring file-ingestion operation. FILE and LOCATION are not valid replacements for FROM in the displayed COPY INTO syntax.
Therefore, the statement must begin with COPY INTO and use FROM before the source storage path.
質問 # 70
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two catalogs named Catalog1 and Catalog2.
An external application uses a service principal named SP1 to connect to a SQL warehouse.
You need to ensure that SP1 can query the data in Catalog1 and Catalog2. The solution must follow the principle of least privilege.
Which permissions should you grant to SP1 for the catalogs?
正解：C
解説：
To query tables governed by Unity Catalog, SP1 requires permissions at every relevant level of the three-level namespace. USE CATALOG permits SP1 to interact with objects inside Catalog1 and Catalog2. USE SCHEMA permits it to access the schemas contained in those catalogs. Finally, SELECT authorizes SP1 to read data from the tables and views.
Because Unity Catalog supports privilege inheritance, granting USE SCHEMA and SELECT at the catalog level applies those permissions to the applicable current and future child objects. This is appropriate when SP1 must query data throughout both catalogs. These permissions provide read-only access and do not allow SP1 to create objects or modify and delete data. Microsoft Learn Option B is missing USE CATALOG. Option C provides namespace access but does not authorize data retrieval. Option D lacks USE SCHEMA, which is required to interact with objects inside each schema.
Therefore, SP1 requires USE CATALOG, USE SCHEMA, and SELECT.
質問 # 71
You have an Azure Databricks account that contains a single workspace named Workspace1. Workspace1 is enabled for Unity Catalog.
You discover that data access events for Unity Catalog tables fail to appear in the logs.
You need to ensure that all the data access events are captured centrally for auditing purposes. The log data must be available for analysis as quickly as possible.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
Enable audit logging for: The Databricks account
Send audit logs to: Log Analytics
Enabling audit logging at the Databricks account level provides centralized coverage of account and workspace activities, including Unity Catalog data-access events. Enabling logging only for Workspace1 would provide a narrower configuration and would not offer the same centralized account-level auditing model. Individual Unity Catalog tables and the Azure subscription are not the appropriate Databricks audit- log scope selections. Log Analytics is the suitable destination because it supports rapid ingestion and immediate querying of log records through Azure Monitor Logs. Auditors can search, filter, correlate, and analyze the captured events without first developing a separate data-processing solution. An Azure Storage account is more suitable for inexpensive archival, while Event Hubs is primarily a streaming transport that normally requires an additional consumer before investigators can analyze the records.
質問 # 72
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains two Delta tables named Table1 and Table2 of the same data type.
Table1 contains a column named Column1. Table2 contains a column named Column2.
You run the following query.
SELECT Colum1
FROM Table1
GROUP BY Column1
HAVING COUNT(*) > 1
INTERSECT
SELECT Column2
FROM Table2
GROUP BY Column2
HAVING COUNT(*) > 1;
What occurs when you run the query?
正解：D
解説：
The query will output a single column containing only the values that are duplicated within Table1 and also duplicated within Table2.
Duplicates Isolated First: The GROUP BY and HAVING COUNT(*) > 1 clauses filter each table independently to find only their duplicate rows.
Matching Values Found: The INTERSECT operator compares the filtered results of both tables.
Shared Values Returned: Only values that exist in both filtered datasets are included in the final output.
Automatic Distinct Filtering: INTERSECT automatically removes any duplicate values from the final result set, ensuring each matching value appears exactly once.
Column Naming: The output column will inherit its name from the first SELECT statement (Column1).
Reference:
https://quizlet.com/study-guides/inf3707-chapter-4-data-retrieval-from-2-or-more-tables-
6ec17b26-76a8-4c33-8fa7-18d64154512d
質問 # 73
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