Databricks-Certified-Professional-Data-Engineerキャリアパス、Databricks-Certified-Professional-Data-Engineerシュミレーション問題集

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Databricks認定プロフェッショナルデータエンジニアの試験では、データの摂取、変換、ストレージ、処理など、幅広いトピックについて説明しています。この試験では、DataBricksツールとテクノロジーを使用して、実際の問題と課題を解決する候補者の能力をテストします。この試験に合格した候補者は、ビッグデータ処理と分析のための主要なクラウドベースのプラットフォームであるDataBricksを使用して、データパイプラインの設計、構築、管理に習熟していることを示しています。

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完璧Databricks-Certified-Professional-Data-Engineer｜有効的なDatabricks-Certified-Professional-Data-Engineerキャリアパス試験｜試験の準備方法Databricks Certified Professional Data Engineer Examシュミレーション問題集

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Databricks Certified Professional Data Engineer試験は、Databricksを使用して一連のタスクを完了することが求められる実践的な試験です。この試験は、データパイプラインを設計・実装し、データソースやシンクを操作し、Databricksを使用して変換を行う能力を評価します。また、データパイプラインをパフォーマンスや信頼性の観点から最適化し、チューニングする能力も試験されます。

Databricks Certified Professional Data Engineer Exam 認定 Databricks-Certified-Professional-Data-Engineer 試験問題 (Q175-Q180):

質問 # 175
A data engineer has configured their Databricks Asset Bundle with multiple targets in databricks.yml and deployed it to the production workspace. Now, to validate the deployment, they need to invoke a job named my_project_job specifically within the prod target context. Assuming the job is already deployed, they need to trigger its execution while ensuring the target-specific configuration is respected.
Which command will trigger the job execution?

A. databricks execute my_project_job -e prod
B. databricks run my_project_job -t prod
C. databricks job run my_project_job --env prod
D. databricks bundle run my_project_job -t prod

正解：D

解説：
Databricks Asset Bundles (DABs) enable declarative configuration and deployment of Databricks resources such as jobs, pipelines, and dashboards across multiple environments.
Once deployed, jobs can be executed in a specific target context using the databricks bundle run command, which ensures all environment-specific configurations from the bundle definition (such as parameters, cluster settings, and workspace URLs) are respected.
The -t flag specifies the target environment (e.g., dev, staging, or prod). This ensures that the execution runs with the correct configuration defined under that target in databricks.yml.
Other options (A, B, and C) are invalid because they reference deprecated or incorrect command syntax that doesn't integrate with bundle targets. Therefore, D is the correct and verified answer.

質問 # 176
At the end of the inventory process, a file gets uploaded to the cloud object storage, you are asked to build a process to ingest data which of the following method can be used to ingest the data in-crementally, schema of the file is expected to change overtime ingestion process should be able to handle these changes automatically.
Below is the auto loader to command to load the data, fill in the blanks for successful execution of below code.
1.spark.readStream
2..format("cloudfiles")
3..option("_______","csv)
4..option("_______", 'dbfs:/location/checkpoint/')
5..load(data_source)
6..writeStream
7..option("_______",' dbfs:/location/checkpoint/')
8..option("_______", "true")
9..table(table_name))

A. cloudfiles.format, checkpointlocation, cloudfiles.schemalocation, overwrite
B. cloudfiles.format, cloudfiles.schemalocation, checkpointlocation, append
C. cloudfiles.format, cloudfiles.schemalocation, checkpointlocation, mergeSchema
D. format, checkpointlocation, schemalocation, overwrite
E. cloudfiles.format, cloudfiles.schemalocation, checkpointlocation, overwrite

正解：C

解説：
Explanation
The answer is cloudfiles.format, cloudfiles.schemalocation, checkpointlocation, mergeSchema.
Here is the end to end syntax of streaming ELT, below link contains complete options Auto Loader options | Databricks on AWS
1.spark.readStream
2..format("cloudfiles") # Returns a stream data source, reads data as it arrives based on the trigger.
3..option("cloudfiles.format","csv") # Format of the incoming files
4..option("cloudfiles.schemalocation", "dbfs:/location/checkpoint/") The location to store the inferred schema and subsequent changes
5..load(data_source)
6..writeStream
7..option("checkpointlocation","dbfs:/location/checkpoint/") # The location of the stream's checkpoint
8..option("mergeSchema", "true") # Infer the schema across multiple files and to merge the schema of each file. Enabled by default for Auto Loader when inferring the schema.
9..table(table_name)) # target table

質問 # 177
Which of the following is correct for the global temporary view?

A. global temporary views can be still accessed even if the notebook is detached and at-tached
B. global temporary views are created in a database called temp database
C. global temporary views can be still accessed even if the cluster is restarted
D. global temporary views cannot be accessed once the notebook is detached and attached
E. global temporary views can be accessed across many clusters

正解：A

解説：
Explanation
The answer is global temporary views can be still accessed even if the notebook is detached and attached There are two types of temporary views that can be created Local and Global
* A local temporary view is only available with a spark session, so another notebook in the same cluster can not access it. if a notebook is detached and reattached local temporary view is lost.
* A global temporary view is available to all the notebooks in the cluster, even if the notebook is detached and reattached it can still be accessible but if a cluster is restarted the global temporary view is lost.

質問 # 178
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE.
Three datasets are defined against Delta Lake table sources using LIVE TABLE . The table is configured to
run in Development mode using the Triggered Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after
clicking Start to update the pipeline?

A. All datasets will be updated once and the pipeline will shut down. The compute resources will be
terminated
B. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will
be deployed for the update and terminated when the pipeline is stopped
C. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to
allow for additional testing
D. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will
persist after the pipeline is stopped to allow for additional testing
E. All datasets will be updated continuously and the pipeline will not shut down. The compute resources
will persist with the pipeline

正解：C

質問 # 179
Although the Databricks Utilities Secrets module provides tools to store sensitive credentials and avoid accidentally displaying them in plain text users should still be careful with which credentials are stored here and which users have access to using these secrets.
Which statement describes a limitation of Databricks Secrets?

A. Because the SHA256 hash is used to obfuscate stored secrets, reversing this hash will display the value in plain text.
B. Account administrators can see all secrets in plain text by loggingon to the Databricks Accounts console.
C. Iterating through a stored secret and printing each character will display secret contents in plain text.
D. The Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials.
E. Secrets are stored in an administrators-only table within the Hive Metastore; database administrators have permission to query this table by default.

正解：D

解説：
Explanation
This is the correct answer because it describes a limitation of Databricks Secrets. Databricks Secrets is a module that provides tools to store sensitive credentials and avoid accidentally displaying them in plain text.
Databricks Secrets allows creating secret scopes, which are collections of secrets that can be accessed by users or groups. Databricks Secrets also allows creating and managing secrets using the Databricks CLI or the Databricks REST API. However, a limitation of Databricks Secrets is that the Databricks REST API can be used to list secrets in plain text if the personal access token has proper credentials. Therefore, users should still be careful with which credentials are stored in Databricks Secrets and which users have access to using these secrets. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under "List secrets" section.

質問 # 180
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