Databricks-Certified-Professional-Data-Engineer問題集、Databricks実際の試験問題

質問 1

A data engineer is developing a Lakeflow Declarative Pipeline (LDP) using a Databricks notebook directly connected to their pipeline. After adding new table definitions and transformation logic in their notebook, they want to check for any syntax errors in the pipeline code without actually processing data or running the pipeline.
How should the data engineer perform this syntax check?

A. Disconnect the notebook from the pipeline and reconnect it to a compute cluster to access code validation features.

B. Use the "Validate" option in the notebook to check for syntax errors.

C. Switch to a workspace file instead of a notebook to access validation and diagnostics tools.

D. Open the web terminal from the notebook and run a shell command to validate the pipeline code.

正解: B

解説: (PassTest メンバーにのみ表示されます)

質問 2

A data team is automating a daily multi-task ETL pipeline in Databricks. The pipeline includes a notebook for ingesting raw data, a Python wheel task for data transformation, and a SQL query to update aggregates. They want to trigger the pipeline programmatically and see previous runs in the GUI. They need to ensure tasks are retried on failure and stakeholders are notified by email if any task fails.
Which two approaches will meet these requirements? (Choose 2 answers)

A. Create a single orchestrator notebook that calls each step with dbutils.notebook.run(), defining a job for that notebook and configuring retries and notifications at the notebook level.

B. Use the REST API endpoint /jobs/runs/submit to trigger each task individually as separate job runs and implement retries using custom logic in the orchestrator.

C. Trigger the job programmatically using the Databricks Jobs REST API (/jobs/run-now), the CLI (databricks jobs run-now), or one of the Databricks SDKs.

D. Use Databricks Asset Bundles (DABs) to deploy the workflow, then trigger individual tasks directly by referencing each task's notebook or script path in the workspace.

E. Create a multi-task job using the UI, Databricks Asset Bundles (DABs), or the Jobs REST API (/jobs
/create) with notebook, Python wheel, and SQL tasks. Configure task-level retries and email notifications in the job definition.

正解: C,E

解説: (PassTest メンバーにのみ表示されます)

質問 3

A Structured Streaming job deployed to production has been resulting in higher than expected cloud storage costs. At present, during normal execution, each micro-batch of data is processed in less than 3 seconds; at least 12 times per minute, a micro-batch is processed that contains 0 records. The streaming write was configured using the default trigger settings. The production job is currently scheduled alongside many other Databricks jobs in a workspace with instance pools provisioned to reduce start-up time for jobs with batch execution. Holding all other variables constant and assuming records need to be processed in less than 10 minutes, which adjustment will meet the requirement?

A. Set the trigger interval to 3 seconds; the default trigger interval is consuming too many records per batch, resulting in spill to disk that can increase volume costs.

B. Set the trigger interval to 10 minutes; each batch calls APIs in the source storage account, so decreasing trigger frequency to the maximum allowable threshold should minimize this cost.

C. Set the trigger interval to 500 milliseconds; setting a small but non-zero trigger interval ensures that the source is not queried too frequently.

D. Use the trigger once option and configure a Databricks job to execute the query every 10 minutes; this approach minimizes costs for both compute and storage.

正解: D

解説: (PassTest メンバーにのみ表示されます)

質問 4

A data engineering team is setting up deployment automation. To deploy workspace assets remotely using the Databricks CLI command, they must configure it with proper authentication.
Which authentication approach will provide the highest level of security ?

A. Use a shared user account and its OAuth client secret.

B. Use a service principal ID and its OAuth client secret.

C. Use a service principal with OAuth token federation.

D. Use a service principal and its Personal Access Token.

正解: C

質問 5

A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI. Upon reviewing their work, the engineer realizes that they are listed as the " Owner " for each job. They attempt to transfer " Owner " privileges to the " DevOps " group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?

A. Other than the default " admins " group, only individual users can be granted privileges on jobs.

B. Databricks jobs must have exactly one owner; " Owner " privileges cannot be assigned to a group.

C. A user can only transfer job ownership to a group if they are also a member of that group.

D. The creator of a Databricks job will always have " Owner " privileges; this configuration cannot be changed.

E. Only workspace administrators can grant " Owner " privileges to a group.

正解: B

解説: (PassTest メンバーにのみ表示されます)

質問 6

The data architect has decided that once data has been ingested from external sources into the Databricks Lakehouse, table access controls will be leveraged to manage permissions for all production tables and views.
The following logic was executed to grant privileges for interactive queries on a production database to the core engineering group.
GRANT USAGE ON DATABASE prod TO eng;
GRANT SELECT ON DATABASE prod TO eng;
Assuming these are the only privileges that have been granted to the eng group and that these users are not workspace administrators, which statement describes their privileges?

A. Group members are able to query and modify all tables and views in the prod database, but cannot create new tables or views.

B. Group members are able to create, query, and modify all tables and views in the prod database, but cannot define custom functions.

C. Group members are able to query all tables and views in the prod database, but cannot create or edit anything in the database.

D. Group members are able to list all tables in the prod database but are not able to see the results of any queries on those tables.

E. Group members have full permissions on the prod database and can also assign permissions to other users or groups.

正解: C

解説: (PassTest メンバーにのみ表示されます)

質問 7

A data engineer is designing a pipeline in Databricks that processes records from a Kafka stream where late- arriving data is common.
Which approach should the data engineer use?

A. Use batch processing and overwrite the entire output table each time to ensure late data is incorporated correctly.

B. Use a watermark to specify the allowed lateness to accommodate records that arrive after their expected window, ensuring correct aggregation and state management.

C. Use an Auto CDC pipeline with batch tables to simplify late data handling.

D. Implement a custom solution using Databricks Jobs to periodically reprocess all historical data.

正解: B

解説: (PassTest メンバーにのみ表示されます)

質問 8

A data team ' s Structured Streaming job is configured to calculate running aggregates for item sales to update a downstream marketing dashboard. The marketing team has introduced a new field to track the number of times this promotion code is used for each item. A junior data engineer suggests updating the existing query as follows: Note that proposed changes are in bold.

Which step must also be completed to put the proposed query into production?

A. Specify a new checkpointlocation

B. Increase the shuffle partitions to account for additional aggregates

C. Run REFRESH TABLE delta, /item_agg '

D. Remove .option (mergeSchema ' , true ' ) from the streaming write

正解: A

解説: (PassTest メンバーにのみ表示されます)

質問 9

How are the operational aspects of Lakeflow Declarative Pipelines different from Spark Structured Streaming ?

A. Lakeflow Declarative Pipelines manage the orchestration of multi-stage pipelines automatically, while Structured Streaming requires external orchestration for complex dependencies.

B. Lakeflow Declarative Pipelines automatically handle schema evolution, while Structured Streaming always requires manual schema management.

C. Structured Streaming can process continuous data streams, while Lakeflow Declarative Pipelines cannot.

D. Lakeflow Declarative Pipelines can write to Delta Lake format, while Structured Streaming cannot.

正解: A

解説: (PassTest メンバーにのみ表示されます)

Databricks Certified Professional Data Engineer - Databricks-Certified-Professional-Data-Engineer 模擬練習