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問題 #1

A data architect is implementing Delta Sharing as part of their data governance strategy to enable secure data collaboration with external partners and internal business units. The architect must establish a permission framework that allows designated data stewards to create shares for their respective domains while maintaining security boundaries and audit compliance. Which specific permissions and roles must be assigned to enable users to create, configure, and manage Delta Shares while maintaining proper security governance and access controls?

A. Any user with USE_CATALOG privilege can create shares
B. Only workspace admins can create and manage shares
C. Users need to be metastore admins or have CREATE SHARE privilege for the metastore
D. Users need the MANAGE SHARES permission on the workspace

問題 #2

The data science team has requested assistance in accelerating queries on free form text from user reviews. The data is currently stored in Parquet with the below schema:
item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
A junior data engineer suggests converting this data to Delta Lake will improve query performance.
Which response to the junior data engineer's suggestion is correct?

A. Delta Lake statistics are not optimized for free text fields with high cardinality.
B. The Delta log creates a term matrix for free text fields to support selective filtering.
C. Delta Lake statistics are only collected on the first 4 columns in a table.
D. Text data cannot be stored with Delta Lake.
E. ZORDER ON review will need to be run to see performance gains.

問題 #3

A data engineer is brining an existing production Databricks job under asset bundle management and wants to ensure that:
- The job's current configuration is captured as YAML, and all
referenced files are included in their bundle project.
- Future changes to the bundle's YAML will update the existing job in-
place (not create a new job)
How should the data engineer successfully move the production job under asset bundle management?

A. Manually create the YAML configuration for the job in your bundle project, ensuring all settings match the existing job. Then, run Databricks bundle deploy the bundle, which will update the existing job in your workspace.
B. Run databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deployment, bind to link the bundle's job resource to the existing job in Databricks.
C. Run Databricks bundle generate job --existing-job-id to generate the YAML and download referenced files. Then, run Databricks bundle deploy to deploy the bundle, which will always update the existing job automatically.
D. Export the job definition as JSON, convert it to YAML, and place it in your bundle. Then, run Databricks bundle deploy to update the existing job.

問題 #4

A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

A. Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.
B. Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.
C. Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.
D. The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.
E. The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.

問題 #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. Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
B. A user can only transfer job ownership to a group if they are also a member of that group.
C. The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.
D. Only workspace administrators can grant "Owner" privileges to a group.
E. Other than the default "admins" group, only individual users can be granted privileges on jobs.

問題與答案：