최신 Databricks-Certified-Professional-Data-Engineer 무료덤프 - Databricks Certified Professional Data Engineer

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

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

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

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

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

A. Set the configuration delta.deduplicate = true.

B. Rely on Delta Lake schema enforcement to prevent duplicate records.

C. Perform an insert-only merge with a matching condition on a unique key.

D. VACUUM the Delta table after each batch completes.

E. Perform a full outer join on a unique key and overwrite existing data.

A. Execute a query to calculate the difference between the new version and the previous version using Delta Lake's built-in versioning and time travel functionality.

B. Parse the Delta Lake transaction log to identify all newly written data files.

C. Execute DESCRIBE HISTORY customer_churn_params to obtain the full operation metrics for the update, including a log of all records that have been added or modified.

D. Parse the Spark event logs to identify those rows that were updated, inserted, or deleted.

A. The job_id and number of times the job has been are concatenated and returned.

B. The job_id is returned in this field.

C. The number of times the job definition has been run in the workspace.

D. The globally unique ID of the newly triggered run.

A. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.

B. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.

C. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.

D. Z-order can only be applied to numeric values stored in Delta Lake tables

E. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.

A. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.

B. Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.

C. Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.

D. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB* 1024*1024/512), and then write to parquet.

E. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.

A. Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values for report key values in a DLT expectation for the report table.

B. Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null

C. Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table

D. Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table

A. Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository

B. Use repos to merge all difference and make a pull request back to the remote repository.

C. Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.

D. Use Repos to merge all differences and make a pull request back to the remote repository.

A. 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.

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. 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.

D. 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.

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.

A. Because access to production data will always be verified using passthrough credentials it is safe to mount data to any Databricks development environment.

B. Because delta Lake versions all data and supports time travel, it is not possible for user error or malicious actors to permanently delete production data, as such it is generally safe to mount production data anywhere.

C. All developer, testing and production code and data should exist in a single unified workspace; creating separate environments for testing and development further reduces risks.

D. In environments where interactive code will be executed, production data should only be accessible with read permissions; creating isolated databases for each environment further reduces risks.