Databricks Certification Databricks-Certified-Data-Engineer-Professional

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最新的 Databricks Certification Databricks-Certified-Data-Engineer-Professional 免費考試真題:

1. Two data engineers are working on the same Databricks notebook in separate branches. Both have edited the same section of code. When one tries to merge the other's branch into their own using the Databricks Git folders UI, a merge conflict occurs on that notebook file. The UI highlights the conflict and presents options for resolution. How should the data engineers resolve this merge conflict using Databricks Git folders?

A) Use the Git CLI in the cluster's web terminal to force-push the conflicted merge (git push -force), overriding the remote branch with the local version and discarding changes.
B) Use the Git folders UI to manually edit the notebook file, selecting the desired lines from both versions and removing the conflict markers, then mark the conflict as resolved.
C) Delete the conflicted notebook file via the Databricks workspace UI, commit the deletion, and recreate the notebook from scratch in a new commit to bypass the conflict entirely.
D) Abort the merge, discard all local changes, and try the merge operation again without reviewing the conflicting code.

2. Which statement describes Delta Lake optimized writes?

A) Before a job cluster terminates, OPTIMIZE is executed on all tables modified during the most recent job.
B) A shuffle occurs prior to writing to try to group data together resulting in fewer files instead of each executor writing multiple files based on directory partitions.
C) Optimized writes logical partitions instead of directory partitions partition boundaries are only represented in metadata fewer small files are written.
D) An asynchronous job runs after the write completes to detect if files could be further compacted; yes, an OPTIMIZE job is executed toward a default of 1 GB.

3. The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?

A) Whenever a table is being created, make sure that the location keyword is used.
B) When tables are created, make sure that the external keyword is used in the create table statement.
C) When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
D) Whenever a database is being created, make sure that the location keyword is used
E) When the workspace is being configured, make sure that external cloud object storage has been mounted.

4. A data engineer is attempting to execute the following PySpark code:
df = spark.read.table("sales")
result = df.groupBy("region").agg(sum("revenue"))
However, upon inspecting the execution plan and profiling the Spark job, they observe excessive data shuffling during the aggregation phase.
Which technique should be applied to reduce shuffling during the groupBy aggregation operation?

A) Caching the DataFrame df.
B) Repartition by region before aggregation.
C) Use broadcast join.
D) Use coalesce() after the aggregation.

5. A data engineer needs to productionize a new Spark application written by teammate. This application has numerous external dependencies, including libraries, and requires custom environment variables and Spark configuration parameters to be set. Which two methods will help the data engineer accomplish the task? (Choose two.)

A) Install libraries on DBFS
B) Use compute policies to set system properties, environment variables, and Spark configuration parameters.
C) Add libraries to compute policies
D) Use secrets in init scripts to store configuration data
E) Create init scripts on DBFS.

問題與答案：