Question 1

Which statement regarding static Delta tables in Stream-Static joins is correct?

Options :

A : Static Delta tables must be small enough to be broadcasted to all worker nodes in the cluster.

B : Static Delta tables need to be partitioned in order to be used in stream-static join.

C : Static Delta tables need to be refreshed with REFRESH TABLE command for each microbatch of a stream-static join

D : The latest version of the static Delta table is returned each time it is queried by a microbatch of the stream-static join

E : The latest version of the static Delta table is returned only for the first microbatch of the stream-static join. Then, it will be cached to be used by any upcoming microbatch.

Answer: D

Question 2

A developer has successfully configured their credentials for Databricks Repos and cloned a remote Git repository. They do not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace.

Which approach allows this user to share their code updates without the risk of overwriting the work of their teammates?

Options :

A : Use Repos to create a new branch, commit all changes, and push changes to the remote Git repository.

B : Use Repos to create a fork of the remote repository, commit all changes, and make a pull request on the source repository.

C : Use Repos to pull changes from the remote Git repository; commit and push changes to a branch that appeared as changes were pulled.

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

Answer: A

Question 3

A data engineer wants to upload a CSV file from local system storage to the DBFS of a Databricks workspace. They have Databricks CLI already configured on the local system.

Which of the following Databricks CLI commands can the data engineer use to complete this task ?

Options :

A : workspace

B : fs

C : jobs

D : configure

E : libraries

Answer: B

Question 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?

Options :

A : 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.

B : 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.

C : 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.

D : 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.

E : The Jobs UI 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.

Answer: D

Question 5

When using CLI or REST API to get results from jobs with multiple tasks, which statement correctly describes the response structure?

Options :

A : Each run of a job will have a unique job_id; all tasks within this job will have a unique job_id

B : Each run of a job will have a unique job_id; all tasks within this job will have a unique task_id

C : Each run of a job will have a unique orchestration_id; all tasks within this job will have a unique run_id

D : Each run of a job will have a unique run_id; all tasks within this job will have a unique task_id

E : Each run of a job will have a unique run_id; all tasks within this job will also have a unique run_id

Answer: D