Databricks-Certified-Data-Engineer-Associate Free Practice Test

Which of the following data lakehouse features results in improved data quality over a traditional data lake?

1. A. A data lakehouse provides storage solutions for structured and unstructured data.
2. B. A data lakehouse supports ACID-compliant transactions.
3. C. A data lakehouse allows the use of SQL queries to examine data.
4. D. A data lakehouse stores data in open formats.
5. E. A data lakehouse enables machine learning and artificial Intelligence workloads.

Correct Answer: B
One of the key features of a data lakehouse that results in improved data quality over a traditional data lake is its support for ACID (Atomicity, Consistency, Isolation, Durability) transactions. ACID transactions provide data integrity and consistency guarantees, ensuring that operations on the data are reliable and that data is not left in an inconsistent state due to failures or concurrent access. In a traditional data lake, such transactional guarantees are often lacking, making it challenging to maintain data quality,
especially in scenarios involving multiple data writes, updates, or complex transformations. A data lakehouse, by offering ACID compliance, helps maintain data quality by providing strong consistency and reliability, which is crucial for data pipelines and analytics.

A single Job runs two notebooks as two separate tasks. A data engineer has noticed that one of the notebooks is running slowly in the Job’s current run. The data engineer asks a tech lead for help in identifying why this might be the case.
Which of the following approaches can the tech lead use to identify why the notebook is running slowly as part of the Job?

1. A. They can navigate to the Runs tab in the Jobs UI to immediately review the processing notebook.
2. B. They can navigate to the Tasks tab in the Jobs UI and click on the active run to review the processing notebook.
3. C. They can navigate to the Runs tab in the Jobs UI and click on the active run to review the processing notebook.
4. D. There is no way to determine why a Job task is running slowly.
5. E. They can navigate to the Tasks tab in the Jobs UI to immediately review the processing notebook.

Correct Answer: C
The job run details page contains job output and links to logs, including information about the success or failure of each task in the job run. You can access job run details from the Runs tab for the job. To view job run details from the Runs tab, click the link for the run in the Start time column in the runs list view. To return to the Runs tab for the job, click the Job ID value.
If the job contains multiple tasks, click a task to view task run details, including: the cluster that ran the task
the Spark UI for the task logs for the task
metrics for the task
https://docs.databricks.com/en/workflows/jobs/monitor-job-runs.html#job-run-details

Which of the following describes a scenario in which a data team will want to utilize cluster pools?

1. A. An automated report needs to be refreshed as quickly as possible.
2. B. An automated report needs to be made reproducible.
3. C. An automated report needs to be tested to identify errors.
4. D. An automated report needs to be version-controlled across multiple collaborators.
5. E. An automated report needs to be runnable by all stakeholders.

Correct Answer: A
Cluster pools are typically used in distributed computing environments, such as cloud-based data platforms like Databricks. They allow you to pre-allocate a set of compute resources (a cluster) for specific tasks or workloads. In this case, if an automated report needs to be refreshed as quickly as possible, you can allocate a cluster pool with sufficient resources to ensure fast data processing and report generation. This helps ensure that the report is generated with minimal latency and can be delivered to stakeholders in a timely manner. Cluster pools allow you to optimize resource allocation for high-demand, time-sensitive tasks like real-time report generation.

A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The code block used by the data engineer is below:

If the data engineer only wants the query to process all of the available data in as many batches as required, which of the following lines of code should the data engineer use to fill in the blank?

1. A. processingTime(1)
2. B. trigger(availableNow=True)
3. C. trigger(parallelBatch=True)
4. D. trigger(processingTime="once")
5. E. trigger(continuous="once")

Correct Answer: B
https://stackoverflow.com/questions/71061809/trigger-availablenow-for-delta- source-streaming-queries-in-pyspark-databricks

A data engineer is working with two tables. Each of these tables is displayed below in its entirety.

The data engineer runs the following query to join these tables together:

Which of the following will be returned by the above query?

1. A. Option A
2. B. Option B
3. C. Option C
4. D. Option D
5. E. Option E

Correct Answer: C