Databricks Certified Data Engineer Associate Exam (Databricks-Certified-Data-Engineer-Associate) Free Practice Test

Question 1

A data analysis team has noticed that their Databricks SQL queries are running too slowly when connected to their always-on SQL endpoint. They claim that this issue is present when many members of the team are running small queries simultaneously. They ask the data engineering team for help. The data engineering team notices that each of the team's queries uses the same SQL endpoint.
Which of the following approaches can the data engineering team use to improve the latency of the team's queries?

A. They can turn on the Serverless feature for the SQL endpoint.

B. They can turn on the Auto Stop feature for the SQL endpoint.

C. They can increase the maximum bound of the SQL endpoint's scaling range.

D. They can increase the cluster size of the SQL endpoint.

E. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to "Reliability Optimized."

Correct Answer: C

Question 2

A data engineer is inspecting an ETL pipeline based on a Pyspark job that consistently encounters performance bottlenecks. Based on developer feedback, the data engineer assumes the job is low on compute resources. To pinpoint the issue, the data engineer observes the Spark UI and finds out the job has a high CPU time vs Task time. Which course of action should the data engineer take?

A. High CPU time vs Task time means efficient use of cluster and no change needed

B. High CPU time vs Task time means over-utilized memory and the need to increase parallelism

C. High CPU time vs Task time means a CPU over-utilized job. The data engineer may need to consider executor and core tuning or resizing the cluster

D. High CPU time vs Task time means an under-utilized cluster. The data engineer may need to repartition data to spread the jobs more evenly throughout the cluster.

Correct Answer: C

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Question 3

A Data Engineer is building a simple data pipeline using Delta Live Tables (DLT) in Databricks to ingest customer data. The raw customer data is stored in a cloud storage location in JSON format. The task is to create a DLT pipeline that reads the raw JSON data and writes it into a Delta table for further processing. Which code snippet will correctly ingest the raw JSON data and create a Delta table using DLT?

A.

B.

C.

D.

Correct Answer: B

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Question 4

A data engineer is working on a Databricks project that utilizes cloud storage. The data engineer wants to load several JSON files from containers on a storage account as soon as the file arrives within the storage account. Which syntax should the data engineer follow to first load the files into a dataframe and check that it is working as expected using Python?

A. df = spark.readStream.format("cloud").option("json").load("/input/path")

B. df = spark.read.json("input/path")

C. df = spark.readStream.format("json".load("input/path")

D. df = spark.readStream.format("cloudFiles").option("cloudFiles.format", "json").load("/input/path")

Correct Answer: D

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Question 5

Differentiate between all-purpose clusters and jobs clusters.
A data engineering team has created a python notebook to load data from cloud storage, this job has been tested and now needs to be scheduled in production.
Which would be the best cluster to be used in this case?

A. Jobs Cluster

B. Serverless SQL warehouse

C. All purpose cluster

D. Any Unity Catalog-enabled cluster

Correct Answer: A

Question 6

An organization plans to share a large dataset stored in a Databricks workspace on AWS with a partner organization whose Databricks workspace is hosted on Azure. The data engineer wants to minimize data transfer costs while ensuring secure and efficient data sharing. Which strategy will reduce data egress costs associated with cross-cloud data sharing?

A. Sharing data via pre-signed URLs without monitoring egress costs

B. Using Delta Sharing without any additional configurations

C. Migrating the dataset to Cloudflare R2 object storage before sharing

D. Configure VPN connection between AWS and Azure for faster data sharing

Correct Answer: B

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Question 7

A Databricks single-task workflow fails at the last task due to an error in a notebook. The data engineer fixes the mistake in the notebook. What should the data engineer do to rerun the workflow?

A. Repair the task

B. Restart the cluster

C. Rerun the pipeline

D. Switch the cluster

Correct Answer: A

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Question 8

A data engineer is debugging a Python notebook in Databricks that processes a dataset using PySpark. The notebook fails with an error during a DataFrame transformation. The engineer wants to inspect the state of variables, such as the input DataFrame and intermediate results, to identify where the error occurs. Which tool should the engineer use to debug the notebook and inspect the values of variables like DataFrames?

A. Use the Spark UI to analyze the execution plan and identify stages where the job failed

B. Use the Databricks CLI to download and analyze driver logs for detailed error messages

C. Use the Python Notebook Interactive Debugger to set breakpoints and inspect variable values in real-time

D. Use the Ganglia UI to monitor cluster resource usage and identify hardware issues

Correct Answer: C

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Question 9

Which of the following describes the relationship between Gold tables and Silver tables?

A. Gold tables are more likely to contain aggregations than Silver tables.

B. Gold tables are more likely to contain a less refined view of data than Silver tables.

C. Gold tables are more likely to contain truthful data than Silver tables.

D. Gold tables are more likely to contain valuable data than Silver tables.

E. Gold tables are more likely to contain more data than Silver tables.

Correct Answer: A

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