What are the prerequisites for taking the Databricks Certified Data Engineer Associate exam?

There are no formal prerequisites, but Databricks recommends having practical experience with Apache Spark, Delta Lake, and the Databricks platform. You should be comfortable with SQL, Python, or Scala and have hands-on experience with data engineering tasks.

How long is the Databricks Certified Data Engineer Associate exam?

The exam is 120 minutes (2 hours) in duration. You will need to complete all questions within this timeframe to receive your results.

What topics are covered in the Data Engineer Associate certification exam?

The exam covers Databricks workspace navigation, Delta Lake, Apache Spark SQL, data ingestion, data transformation, data quality, and job automation. It also includes topics on security, performance optimization, and using Databricks tools for end-to-end data engineering workflows.

How many questions are on the Databricks Certified Data Engineer Associate exam?

The exam typically contains 45 questions in multiple-choice and multiple-select formats. The exact number may vary, and questions are designed to assess both theoretical knowledge and practical application of data engineering concepts.

What is the passing score for the Data Engineer Associate exam?

The passing score is typically 70% or higher, though Databricks uses a scaled scoring system that may adjust based on question difficulty. You will receive your pass/fail result immediately after completing the exam.

Free Databricks-Certified-Data-Engineer-Associate Exam Questions

Q1 MultipleChoice

A data engineer wants to schedule their Databricks SQL dashboard to refresh once per day, but they only want the associated SQL endpoint to be running when it is necessary.

Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

AThey can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.

BThey can set up the dashboard's SQL endpoint to be serverless.

CThey can turn on the Auto Stop feature for the SQL endpoint.

DThey can reduce the cluster size of the SQL endpoint.

EThey can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.

Correct Answer: B

Explanation:

A serverless SQL endpoint is a compute resource that is automatically managed by Databricks and scales up or down based on the workload. A serverless SQL endpoint can be used to run queries and dashboards without requiring manual configuration or management. A serverless SQL endpoint is only active when it is needed and shuts down automatically when idle, minimizing the total running time and cost. A serverless SQL endpoint can be created and assigned to a dashboard using the Databricks SQL UI or the SQL Analytics API.Reference:

Create a serverless SQL endpoint

Assign a SQL endpoint to a dashboard

SQL Analytics API

Q2 MultipleChoice

A data engineer is maintaining a data pipeline. Upon data ingestion, the data engineer notices that the source data is starting to have a lower level of quality. The data engineer would like to automate the process of monitoring the quality level.

Which of the following tools can the data engineer use to solve this problem?

Correct Answer: D

Explanation:

Delta Live Tables is a tool that enables data engineers to build and manage reliable data pipelines with minimal code. One of the features of Delta Live Tables isdata quality monitoring, which allows data engineers to define quality expectations for their data and automatically check them at every step of the pipeline. Data quality monitoring can help detect and resolve data quality issues, such as missing values, duplicates, outliers, or schema changes. Data quality monitoring can also generate alerts and reports on the quality level of the data, and enable data engineers to troubleshoot and fix problems quickly.Reference:Delta Live Tables Overview,Data Quality Monitoring

Q3 MultipleChoice

A data engineer is working on a personal laptop and needs to perform complex transformations on data stored in a Delta Lake on cloud storage. The engineer decides to use Databricks Connect to interact with Databricks clusters and work in their local IDE.

How does Databricks Connect enable the engineer to develop, test, and debug code seamlessly on their local machine while interacting with Databricks clusters?

ABy allowing direct execution of Spark jobs from the local machine without needing a network connection

BBy providing a local environment that mimics the Databricks runtime, enabling the engineer to develop, test, and debug code using a specific IDE that is required by Databricks

CBy providing a local environment that mimics the Databricks runtime, enabling the engineer to develop, test, and debug code using their preferred ide

DBy providing a local environment that mimics the Databricks runtime, enabling the engineer to develop, test, and debug code only through Databricks' own web interface

Correct Answer: C

Q4 MultipleChoice

A data engineering team needs to ingest historical CSV files from a cloud-storage location that already contains 50,000 existing files. The team also expects new files to arrive continuously. The team wants to use Auto Loader to incrementally process both the existing files and new arrivals efficiently.

Which Auto Loader mode should the team configure for this use case?

AUse file notification mode because it scales efficiently for both large numbers of existing files and continuous new arrivals by using cloud-storage events rather than repeatedly listing directories.

BUse directory listing mode because it provides better performance for the initial processing of 50,000 existing files through optimized incremental listing capabilities.

CUse directory listing mode initially to process the 50,000 existing files, and then reconfigure the stream to use file notification mode for ongoing ingestion of new arrivals.

DUse file notification mode only after all existing files have been processed because it cannot efficiently handle large volumes of pre-existing files.

Correct Answer: A

Q5 MultipleChoice

Which type of workloads are compatible with Auto Loader?

AStreaming workloads

BMachine learning workloads

CServerless workloads

DBatch workloads

Correct Answer: A

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