Databricks Certified Data Engineer Associate Exam Questions
Certified Data Engineer Associate (Page 5 )

Updated On: 23-Apr-2026

What is the maximum output supported by a job cluster to ensure a notebook does not fail?

  1. 25MBs
  2. 10MBs
  3. 30MBs
  4. 15MBs

Answer(s): B

Explanation:

The maximum output supported by a job cluster in Databricks is 10MB. If the output exceeds this limit, the notebook may fail.



A data engineer needs to conduct Exploratory Analysis on data residing in a database that is within the company's custom-defined network in the cloud. The data engineer is using SQL for this task.

Which type of SQL Warehouse will enable the data engineer to process large numbers of queries quickly and cost-effectively?

  1. Serverless compute for notebooks
  2. Pro SQL Warehouse
  3. Classic SQL Warehouse
  4. Serverless SQL Warehouse

Answer(s): B

Explanation:

A Pro SQL Warehouse is designed for high-performance, cost-effective query execution at scale. It is optimized for running large volumes of queries quickly, making it ideal for exploratory analysis on enterprise datasets.



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?

  1. Use the Databricks CLI to download and analyze driver logs for detailed error messages
  2. Use the Python Notebook Interactive Debugger to set breakpoints and inspect variable values in real-time
  3. Use the Ganglia UI to monitor cluster resource usage and identify hardware issues
  4. Use the Spark UI to analyze the execution plan and identify stages where the job failed

Answer(s): B

Explanation:

The Python Notebook Interactive Debugger in Databricks allows setting breakpoints and inspecting variable values, including DataFrames, in real time. This makes it the correct tool for debugging transformation errors in a PySpark notebook.



A data engineer wants to create an external table in Databricks that references data stored in an Azure Data Lake Storage (ADLS) location. The goal is to enable Databricks to access and query this external data without moving it into the Databricks-managed storage.

Which step should the data engineer take to successfully create the external table?

  1. Use the CREATE MANAGED TABLE statement and specify the LOCATION clause with the path to the external data.
  2. CREATE UNMANAGED TABLE statement without specifying a LOCATION clause.
  3. Use the CREATE TABLE statement and specify the LOCATION clause with the path to the external data.
  4. CREATE EXTERNAL TABLE statement without specifying a LOCATION clause.

Answer(s): C

Explanation:

To reference data stored outside of Databricks-managed storage, the engineer should use CREATE TABLE ...
LOCATION 'path', which creates an unmanaged (external) table pointing to the ADLS data without moving it into Databricks storage.



A data engineer is developing a small proof of concept in a notebook. When running the entire notebook, the Cluster usage spikes. The data engineer wants to keep the development requirements and get real-time results.

Which Cluster meets these requirements?

  1. All Purpose Cluster with autoscaling
  2. Job Cluster with Photon enabled and autoscaling
  3. Job Cluster with autoscaling enabled
  4. All-Purpose Cluster with a large fixed memory size

Answer(s): A

Explanation:

An All-Purpose Cluster with autoscaling is best for interactive development and proof of concept work in notebooks, since it provides real-time results and can dynamically scale resources as usage spikes.



A data engineer needs to process SQL queries on a large dataset with fluctuating workloads. The workload requires automatic scaling based on the volume of queries, without the need to manage or provision infrastructure. The solution should be cost-efficient and charge only for the compute resources used during query execution.

Which compute option should the data engineer use?

  1. Databricks SQL Analytics
  2. Databricks Runtime for ML
  3. Databricks Jobs
  4. Serverless SQL Warehouse

Answer(s): D

Explanation:

A Serverless SQL Warehouse automatically scales to handle fluctuating workloads, requires no infrastructure management, and charges only for the compute used during query execution, making it cost-efficient for large datasets.



An organization has implemented a data pipeline in Databricks and needs to ensure it can scale automatically based on varying workloads without manual cluster management. The goal is to meet the company's Service Level Agreements (SLAs), which require high availability and minimal downtime, while Databricks automatically handles resource allocation and optimization.

Which approach fulfills these requirements?

  1. Deploy Job Clusters with fixed configurations, dedicated to specific tasks, without automatic scaling.
  2. Use Spot Instances to allocate resources dynamically while minimizing costs, with potential interruptions.
  3. Use Interactive Clusters in Databricks, adjusting cluster sizes manually based on workload demands.
  4. Use Serverless compute in Databricks to automatically scale and provision resources with minimal manual intervention.

Answer(s): D

Explanation:

Serverless compute in Databricks automatically provisions and scales resources to meet workload demands, ensuring high availability and minimal downtime while reducing the need for manual cluster management, which aligns with SLA requirements.



A data engineer has written a function in a Databricks Notebook to calculate the population of bacteria in a given medium.



Analysts use this function in the notebook and sometimes provide input arguments of the wrong data type, which can cause errors during execution.

Which Databricks feature will help the data engineer quickly identify if an incorrect data type has been provided as input?

  1. The Databricks debugger enables breakpoints that will raise an error if the wrong data type is submitted.
  2. The Databricks debugger enables the use of a variable explorer to see at a glance the value of the variables.

Answer(s): B

Explanation:

The Databricks debugger supports setting breakpoints that pause execution and allow inspection of variables. If the wrong data type is passed to the function, the debugger raises an error at runtime, helping the engineer quickly identify the issue.



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