Free Nov-2025 UPDATED Databricks Databricks-Certified-Data-Engineer-Associate Exam Questions & Answer
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The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is designed to test an individual's knowledge and skills in using Databricks for data engineering tasks. Databricks Certified Data Engineer Associate Exam certification is highly recognized and respected in the industry and can be used as a benchmark for recruiters and employers to assess the candidate's proficiency in data engineering with Databricks.
The GAQM Databricks-Certified-Data-Engineer-Associate (Databricks Certified Data Engineer Associate) Certification Exam is a highly sought-after certification in the field of data engineering. Databricks Certified Data Engineer Associate Exam certification is designed to validate the skills and knowledge required to design, build, and maintain data pipelines and data solutions on the Databricks platform. Databricks-Certified-Data-Engineer-Associate exam is intended for professionals who have a strong understanding of data engineering concepts and have experience working with Databricks.
NEW QUESTION # 18
A data engineer needs to apply custom logic to identify employees with more than 5 years of experience in array column employees in table stores. The custom logic should create a new column exp_employees that is an array of all of the employees with more than 5 years of experience for each row. In order to apply this custom logic at scale, the data engineer wants to use the FILTER higher-order function.
Which of the following code blocks successfully completes this task?
- A. Option A
- B. Option C
- C. Option E
- D. Option B
- E. Option D
Answer: A
Explanation:
Option A is the correct answer because it uses the FILTER higher-order function correctly to filter out employees with more than 5 years of experience from the array column "employees". It applies a lambda function i -> i.years_exp > 5 that checks if the years of experience of each employee in the array is greater than 5. If this condition is met, the employee is included in the new array column "exp_employees".
NEW QUESTION # 19
A data engineer has a Job with multiple tasks that runs nightly. Each of the tasks runs slowly because the clusters take a long time to start.
Which of the following actions can the data engineer perform to improve the start up time for the clusters used for the Job?
- A. They can use jobs clusters instead of all-purpose clusters
- B. They can use clusters that are from a cluster pool
- C. They can configure the clusters to autoscale for larger data sizes
- D. They can use endpoints available in Databricks SQL
- E. They can configure the clusters to be single-node
Answer: A
NEW QUESTION # 20
A dataset has been defined using Delta Live Tables and includes an expectations clause:
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION FAIL UPDATE What is the expected behavior when a batch of data containing data that violates these constraints is processed?
- A. Records that violate the expectation cause the job to fail.
- B. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
- C. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.
- D. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.
- E. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
Answer: A
Explanation:
Explanation
https://docs.databricks.com/en/delta-live-tables/expectations.html
Action
Result
warn (default)
Invalid records are written to the target; failure is reported as a metric for the dataset.
drop
Invalid records are dropped before data is written to the target; failure is reported as a metrics for the dataset.
fail
Invalid records prevent the update from succeeding. Manual intervention is required before re-processing.
NEW QUESTION # 21
Which of the following Git operations must be performed outside of Databricks Repos?
- A. Merge
- B. Push
- C. Pull
- D. Commit
- E. Clone
Answer: E
NEW QUESTION # 22
Which of the following tools is used by Auto Loader process data incrementally?
- A. Spark Structured Streaming
- B. Databricks SQL
- C. Data Explorer
- D. Unity Catalog
- E. Checkpointing
Answer: A
Explanation:
Auto Loader provides a Structured Streaming source called cloudFiles that can process new data files as they arrive in cloud storage without any additional setup. Auto Loader uses a scalable key-value store to track ingestion progress and ensure exactly-once semantics. Auto Loader can ingest various file formats and load them into Delta Lake tables. Auto Loader is recommended for incremental data ingestion with Delta Live Tables, which extends the functionality of Structured Streaming and allows you to write declarative Python or SQL code to deploy a production-quality data pipeline. Reference: What is Auto Loader?, What is Auto Loader? | Databricks on AWS, Solved: How does Auto Loader ingest data? - Databricks - 5629
NEW QUESTION # 23
In which of the following scenarios should a data engineer use the MERGE INTO command instead of the INSERT INTO command?
- A. When the source is not a Delta table
- B. When the source table can be deleted
- C. When the target table cannot contain duplicate records
- D. When the target table is an external table
- E. When the location of the data needs to be changed
Answer: C
Explanation:
Explanation
With merge , you can avoid inserting the duplicate records. The dataset containing the new logs needs to be deduplicated within itself. By the SQL semantics of merge, it matches and deduplicates the new data with the existing data in the table, but if there is duplicate data within the new dataset, it is inserted.https://docs.databricks.com/en/delta/merge.html#:~:text=With%20merge%20%2C%20you%20can%20a
NEW QUESTION # 24
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 cade block used by the data engineer is below:
If the data engineer only wants the query to execute a micro-batch to process data every 5 seconds, which of the following lines of code should the data engineer use to fill in the blank?
- A. trigger(continuous="5 seconds")
- B. trigger()
- C. trigger(processingTime="5 seconds")
- D. trigger("5 seconds")
- E. trigger(once="5 seconds")
Answer: C
NEW QUESTION # 25
A data engineer has a Job that has a complex run schedule, and they want to transfer that schedule to other Jobs.
Rather than manually selecting each value in the scheduling form in Databricks, which of the following tools can the data engineer use to represent and submit the schedule programmatically?
- A. There is no way to represent and submit this information programmatically
- B. datetime
- C. pyspark.sql.types.TimestampType
- D. pyspark.sql.types.DateType
- E. Cron syntax
Answer: E
NEW QUESTION # 26
A data engineer wants to create a data entity from a couple of tables. The data entity must be used by other data engineers in other sessions. It also must be saved to a physical location.
Which of the following data entities should the data engineer create?
- A. Temporary view
- B. Database
- C. View
- D. Function
- E. Table
Answer: E
Explanation:
1: A table is a data entity that is stored in a physical location and can be accessed by other data engineers in other sessions. A table can be created from one or more tables using the CREATE TABLE or CREATE TABLE AS SELECT commands. A table can also be registered from an existing DataFrame using the spark.catalog.createTable method. A table can be queried using SQL or DataFrame APIs. A table can also be updated, deleted, or appended using the MERGE INTO command or the DeltaTable API. Reference:
Create a table
Create a table from a query result
Register a table from a DataFrame
[Query a table]
[Update, delete, or merge into a table]
NEW QUESTION # 27
A data engineering team has noticed that their Databricks SQL queries are running too slowly when they are submitted to a non-running SQL endpoint. The data engineering team wants this issue to be resolved.
Which of the following approaches can the team use to reduce the time it takes to return results in this scenario?
- A. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to
"Reliability Optimized." - B. They can increase the maximum bound of the SQL endpoint's scaling range
- C. They can turn on the Auto Stop feature for the SQL endpoint.
- D. They can increase the cluster size of the SQL endpoint.
- E. They can turn on the Serverless feature for the SQL endpoint.
Answer: E
Explanation:
Option D is the correct answer because it enables the Serverless feature for the SQL endpoint, which allows the endpoint to automatically scale up and down based on the query load. This way, the endpoint can handle more concurrent queries and reduce the time it takes to return results. The Serverless feature also reduces the cold start time of the endpoint, which is the time it takes to start the cluster when a query is submitted to a non-running endpoint. The Serverless feature is available for both AWS and Azure Databricks platforms.
References: Databricks SQL Serverless, Serverless SQL endpoints, New Performance Improvements in Databricks SQL
NEW QUESTION # 28
A data engineer has realized that they made a mistake when making a daily update to a table. They need to use Delta time travel to restore the table to a version that is 3 days old. However, when the data engineer attempts to time travel to the older version, they are unable to restore the data because the data files have been deleted.
Which of the following explains why the data files are no longer present?
- A. The HISTORY command was run on the table
- B. The VACUUM command was run on the table
- C. The DELETE HISTORY command was run on the table
- D. The TIME TRAVEL command was run on the table
- E. The OPTIMIZE command was nun on the table
Answer: B
Explanation:
The VACUUM command is used to remove files that are no longer referenced by a Delta table and are older than the retention threshold1. The default retention period is 7 days2, but it can be changed by setting the delta.logRetentionDuration and delta.deletedFileRetentionDuration configurations3. If the VACUUM command was run on the table with a retention period shorter than 3 days, then the data files that were needed to restore the table to a 3-day-old version would have been deleted. The other commands do not delete data files from the table. The TIME TRAVEL command is used to query a historical version of the table4. The DELETE HISTORY command is not a valid command in Delta Lake. The OPTIMIZE command is used to improve the performance of the table by compacting small files into larger ones5. The HISTORY command is used to retrieve information about the operations performed on the table. Reference: 1: VACUUM | Databricks on AWS 2: Work with Delta Lake table history | Databricks on AWS 3: [Delta Lake configuration | Databricks on AWS] 4: Work with Delta Lake table history - Azure Databricks 5: [OPTIMIZE | Databricks on AWS] : [HISTORY | Databricks on AWS]
NEW QUESTION # 29
A new data engineering team team. has been assigned to an ELT project. The new data engineering team will need full privileges on the database customers to fully manage the project.
Which of the following commands can be used to grant full permissions on the database to the new data engineering team?
- A. GRANT ALL PRIVILEGES ON DATABASE team TO customers;
- B. GRANT SELECT CREATE MODIFY USAGE PRIVILEGES ON DATABASE customers TO team;
- C. GRANT USAGE ON DATABASE customers TO team;
- D. GRANT SELECT PRIVILEGES ON DATABASE customers TO teams;
- E. GRANT ALL PRIVILEGES ON DATABASE customers TO team;
Answer: E
Explanation:
To grant full permissions on a database to a user, group, or service principal, the GRANT ALL PRIVILEGES ON DATABASE command can be used. This command grants all the applicable privileges on the database, such as CREATE, SELECT, MODIFY, and USAGE. The other options are either incorrect or incomplete, as they do not grant all the privileges or specify the wrong database or principal. References:
* GRANT
* Privileges
NEW QUESTION # 30
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to an ELT job. The ELT job has its Databricks SQL query that returns the number of input records containing unexpected NULL values. The data engineer wants their entire team to be notified via a messaging webhook whenever this value reaches 100.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of NULL values reaches 100?
- A. They can set up an Alert with one-time notifications.
- B. They can set up an Alert with a new email alert destination.
- C. They can set up an Alert with a new webhook alert destination.
- D. They can set up an Alert with a custom template.
- E. They can set up an Alert without notifications.
Answer: C
Explanation:
A webhook alert destination is a way to send notifications to external applications or services via HTTP requests. A data engineer can use a webhook alert destination to notify their entire team via a messaging webhook, such as Slack or Microsoft Teams, whenever the number of NULL values in the input data reaches
100. To set up a webhook alert destination, the data engineer needs to do the following steps:
* In the Databricks SQL workspace, navigate to the Settings gear icon and select SQL Admin Console.
* Click Alert Destinations and click Add New Alert Destination.
* Select Webhook and enter the webhook URL and the optional custom template for the notification message.
* Click Create to save the webhook alert destination.
* In the Databricks SQL editor, create or open the query that returns the number of input records containing unexpected NULL values.
* Click the Create Alert icon above the editor window and configure the alert criteria, such as the value column, the condition, and the threshold.
* In the Notification section, select the webhook alert destination that was created earlier and click Create Alert. References: What are Databricks SQL alerts?, Monitor alerts, Monitoring Your Business with Alerts, Using Automation Runbook Webhooks To Alert on Databricks Status Updates.
NEW QUESTION # 31
Which of the following describes when to use the CREATE STREAMING LIVE TABLE (formerly CREATE INCREMENTAL LIVE TABLE) syntax over the CREATE LIVE TABLE syntax when creating Delta Live Tables (DLT) tables using SQL?
- A. CREATE STREAMING LIVE TABLE should be used when the subsequent step in the DLT pipeline is static.
- B. CREATE STREAMING LIVE TABLE should be used when the previous step in the DLT pipeline is static.
- C. CREATE STREAMING LIVE TABLE should be used when data needs to be processed through complicated aggregations.
- D. CREATE STREAMING LIVE TABLE is redundant for DLT and it does not need to be used.
- E. CREATE STREAMING LIVE TABLE should be used when data needs to be processed incrementally.
Answer: E
Explanation:
A streaming live table or view processes data that has been added only since the last pipeline update.
Streaming tables and views are stateful; if the defining query changes, new data will be processed based on the new query and existing data is not recomputed. This is useful when data needs to be processed incrementally, such as when ingesting streaming data sources or performing incremental loads from batch data sources. A live table or view, on the other hand, may be entirely computed when possible to optimize computation resources and time. This is suitable when data needs to be processed in full, such as when performing complex transformations or aggregations that require scanning all the data. References: Difference between LIVE TABLE and STREAMING LIVE TABLE, CREATE STREAMING TABLE, Load data using streaming tables in Databricks SQL.
NEW QUESTION # 32
A data engineer has a Job with multiple tasks that runs nightly. Each of the tasks runs slowly because the clusters take a long time to start.
Which of the following actions can the data engineer perform to improve the start up time for the clusters used for the Job?
- A. They can use jobs clusters instead of all-purpose clusters
- B. They can use clusters that are from a cluster pool
- C. They can configure the clusters to autoscale for larger data sizes
- D. They can use endpoints available in Databricks SQL
- E. They can configure the clusters to be single-node
Answer: B
Explanation:
The best action that the data engineer can perform to improve the start up time for the clusters used for the Job is to use clusters that are from a cluster pool. A cluster pool is a set of idle clusters that can be used by jobs or interactive sessions. By using a cluster pool, the data engineer can avoid the cluster creation time and reduce the latency of the tasks. Cluster pools also offer cost savings and resource efficiency, as they can be shared by multiple users and jobs.
Option A is not relevant, as endpoints available in Databricks SQL are used for creating and managing SQL analytics workloads, not for improving cluster start up time.
Option B is not correct, as jobs clusters and all-purpose clusters have similar start up times. Jobs clusters are clusters that are dedicated to run a single job and are terminated when the job is completed. All-purpose clusters are clusters that can be used for multiple purposes, such as interactive sessions, notebooks, or multiple jobs. Both types of clusters can benefit from using a cluster pool.
Option C is not advisable, as configuring the clusters to be single-node will reduce the parallelism and performance of the tasks. Single-node clusters are clusters that have only one worker node and are typically used for testing or development purposes. They are not suitable for running production jobs that require high scalability and fault tolerance.
Option E is not helpful, as configuring the clusters to autoscale for larger data sizes will not affect the start up time of the clusters. Autoscaling is a feature that allows clusters to dynamically adjust the number of worker nodes based on the workload. It can help optimize the resource utilization and cost efficiency of the clusters, but it does not speed up the cluster creation process.
References:
* Cluster Pools
* Jobs
* Clusters
* [Databricks Data Engineer Professional Exam Guide]
NEW QUESTION # 33
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to a data analytics dashboard for a retail use case. The job has a Databricks SQL query that returns the number of store-level records where sales is equal to zero. The data engineer wants their entire team to be notified via a messaging webhook whenever this value is greater than 0.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of stores with $0 in sales is greater than zero?
- A. They can set up an Alert with one-time notifications.
- B. They can set up an Alert with a new email alert destination.
- C. They can set up an Alert with a new webhook alert destination.
- D. They can set up an Alert with a custom template.
- E. They can set up an Alert without notifications.
Answer: C
Explanation:
A webhook alert destination is a notification destination that allows Databricks to send HTTP POST requests to a third-party endpoint when an alert is triggered. This enables the data engineer to integrate Databricks alerts with their preferred messaging or collaboration platform, such as Slack, Microsoft Teams, or PagerDuty. To set up a webhook alert destination, the data engineer needs to create and configure a webhook connector in their messaging platform, and then add the webhook URL to the Databricks notification destination. After that, the data engineer can create an alert for their Databricks SQL query, and select the webhook alert destination as the notification destination. The alert can be configured with a custom condition, such as when the number of stores with $0 in sales is greater than zero, and a custom message template, such as "Alert: {number_of_stores} stores have $0 in sales". The alert can also be configured with a recurrence interval, such as every hour, to check the query result periodically. When the alert condition is met, the data engineer and their team will receive a notification via the messaging webhook, with the custom message and a link to the Databricks SQL query. The other options are either not suitable for sending notifications via a messaging webhook (A, B, E), or not suitable for sending recurring notifications . Reference: Databricks Documentation - Manage notification destinations, Databricks Documentation - Create alerts for Databricks SQL queries, Databricks Documentation - Configure alert conditions and messages.
NEW QUESTION # 34
In which of the following file formats is data from Delta Lake tables primarily stored?
- A. CSV
- B. Delta
- C. JSON
- D. A proprietary, optimized format specific to Databricks
- E. Parquet
Answer: E
Explanation:
Delta Lake is an open source project that provides ACID transactions, time travel, and other features on top of Apache Parquet, a columnar file format that is widely used for big data analytics. Delta Lake uses versioned Parquet files to store your data in your cloud storage, along with JSON files as transaction logs and checkpoint files to track the changes and ensure data integrity. Delta Lake is compatible with any Apache Hive compatible file format, such as CSV, JSON, or AVRO, but it primarily stores data as Parquet files for better performance and compression. References: How to Create Delta Lake tables, 5 reasons to choose Delta Lake format (on Databricks), Parquet vs Delta format in Azure Data Lake Gen 2 store, What is Delta Lake? - Azure Databricks, Lakehouse and Delta tables - Microsoft Fabric
NEW QUESTION # 35
A data engineer has a Python variable table_name that they would like to use in a SQL query. They want to construct a Python code block that will run the query using table_name.
They have the following incomplete code block:
____(f"SELECT customer_id, spend FROM {table_name}")
Which of the following can be used to fill in the blank to successfully complete the task?
- A. dbutils.sql
- B. spark.table
- C. spark.sql
- D. spark.delta.table
- E. spark.delta.sql
Answer: C
Explanation:
The spark.sql method can be used to execute SQL queries programmatically and return the result as a DataFrame. The spark.sql method accepts a string argument that contains a valid SQL statement. The data engineer can use a formatted string literal (f-string) to insert the Python variable table_name into the SQL query. The other methods are either invalid or not suitable for running SQL queries. Reference: Running SQL Queries Programmatically, Formatted string literals, spark.sql
NEW QUESTION # 36
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The GAQM Databricks-Certified-Data-Engineer-Associate exam is a certification exam that tests the ability of individuals to design and implement data-driven solutions using Databricks. Databricks-Certified-Data-Engineer-Associate exam is designed for data engineers who want to demonstrate their proficiency in working with data and analytics technologies. Databricks-Certified-Data-Engineer-Associate exam covers a range of topics, including data engineering, data modeling, data processing, and data analysis.
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