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Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:

Section	Objectives
Databricks Lakehouse Platform Fundamentals	- Workspace, architecture, and core platform concepts - Clusters, notebooks, and basic Databricks environment usage
Data Governance and Quality	- Unity Catalog basics - Data quality concepts and management - Data access control and governance
Data Processing and Transformations	- Apache Spark SQL operations (joins, aggregations, filtering) - PySpark DataFrame transformations - Delta Lake fundamentals (tables, transactions, optimization) - User-defined functions (UDFs)
Data Ingestion and ELT Development	- Data ingestion using Spark SQL and PySpark - ETL patterns and transformations - Handling structured and semi-structured data
Productionizing Data Pipelines	- Databricks Workflows / Jobs orchestration - Pipeline deployment and operationalization - Scheduling and monitoring jobs

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q179-Q184):

NEW QUESTION # 179
Which of the following commands will return the location of database customer360?

A. ALTER DATABASE customer360 SET DBPROPERTIES ('location' = '/user'};
B. DROP DATABASE customer360;
C. DESCRIBE DATABASE customer360;
D. DESCRIBE LOCATION customer360;
E. USE DATABASE customer360;

Answer: C

NEW QUESTION # 180
A data engineer works for an organization that must meet a stringent Service Level Agreement (SLA) that demands minimal runtime errors and high availability for its data processing pipelines. The data engineer wants to avoid the operational overhead of managing and tuning clusters.
Which architectural solution will meet the requirements?

A. Deploy a dedicated, manually managed cluster optimized by in-house IT staff.
B. Utilize Databricks serverless compute that automatically optimizes resources and abstracts cluster management.
C. Implement a hybrid approach with scheduled batch jobs on custom cloud VMs.
D. Use an auto-scaling cluster configured and monitored by the user.

Answer: B

NEW QUESTION # 181
A data engineering team uses Declarative Automation Bundles (formerly Databricks Asset Bundles) to deploy the same codebase across dev, test, and prod environments. The team wants environment-specific behavior to be applied only through bundle configuration, without modifying notebooks, job definitions, or deployment logic during promotion. Which approach applies environment-specific configuration while meeting this requirement?

A. Define bundle variables and reference them from bundle resources, then override variable values per environment using bundle targets.
B. Parameterize environment-specific values inside notebooks and set those parameters when each job run is triggered.
C. Deploy the same bundle once and edit job settings in the workspace UI for each environment after deployment.
D. Use separate Git branches per environment so each branch can contain environment-specific configuration values.

Answer: A

Explanation:
Bundle variables hold reusable configuration values, and bundle targets override those values for dev, test, and prod at deployment time. This preserves one codebase and avoids modifying notebooks or workspace resources during promotion.

NEW QUESTION # 182
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 SELECT PRIVILEGES ON DATABASE customers TO teams;
B. GRANT USAGE ON DATABASE customers TO team;
C. GRANT ALL PRIVILEGES ON DATABASE customers TO team;
D. GRANT ALL PRIVILEGES ON DATABASE team TO customers;
E. GRANT SELECT CREATE MODIFY USAGE PRIVILEGES ON DATABASE customers TO team;

Answer: C

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 # 183
A company has a strict 15-minute Service Level Agreement (SLA) for updating its currency exchange dashboard. The source data arrives in small increments every few minutes. The data engineering team needs a triggering strategy for their Lakeflow Job that keeps end-to-end latency within the SLA while minimizing compute cost and DBU consumption. Which triggering strategy for the Lakeflow Job is the recommended way to meet this 15-minute SLA?

A. Use a Continuous trigger to ensure data is processed as soon as it arrives.
B. Use a Scheduled trigger set to run every 12 minutes, with the streaming task configured to use Trigger.AvailableNow.
C. Use a File Arrival trigger configured to fire every time a single JSON file lands in the bucket.
D. Use a Scheduled trigger set to run every 1 minute to ensure the SLA is never at risk.

Answer: B

Explanation:
A scheduled Trigger.AvailableNow workload is the most cost-efficient pattern when minute-level latency is acceptable. Running every 12 minutes provides a buffer within the 15-minute SLA while allowing compute to stop after processing all available data. Continuous or per-file execution would consume more compute.

NEW QUESTION # 184
......

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