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

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

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q295-Q300):

NEW QUESTION # 295
In a Lakeflow Declarative Pipeline (Delta Live Tables), a data engineer must ensure that records with a null order_id are discarded from the silver table while the pipeline continues to run.
Which expectation clause should be used?

A. CONSTRAINT valid_id EXPECT (order_id IS NOT NULL) ON VIOLATION DROP ROW
B. CONSTRAINT valid_id EXPECT (order_id IS NOT NULL)
C. CONSTRAINT valid_id EXPECT (order_id IS NOT NULL) ON VIOLATION FAIL UPDATE
D. ALTER TABLE silver_orders ADD CONSTRAINT valid_id CHECK (order_id IS NOT NULL)

Answer: A

Explanation:
ON VIOLATION DROP ROW removes failing records from the target dataset and records the violation in the event log while the pipeline continues. An expectation with no ON VIOLATION clause retains bad records and only reports metrics, and FAIL UPDATE stops the pipeline.

NEW QUESTION # 296
A platform team is creating a standardized template for Databricks Asset Bundles to support CI/CD. The template must specify defaults for artifacts, workspace root paths, and a run identity, while allowing a "dev" target to be the default and override specific paths.
How should the team use databricks.yml to satisfy these requirements?

A. Use project, packages, environment, identity, and stages; set dev as default stage and override workspace under environment.
B. Use deployment, builds, context, identity, and environments; set dev as default environment and override paths under builds.
C. Use roots, modules, profiles, actor, and targets; where profiles contain workspace and artifacts defaults and actor sets run identity.
D. Use bundle, artifacts, workspace, run_as, and targets at the top level; set one target with default: true and override workspace paths or artifacts under that target.

Answer: D

Explanation:
In Databricks Asset Bundles, the databricks.yml file defines all top-level configuration keys, including bundle, artifacts, workspace, run_as, and targets. The targets section defines specific deployment contexts (for example, dev, test, prod). Setting default: true for a target marks it as the default environment. Overrides for workspace paths and artifact configurations can be defined inside each target while keeping defaults at the top level.
Reference Source: Databricks Asset Bundle Configuration Guide - "Structure of databricks.yml and target overrides."

NEW QUESTION # 297
A data engineer is troubleshooting two different pipeline failures:
- Pipeline A fails with a java.lang.OutOfMemoryError is immediately
thrown after the command display(df.collect()) is called on a 100GB
dataset.
- Pipeline B fails during a wide transformation (a join of two large
tables) with an ExecutorLostFailure error message, indicating executor
memory exhaustion during shuffle.
Which action should the data engineer take to fix these two issues?

A. For Pipeline A, switch to a Storage Optimized cluster; for Pipeline B, use a Compute Optimized cluster.
B. For Pipeline A, increase the number of executors; for Pipeline B, increase the driver memory.
C. For Pipeline A, increase the spark.driver.memory; for Pipeline B, increase the spark.sql.shuffle.partitions or executor memory.
D. For both pipelines, enable Adaptive Query Execution (AQE) as it automatically manages memory allocation for both the driver and executors.

Answer: C

Explanation:
collect() moves the entire 100GB dataset to the driver, causing driver memory exhaustion, so Pipeline A requires avoiding collect() or increasing driver memory. Pipeline B fails on executors during a shuffle, so increasing shuffle partitions to reduce partition size or adding executor memory addresses the issue.

NEW QUESTION # 298
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

A. Replace "transactions" with the path to the location of the Delta table
B. Replace format("delta") with format("stream")
C. Replace predict with a stream-friendly prediction function
D. Replace schema(schema) with option ("maxFilesPerTrigger", 1)
E. Replace spark.read with spark.readStream

Answer: E

Explanation:
To read from a stream source, the data engineer needs to use the spark.readStream method instead of the spark.read method. The spark.readStream method returns a DataStreamReader object that can be used to specify the details of the input source, such as the format, the schema, the path, and the options. The spark.read method is only suitable for batch processing, not streaming processing. The other changes are not necessary or correct for reading from a stream source. Reference: Structured Streaming Programming Guide, Read a stream, Databricks Data Sources

NEW QUESTION # 299
An engineering manager wants to monitor the performance of a recent project using a Databricks SQL query.
For the first week following the project's release, the manager wants the query results to be updated every minute. However, the manager is concerned that the compute resources used for the query will be left running and cost the organization a lot of money beyond the first week of the project's release.
Which of the following approaches can the engineering team use to ensure the query does not cost the organization any money beyond the first week of the project's release?

A. They can set the query's refresh schedule to end after a certain number of refreshes.
B. They can set the query's refresh schedule to end on a certain date in the query scheduler.
C. They can set a limit to the number of DBUs that are consumed by the SQL Endpoint.
D. They can set a limit to the number of individuals that are able to manage the query's refresh schedule.
E. They cannot ensure the query does not cost the organization money beyond the first week of the project's release.

Answer: B

Explanation:
In Databricks SQL, you can use scheduled query executions to update your dashboards or enable routine alerts. By default, your queries do not have a schedule. To set the schedule, you can use the dropdown pickers to specify the frequency, period, starting time, and time zone. You can also choose to end the schedule on a certain date by selecting the End date checkbox and picking a date from the calendar. This way, you can ensure that the query does not run beyond the first week of the project's release and does not incur any additional cost. Option A is incorrect, as setting a limit to the number of DBUs does not stop the query from running. Option B is incorrect, as there is no option to end the schedule after a certain number of refreshes.
Option C is incorrect, as there is a way to ensure the query does not cost the organization money beyond the first week of the project's release. Option D is incorrect, as setting a limit to the number of individuals who can manage the query's refresh schedule does not affect the query's execution or cost. References: Schedule a query, Schedule a query - Azure Databricks - Databricks SQL

NEW QUESTION # 300
......

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