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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Production Pipelines and Orchestration | - Error handling and recovery strategies - Databricks Workflows - Job scheduling and monitoring |
| Topic 2: Delta Lake and Data Management | - Delta Lake transactions and ACID properties - Time travel and versioning - Schema evolution and enforcement |
| Topic 3: Databricks Lakehouse Platform Architecture | - Medallion architecture (Bronze, Silver, Gold) - Data governance concepts (Unity Catalog basics) - Workspace and cluster architecture |
| Topic 4: Data Ingestion and Processing | - Structured Streaming fundamentals - Batch and streaming ingestion with Auto Loader - ETL pipeline design patterns |
| Topic 5: Data Modeling and Transformation | - Performance optimization techniques - Dimensional modeling concepts - Spark SQL transformations |
Databricks Certified Data Engineer Professional Sample Questions:
1. A data engineer has configured their Databricks Asset Bundle with multiple targets in databricks.yml and deployed it to the production workspace. Now, to validate the deployment, they need to invoke a job named my_project_job specifically within the prod target context.
Assuming the job is already deployed, they need to trigger its execution while ensuring the target- specific configuration is respected. Which command will trigger the job execution?
A) databricks run my_project_job -t prod
B) databricks execute my_project_job -e prod
C) databricks job run my_project_job --env prod
D) databricks bundle run my_project_job -t prod
2. The data science team has requested assistance in accelerating queries on free form text from user reviews. The data is currently stored in Parquet with the below schema:
item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
A junior data engineer suggests converting this data to Delta Lake will improve query performance.
Which response to the junior data engineer's suggestion is correct?
A) Delta Lake statistics are not optimized for free text fields with high cardinality.
B) ZORDER ON review will need to be run to see performance gains.
C) The Delta log creates a term matrix for free text fields to support selective filtering.
D) Delta Lake statistics are only collected on the first 4 columns in a table.
E) Text data cannot be stored with Delta Lake.
3. A data engineering team is setting up deployment automation. To deploy workspace assets remotely using the Databricks CLI command, they must configure it with proper authentication.
Which authentication approach will provide the highest level of security?
A) Use a service principal and its Personal Access Token.
B) Use a service principal ID and its OAuth client secret.
C) Use a shared user account and its OAuth client secret.
D) Use a service principal with OAuth token federation.
4. A data engineer is configuring a Lakeflow Declarative Pipeline to process CDC (Change Data Capture) data from a source. The source events sometimes arrive out of order, and multiple updates may occur with the same update_timestamp but with different update_sequence_id.
What should the data engineer do to ensure events are sequenced correctly?
A) Use a window function to sort update_sequence_id within the same partition, i.e., update_timestamp in the LDP pipeline.
B) Set track_history_column_list to [event_timestamp, event_id] in AUTO CDC APIs.
C) Use SEQUENCE BY STRUCT(event_timestamp, update_sequence_id) in AUTO CDC APIs.
D) Use dropDuplicates() to remove out-of-order and duplicate records in LDP.
5. Which of the following is true of Delta Lake and the Lakehouse?
A) Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
B) Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
C) Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
D) Z-order can only be applied to numeric values stored in Delta Lake tables
E) Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: A | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: E |







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