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

SectionWeightObjectives
Topic 1: Developing Code for Data Processing using Python and SQL22%- Write efficient and maintainable code
- Implement complex data processing logic
- Use Databricks-specific libraries and APIs
Topic 2: Debugging and Deploying10%- Troubleshoot and debug pipelines
- Implement CI/CD and DevOps practices
- Deploy using Asset Bundles, CLI, and APIs
Topic 3: Data Governance7%- Manage data assets and metadata
- Use Unity Catalog for governance
- Enforce data policies and standards
Topic 4: Ensuring Data Security and Compliance10%- Secure data at rest and in transit
- Implement access control and permissions
- Ensure data privacy and compliance
Topic 5: Data Modelling6%- Implement dimensional and relational models
- Optimize table design and partitioning
- Design Medallion Architecture
Topic 6: Data Sharing and Federation5%- Manage cross-platform data access
- Use Delta Sharing for secure data sharing
- Implement Lakehouse Federation
Topic 7: Data Ingestion & Acquisition7%- Ingest data from diverse sources
- Handle incremental and batch data loads
- Use Auto Loader and structured streaming
Topic 8: Monitoring and Alerting10%- Set up alerts and notifications
- Monitor pipeline performance and health
- Track data lineage and metrics
Topic 9: Data Transformation, Cleansing, and Quality10%- Implement schema evolution and management
- Enforce data quality standards
- Apply data cleansing and validation rules
Topic 10: Cost & Performance Optimisation13%- Apply cost management best practices
- Optimize compute and storage resources
- Improve query and pipeline performance

Databricks Certified Data Engineer Professional Sample Questions:

Question 1

Which Python variable contains a list of directories to be searched when trying to locate required modules?

A. pypi.path
B. importlib.resource path
C. sys.path
D. pylib.source
E. os.path


Question 2

Spill occurs as a result of executing various wide transformations. However, diagnosing spill requires one to proactively look for key indicators.
Where in the Spark UI are two of the primary indicators that a partition is spilling to disk?

A. Executor's detail screen and Executor's log files
B. Query's detail screen and Job's detail screen
C. Stage's detail screen and Executor's log files
D. Driver's and Executor's log files
E. Stage's detail screen and Query's detail screen


Question 3

A data engineer is designing a pipeline in Databricks that processes records from a Kafka stream where late-arriving data is common. Which approach should the data engineer use?

A. Use batch processing and overwrite the entire output table each time to ensure late data is incorporated correctly.
B. Implement a custom solution using Databricks Jobs to periodically reprocess all historical data.
C. Use a watermark to specify the allowed lateness to accommodate records that arrive after their expected window, ensuring correct aggregation and state management.
D. Use an Auto CDC pipeline with batch tables to simplify late data handling.


Question 4

A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.
Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.

Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?

A. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.
B. Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.
C. Both commands will fail. No new variables, tables, or views will be created.
D. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.
E. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.


Question 5

A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?

A. The Parquet file footers are scanned for min and max statistics for the latitude column
B. All records are cached to attached storage and then the filter is applied
C. The Hive metastore is scanned for min and max statistics for the latitude column
D. The Delta log is scanned for min and max statistics for the latitude column
E. All records are cached to an operational database and then the filter is applied


Solutions:

Question 1
Answer: C
Question 2
Answer: C
Question 3
Answer: C
Question 4
Answer: A
Question 5
Answer: D

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