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

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

Databricks Certified Data Engineer Professional Sample Questions:

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


2. How are the operational aspects of Lakeflow Declarative Pipelines different from Spark Structured Streaming?

A) Structured Streaming can process continuous data streams, while Lakeflow Declarative Pipelines cannot.
B) Lakeflow Declarative Pipelines automatically handle schema evolution, while Structured Streaming always requires manual schema management.
C) Lakeflow Declarative Pipelines can write to Delta Lake format, while Structured Streaming cannot.
D) Lakeflow Declarative Pipelines manage the orchestration of multi-stage pipelines automatically, while Structured Streaming requires external orchestration for complex dependencies.


3. A view is registered with the following code:

Both users and orders are Delta Lake tables.
Which statement describes the results of querying recent_orders?

A) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
B) All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
C) Results will be computed and cached when the view is defined; these cached results will incrementally update as new records are inserted into source tables.
D) All logic will execute when the view is defined and store the result of joining tables to the DBFS; this stored data will be returned when the view is queried.


4. A data governance team at a large enterprise is improving data discoverability across its organization. The team has hundreds of tables in their Databricks Lakehouse with thousands of columns that lack proper documentation. Many of these tables were created by different teams over several years, with missing context about column meanings and business logic. The data governance team needs to quickly generate comprehensive column descriptions for all existing tables to meet compliance requirements and improve data literacy across the organization. They want to leverage modern capabilities to automatically generate meaningful descriptions rather than manually documenting each column, which would take months to complete. Which approach should the team use in Databricks to automatically generate column comments and descriptions for existing tables?

A) Navigate to the table in Databricks Catalog Explorer, select the table schema view, and use the AI Generate option which leverages artificial intelligence to automatically create meaningful column descriptions based on column names, data types, sample values, and data patterns.
B) Write custom PySpark code using df.describe() and df.schema to programmatically generate basic statistical descriptions for each column.
C) Use Delta Lake's DESCRIBE HISTORY command to analyze table evolution and infer column purposes from historical changes.
D) Use the DESCRIBE TABLE command to extract existing schema information and manually write descriptions based on column names and data types.


5. A data engineering team is implementing an append-only data pipeline using Delta Lake, and wants to ensure that data is never modified or deleted once written. Which Delta Lake feature should the data engineer enable to prevent modifications to existing data?

A) Delta OPTIMIZE
B) Delta VACUUM
C) Delta APPEND_ONLY
D) Delta Time Travel


Solutions:

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

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