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

Section	Weight	Objectives
Debugging and Deploying	10%	- Troubleshoot and debug pipelines - Deploy using Asset Bundles, CLI, and APIs - Implement CI/CD and DevOps practices
Data Sharing and Federation	5%	- Manage cross-platform data access - Implement Lakehouse Federation - Use Delta Sharing for secure data sharing
Ensuring Data Security and Compliance	10%	- Secure data at rest and in transit - Ensure data privacy and compliance - Implement access control and permissions
Monitoring and Alerting	10%	- Monitor pipeline performance and health - Track data lineage and metrics - Set up alerts and notifications
Data Modelling	6%	- Optimize table design and partitioning - Implement dimensional and relational models - Design Medallion Architecture
Developing Code for Data Processing using Python and SQL	22%	- Use Databricks-specific libraries and APIs - Implement complex data processing logic - Write efficient and maintainable code
Cost & Performance Optimisation	13%	- Improve query and pipeline performance - Apply cost management best practices - Optimize compute and storage resources
Data Transformation, Cleansing, and Quality	10%	- Apply data cleansing and validation rules - Implement schema evolution and management - Enforce data quality standards
Data Governance	7%	- Use Unity Catalog for governance - Enforce data policies and standards - Manage data assets and metadata
Data Ingestion & Acquisition	7%	- Use Auto Loader and structured streaming - Handle incremental and batch data loads - Ingest data from diverse sources

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Databricks Certified Data Engineer Professional Exam Databricks-Certified-Data-Engineer-Professional Prüfungsfragen mit Lösungen (Q97-Q102):

97. Frage
The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table named users.

Assuming that user_id is a unique identifying key and that delete_requests contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?

A. Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.
B. No; the Delta Lake delete command only provides ACID guarantees when combined with the merge into command.
C. Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.
D. No; the Delta cache may return records from previous versions of the table until the cluster is restarted.
E. No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.

Antwort: E

Begründung:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from Explanation:
The code uses the DELETE FROM command to delete records from the users table that match a condition based on a join with another table called delete_requests, which contains all users that have requested deletion. The DELETE FROM command deletes records from a Delta Lake table by creating a new version of the table that does not contain the deleted records. However, this does not guarantee that the records to be deleted are no longer accessible, because Delta Lake supports time travel, which allows querying previous versions of the table using a timestamp or version number. Therefore, files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files from physical storage.

98. Frage
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.
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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. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
E. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.

Antwort: A

Begründung:
This is the correct answer because Cmd 1 is written in Python and uses a list comprehension to extract the country names from the geo_lookup table and store them in a Python variable named countries af. This variable will contain a list of strings, not a PySpark DataFrame or a SQL view.
Cmd 2 is written in SQL and tries to create a view named sales af by selecting from the sales table where city is in countries af. However, this command will fail because countries af is not a valid SQL entity and cannot be used in a SQL query. To fix this, a better approach would be to use spark.sql() to execute a SQL query in Python and pass the countries af variable as a parameter.

99. Frage
Which statement describes the correct use of pyspark.sql.functions.broadcast?

A. It marks a column as having low enough cardinality to properly map distinct values to available partitions, allowing a broadcast join.
B. It caches a copy of the indicated table on all nodes in the cluster for use in all future queries during the cluster lifetime.
C. It caches a copy of the indicated table on attached storage volumes for all active clusters within a Databricks workspace.
D. It marks a DataFrame as small enough to store in memory on all executors, allowing a broadcast join.
E. It marks a column as small enough to store in memory on all executors, allowing a broadcast join.

Antwort: D

Begründung:
https://spark.apache.org/docs/3.1.3/api/python/reference/api/pyspark.sql.functions.broadcast.html The broadcast function in PySpark is used in the context of joins. When you mark a DataFrame with broadcast, Spark tries to send this DataFrame to all worker nodes so that it can be joined with another DataFrame without shuffling the larger DataFrame across the nodes. This is particularly beneficial when the DataFrame is small enough to fit into the memory of each node. It helps to optimize the join process by reducing the amount of data that needs to be shuffled across the cluster, which can be a very expensive operation in terms of computation and time.
The pyspark.sql.functions.broadcast function in PySpark is used to hint to Spark that a DataFrame is small enough to be broadcast to all worker nodes in the cluster. When this hint is applied, Spark can perform a broadcast join, where the smaller DataFrame is sent to each executor only once and joined with the larger DataFrame on each executor. This can significantly reduce the amount of data shuffled across the network and can improve the performance of the join operation. In a broadcast join, the entire smaller DataFrame is sent to each executor, not just a specific column or a cached version on attached storage. This function is particularly useful when one of the DataFrames in a join operation is much smaller than the other, and can fit comfortably in the memory of each executor node.

100. Frage
A data engineer is running a groupBy aggregation on a massive user activity log grouped by user_id. A few users have millions of records, causing task skew and long runtimes. Which technique will fix the skew in this aggregation?

A. Filter out the skewed users before the aggregation.
B. Increase the Spark driver memory and retry.
C. Use reduceByKey instead of groupBy to avoid shuffles.
D. Use salting by adding a random prefix to skewed keys before aggregation, then aggregate again after removing the prefix.

Antwort: D

Begründung:
Salting distributes records for heavily skewed keys across multiple partitions by adding a random prefix, which balances task execution during the aggregation. A second aggregation after removing the prefix correctly recombines the partial results, eliminating skew-related bottlenecks without losing accuracy.

101. Frage
The following table consists of items found in user carts within an e-commerce website.
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from

The following MERGE statement is used to update this table using an updates view, with schema evaluation enabled on this table.

How would the following update be handled?

A. The new restored field is added to the target schema, and dynamically read as NULL for existing unmatched records.
B. The new nested field is added to the target schema, and files underlying existing records are updated to include NULL values for the new field.
C. The update is moved to separate ''restored'' column because it is missing a column expected in the target schema.
D. The update throws an error because changes to existing columns in the target schema are not supported.

Antwort: B

Begründung:
With schema evolution enabled in Databricks Delta tables, when a new field is added to a record through a MERGE operation, Databricks automatically modifies the table schema to include the new field. In existing records where this new field is not present, Databricks will insert NULL values for that field. This ensures that the schema remains consistent across all records in the table, with the new field being present in every record, even if it is NULL for records that did not originally include it.

102. Frage
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