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

Section	Weight	Objectives
Databricks Lakehouse Platform	24%	- Data Management - Unity Catalog - Lakehouse Architecture - Delta Lake
Data Processing	28%	- ETL Pipelines - Spark SQL - Data Transformation - Structured Streaming
Data Modeling and Storage	20%	- Storage Optimization - File Formats - Data Modeling
Data Quality and Governance	12%	- Data Quality - Data Lineage - Governance
Monitoring and Troubleshooting	16%	- Monitoring - Performance Optimization - Troubleshooting

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q63-Q68):

NEW QUESTION # 63
The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.
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Which statement describes this implementation?

A. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
B. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
C. The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
D. The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value.
E. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.

Answer: C

NEW QUESTION # 64
The data engineer team is configuring environment for development testing, and production before beginning migration on a new data pipeline. The team requires extensive testing on both the code and data resulting from code execution, and the team want to develop and test against similar production data as possible.
A junior data engineer suggests that production data can be mounted to the development testing environments, allowing pre production code to execute against production data. Because all users have Admin privileges in the development environment, the junior data engineer has offered to configure permissions and mount this data for the team.
Which statement captures best practices for this situation?

A. All developer, testing and production code and data should exist in a single unified workspace; creating separate environments for testing and development further reduces risks.
B. Because delta Lake versions all data and supports time travel, it is not possible for user error or malicious actors to permanently delete production data, as such it is generally safe to mount production data anywhere.
C. Because access to production data will always be verified using passthrough credentials it is safe to mount data to any Databricks development environment.
D. In environments where interactive code will be executed, production data should only be accessible with read permissions; creating isolated databases for each environment further reduces risks.

Answer: D

Explanation:
The best practice in such scenarios is to ensure that production data is handled securely and with proper access controls. By granting only read access to production data in development and testing environments, it mitigates the risk of unintended data modification. Additionally, maintaining isolated databases for different environments helps to avoid accidental impacts on production data and systems.

NEW QUESTION # 65
Predictive Optimization is an automated Databricks service enabled by default for Unity Catalog Managed tables. It helps maintain Delta tables by continuously optimizing them to ensure optimal performance and costs. Which two operations does Predictive Optimization run to maintain the Delta tables? (Choose two.)

A. ANALYZE
B. OPTIMIZE
C. BUCKETING
D. COMPACT
E. PARTITION BY

Answer: A,B

Explanation:
Predictive Optimization automatically runs OPTIMIZE to compact small files and improve data layout, and ANALYZE to collect and refresh table statistics. Together, these operations ensure efficient query planning, better data skipping, and optimized performance and cost for Unity Catalog managed Delta tables.

NEW QUESTION # 66
Which statement describes the correct use of pyspark.sql.functions.broadcast?

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

Answer: E

Explanation:
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.

NEW QUESTION # 67
A streaming video analytics team ingests billions of events daily into a Unity Catalog-managed Delta table video_events. Analysts run ad-hoc point-lookup queries on columns like user_id, campaign_id, and region. The team manually runs OPTIMIZE video_events ZORDER BY (user_id, campaign_id, region), but still sees poor performance on recent data and dislikes the operational overhead. The team wants a hands-off way to keep hot columns co-located as query patterns evolve. Which Delta capability should the team leverage on video_events?

A. Schedule OPTIMIZE/ZORDER to run after each job to improve recent file performance.
B. Enable auto-compaction (optimizeWrite and autoCompact).
C. Enable Delta caching.
D. Utilize Liquid Clustering (CLUSTER BY AUTO) and Predictive Optimization.

Answer: D

Explanation:
According to Databricks Delta Lake optimization documentation, Liquid Clustering is a next- generation file organization capability that automatically manages file co-location without requiring explicit partitioning or manual Z-ORDERing. When combined with Predictive Optimization, Databricks automatically maintains clustering across frequently filtered or queried columns, adapting dynamically as query workloads evolve.
This approach eliminates the need for manual maintenance (such as periodic OPTIMIZE or Z- ORDER commands) while improving query performance on large tables--particularly for high- ingest streaming workloads.
Delta caching (B) only improves performance for cached queries and does not address file layout issues, and (D) handles file size optimization but not clustering. Thus, C is the most efficient, modern, and low-maintenance solution recommended by Databricks.

NEW QUESTION # 68
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