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

Section	Weight	Objectives
Topic 1: Streaming Workloads and Change Data Capture	~11%	- Implement reliable streaming pipelines - Apply AUTO CDC APIs and exactly-once semantics
Topic 2: Data Transformation, Cleansing, and Quality	~12%	- Apply advanced Spark transformations - Enforce data quality and quarantine bad data
Topic 3: Monitoring, Logging, and Troubleshooting	~8%	- Diagnose common pipeline and job failures - Use Spark UI, Query Profiler, and system tables
Topic 4: Developing Code for Data Processing using Python and SQL	~22%	- Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader - Manage dependencies, libraries, and UDFs - Implement scalable Python/SQL code and project structures
Topic 5: Cost and Performance Optimization	~13%	- Optimize queries, clusters, and storage - Leverage system tables and observability tools
Topic 6: Data Modeling	~10%	- Apply dimensional modeling techniques - Design scalable Delta Lake schemas and clustering
Topic 7: Data Sharing and Federation	~8%	- Configure Delta Sharing and Lakehouse Federation
Topic 8: CI/CD, Testing, and Deployment	~6%	- Implement testing and deployment pipelines - Deploy with Declarative Automation Bundles, CLI, and REST API
Topic 9: Security and Governance	~10%	- Manage Unity Catalog permissions and ACLs - Implement row-level security, column masking, and compliance

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Databricks Certified Data Engineer Professional Sample Questions (Q45-Q50):

NEW QUESTION # 45
All records from an Apache Kafka producer are being ingested into a single Delta Lake table with the following schema:
key BINARY, value BINARY, topic STRING, partition LONG, offset LONG, timestamp LONG There are 5 unique topics being ingested. Only the "registration" topic contains Personal Identifiable Information (PII). The company wishes to restrict access to PII. The company also wishes to only retain records containing PII in this table for 14 days after initial ingestion.
However, for non-PII information, it would like to retain these records indefinitely.
Which of the following solutions meets the requirements?

A. Because the value field is stored as binary data, this information is not considered PII and no special precautions should be taken.
B. All data should be deleted biweekly; Delta Lake's time travel functionality should be leveraged to maintain a history of non-PII information.
C. Separate object storage containers should be specified based on the partition field, allowing isolation at the storage level.
D. Data should be partitioned by the topic field, allowing ACLs and delete statements to leverage partition boundaries.
E. Data should be partitioned by the registration field, allowing ACLs and delete statements to be set for the PII directory.

Answer: D

Explanation:
By default partitionning by a column will create a separate folder for each subset data linked to the partition.

NEW QUESTION # 46
When evaluating the Ganglia Metrics for a given cluster with 3 executor nodes, which indicator would signal proper utilization of the VM's resources?

A. Total Disk Space remains constant
B. Network I/O never spikes
C. Bytes Received never exceeds 80 million bytes per second
D. CPU Utilization is around 75%
E. The five Minute Load Average remains consistent/flat

Answer: D

Explanation:
In the context of cluster performance and resource utilization, a CPU utilization rate of around
75% is generally considered a good indicator of efficient resource usage. This level of CPU utilization suggests that the cluster is being effectively used without being overburdened or underutilized. A consistent 75% CPU utilization indicates that the cluster's processing power is being effectively employed while leaving some headroom to handle spikes in workload or additional tasks without maxing out the CPU, which could lead to performance degradation. A five Minute Load Average that remains consistent/flat (Option A) might indicate underutilization or a bottleneck elsewhere.
Monitoring network I/O (Options B and C) is important, but these metrics alone don't provide a complete picture of resource utilization efficiency.
Total Disk Space (Option D) remaining constant is not necessarily an indicator of proper resource utilization, as it's more related to storage rather than computational efficiency.

NEW QUESTION # 47
A company has a task management system that tracks the most recent status of tasks. The system takes task events as input and processes events in near real-time using Lakeflow Declarative Pipelines. A new task event is ingested into the system when a task is created or the task status is changed. Lakeflow Declarative Pipelines provides a streaming table (tasks_status) for BI users to query.
The table represents the latest status of all tasks and includes 5 columns:
task_id (unique for each task)
task_name
task_owner
task_status
task_event_time
The table enables three properties: deletion vectors, row tracking, and change data feed (CDF).
A data engineer is asked to create a new Lakeflow Declarative Pipeline to enrich the tasks_status table in near real-time by adding one additional column representing task_owner's department, which can be looked up from a static dimension table (employee).
How should this enrichment be implemented?

A. Create a new Lakeflow Declarative Pipeline: use the readStream() function to read tasks_status table; enrich with the employee table; store the result in a new streaming table.
B. Create a new Lakeflow Declarative Pipeline: use readStream() function with option readChangeFeed to read tasks_status table CDF; enrich with the employee table; create a new streaming table as the result table and use apply_changes() function to process the changes from the enriched CDF.
C. Create a new Lakeflow Declarative Pipeline: use the read() function to read tasks_status table; enrich with employee table; store the result in a materialized view.
D. Create a new Lakeflow Declarative Pipeline: use the readStream() function with the option skipChangeCommits to read the tasks_status table; enrich with the employee table; store the result in a new streaming table.

Answer: B

Explanation:
Change Data Feed (CDF) allows downstream consumers to read incremental changes (inserts, updates, deletes) from a Delta table. The documentation explains that when streaming from a Delta table with CDF enabled, developers can use readStream().option("readChangeFeed","true") to capture incremental events. For maintaining a derived table with enrichment logic, the recommended practice is to use apply_changes(), which applies CDC semantics (insert/update/delete) correctly to the target streaming table. By joining with the static employee dimension, enriched rows are generated before being merged into the new streaming target. This ensures correctness, scalability, and minimal latency. Batch reads or skipping commits do not maintain correctness for CDC pipelines.

NEW QUESTION # 48
A Delta Lake table representing metadata about content from user has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE Based on the above schema, which column is a good candidate for partitioning the Delta Table?

A. User_id
B. Post_id
C. latitude
D. Post_time
E. Date

Answer: E

Explanation:
Partitioning a Delta Lake table improves query performance by organizing data into partitions based on the values of a column. In the given schema, the date column is a good candidate for partitioning for several reasons:
Time-Based Queries: If queries frequently filter or group by date, partitioning by the date column can significantly improve performance by limiting the amount of data scanned. Granularity: The date column likely has a granularity that leads to a reasonable number of partitions (not too many and not too few). This balance is important for optimizing both read and write performance.
Data Skew: Other columns like post_id or user_id might lead to uneven partition sizes (data skew), which can negatively impact performance.
Partitioning by post_time could also be considered, but typically date is preferred due to its more manageable granularity.

NEW QUESTION # 49
A data engineering team needs to implement a tagging system for their tables as part of an automated ETL process, and needs to apply tags programmatically to tables in Unity Catalog.
Which SQL command adds tags to a table programmatically?

A. ALTER TABLE table_name SET TAGS ('key1' = 'value1', 'key2' = 'value2');
B. SET TAGS FOR table_name AS ('key1' = 'value1', 'key2' = 'value2');
C. APPLY TAGS ON table_name VALUES ('key1' = 'value1', 'key2' = 'value2');
D. COMMENT ON TABLE table_name TAGS ('key1' = 'value1', 'key2' = 'value2');

Answer: A

Explanation:
Unity Catalog supports programmatic tagging through the ALTER TABLE statement. Using SET TAGS allows tags to be added or updated directly on a table as part of automated ETL workflows, making it the correct and supported SQL command for managing table tags.

NEW QUESTION # 50
......

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