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

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

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q21-Q26):

NEW QUESTION # 21
The data governance team has instituted a requirement that the "user" table containing Personal Identifiable Information (PII) must have the appropriate masking on the SSN column. This means that anyone outside of the HRAdminGroup should see masked social security numbers as ***-**-
****.
The team created a masking function:

What does the data governance team need to do next to achieve this goal?

A. CREATE TABLE users
(name STRING, ssn INT MASKED ssn_mask);
B. CREATE TABLE users
(name STRING);
ALTER TABLE users CREATE COLUMN ssn CREATE MASK ssn_mask;
C. CREATE TABLE users
(name STRING, int STRING);
ALTER TABLE users ALTER COLUMN ssn CREATE MASK if is_member('HRAdminGroup');
D. CREATE TABLE users
(name STRING, ssn STRING);
ALTER TABLE users ALTER COLUMN ssn SET MASK ssn_mask;

Answer: D

Explanation:
In Databricks, after creating a masking function, you apply it to a column using ALTER TABLE
<table> ALTER COLUMN <column> SET MASK <mask_function>. The table must already include the column (here, ssn as STRING). This ensures that only users in the HRAdminGroup see the unmasked SSN, while all others see the masked value.

NEW QUESTION # 22
The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.
The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series of VACUUM commands on all Delta Lake tables throughout the organization.
The compliance officer has recently learned about Delta Lake's time travel functionality. They are concerned that this might allow continued access to deleted data.
Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?

A. Because Delta Lake's delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes.
B. Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day.
C. Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.
D. Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the vacuum job is run 8 days later.
E. Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges.

Answer: D

Explanation:
https://learn.microsoft.com/en-us/azure/databricks/delta/vacuum

NEW QUESTION # 23
A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

A. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
B. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
C. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
D. Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.
E. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.

Answer: A

NEW QUESTION # 24
A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

A. Credential validation errors while pulling data from an external system.
B. Spill resulting from attached volume storage being too small.
C. Skew caused by more data being assigned to a subset of spark-partitions.
D. Task queueing resulting from improper thread pool assignment.
E. Network latency due to some cluster nodes being in different regions from the source data

Answer: C

Explanation:
This is the correct answer because skew is a common situation that causes increased duration of the overall job. Skew occurs when some partitions have more data than others, resulting in uneven distribution of work among tasks and executors. Skew can be caused by various factors, such as skewed data distribution, improper partitioning strategy, or join operations with skewed keys. Skew can lead to performance issues such as long-running tasks, wasted resources, or even task failures due to memory or disk spills.

NEW QUESTION # 25
An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

A. Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake's versioning ability to create an audit log.
B. Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.
C. Create a separate history table for each pk_id resolve the current state of the table by running a Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from union all filtering the history tables for the most recent state.
D. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
E. Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.

Answer: B

Explanation:
CDF captures changes only from a Delta table and is only forward-looking once enabled. The CDC logs are writing to object storage. So you would need to ingestion those and merge into downstream tables.

NEW QUESTION # 26
......

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