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

Section	Weight	Objectives
Streaming Workloads and Change Data Capture	~11%	- Implement reliable streaming pipelines - Apply AUTO CDC APIs and exactly-once semantics
CI/CD, Testing, and Deployment	~6%	- Deploy with Declarative Automation Bundles, CLI, and REST API - Implement testing and deployment pipelines
Data Transformation, Cleansing, and Quality	~12%	- Enforce data quality and quarantine bad data - Apply advanced Spark transformations
Monitoring, Logging, and Troubleshooting	~8%	- Diagnose common pipeline and job failures - Use Spark UI, Query Profiler, and system tables
Data Sharing and Federation	~8%	- Configure Delta Sharing and Lakehouse Federation
Data Modeling	~10%	- Design scalable Delta Lake schemas and clustering - Apply dimensional modeling techniques
Developing Code for Data Processing using Python and SQL	~22%	- Implement scalable Python/SQL code and project structures - Build pipelines with Lakeflow Spark Declarative Pipelines and Auto Loader - Manage dependencies, libraries, and UDFs
Cost and Performance Optimization	~13%	- Optimize queries, clusters, and storage - Leverage system tables and observability tools
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 (Q195-Q200):

NEW QUESTION # 195
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 STRING);
ALTER TABLE users ALTER COLUMN ssn SET MASK ssn_mask;
B. CREATE TABLE users
(name STRING);
ALTER TABLE users CREATE COLUMN ssn CREATE MASK ssn_mask;
C. CREATE TABLE users
(name STRING, ssn INT MASKED ssn_mask);
D. CREATE TABLE users
(name STRING, int STRING);
ALTER TABLE users ALTER COLUMN ssn CREATE MASK if is_member('HRAdminGroup');

Answer: A

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 # 196
A data engineer wants to create a cluster using the Databricks CLI for a big ETL pipeline. The cluster should have five workers, one driver of type i3.xlarge, and should use the '14.3.x- scala2.12' runtime. Which command should the data engineer use?

A. databricks compute create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster- name Data Engineer_cluster
B. databricks compute add 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster
C. databricks clusters add 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster-name Data Engineer_cluster
D. databricks clusters create 14.3.x-scala2.12 --num-workers 5 --node-type-id i3.xlarge --cluster- name DataEngineer_cluster

Answer: D

Explanation:
The correct Databricks CLI command to create a new cluster is databricks clusters create. You specify the runtime with --spark-version (here '14.3.x-scala2.12'), the number of workers with -- num-workers, the node type with --node-type-id, and the cluster name with --cluster-name. This command properly initializes the cluster with the desired configuration.

NEW QUESTION # 197
Which distribution does Databricks support for installing custom Python code packages?

A. Wheels
B. jars
C. nom
D. CRAM
E. sbt
F. CRAN

Answer: C

Explanation:
https://learn.microsoft.com/en-us/azure/databricks/workflows/jobs/how-to/use-python-wheels-in-workflows

NEW QUESTION # 198
A junior data engineer seeks to leverage Delta Lake's Change Data Feed functionality to create a Type 1 table representing all of the values that have ever been valid for all rows in a bronze table created with the property delta.enableChangeDataFeed = true. They plan to execute the following code as a daily job:

Which statement describes the execution and results of running the above query multiple times?

A. Each time the job is executed, the entire available history of inserted or updated records will be appended to the target table, resulting in many duplicate entries.
B. Each time the job is executed, the target table will be overwritten using the entire history of inserted or updated records, giving the desired result.
C. Each time the job is executed, newly updated records will be merged into the target table, overwriting previous values with the same primary keys.
D. Each time the job is executed, the differences between the original and current versions are calculated; this may result in duplicate entries for some records.
E. Each time the job is executed, only those records that have been inserted or updated since the last execution will be appended to the target table giving the desired result.

Answer: A

Explanation:
Reading table's changes, captured by CDF, using spark.read means that you are reading them as a static source. So, each time you run the query, all table's changes (starting from the specified startingVersion) will be read.

NEW QUESTION # 199
A data team's Structured Streaming job is configured to calculate running aggregates for item sales to update a downstream marketing dashboard. The marketing team has introduced a new field to track the number of times this promotion code is used for each item. A junior data engineer suggests updating the existing query as follows: Note that proposed changes are in bold.
Original query:

Proposed query:

Which step must also be completed to put the proposed query into production?

A. Run REFRESH TABLE delta, /item_agg'
B. Register the data in the "/item_agg" directory to the Hive metastore
C. Increase the shuffle partitions to account for additional aggregates
D. Specify a new checkpointlocation
E. Remove .option (mergeSchema', true') from the streaming write

Answer: D

Explanation:
When introducing a new aggregation or a change in the logic of a Structured Streaming query, it is generally necessary to specify a new checkpoint location. This is because the checkpoint directory contains metadata about the offsets and the state of the aggregations of a streaming query. If the logic of the query changes, such as including a new aggregation field, the state information saved in the current checkpoint would not be compatible with the new logic, potentially leading to incorrect results or failures. Therefore, to accommodate the new field and ensure the streaming job has the correct starting point and state information for aggregations, a new checkpoint location should be specified.

NEW QUESTION # 200
......

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