Certified-Data-Engineer-Professional Certification Exam Cost | Minimum Certified-Data-Engineer-Professional Pass Score

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

Section	Weight	Objectives
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
Monitoring, Logging, and Troubleshooting	~8%	- Diagnose common pipeline and job failures - Use Spark UI, Query Profiler, and system tables
CI/CD, Testing, and Deployment	~6%	- Deploy with Declarative Automation Bundles, CLI, and REST API - Implement testing and deployment pipelines
Data Sharing and Federation	~8%	- Configure Delta Sharing and Lakehouse Federation
Streaming Workloads and Change Data Capture	~11%	- Apply AUTO CDC APIs and exactly-once semantics - Implement reliable streaming pipelines
Security and Governance	~10%	- Implement row-level security, column masking, and compliance - Manage Unity Catalog permissions and ACLs
Cost and Performance Optimization	~13%	- Optimize queries, clusters, and storage - Leverage system tables and observability tools
Data Transformation, Cleansing, and Quality	~12%	- Apply advanced Spark transformations - Enforce data quality and quarantine bad data
Data Modeling	~10%	- Apply dimensional modeling techniques - Design scalable Delta Lake schemas and clustering

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Minimum Certified-Data-Engineer-Professional Pass Score & Exam Certified-Data-Engineer-Professional Overviews

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Databricks Certified Data Engineer Professional Sample Questions (Q222-Q227):

NEW QUESTION # 222
While reviewing a query's execution in the Databricks Query Profiler, a data engineer observes that the Top Operators panel shows a Sort operator with high Time Spent and Memory Peak metrics. The Spark UI also reports frequent data spilling. How should the data engineer address this issue?

A. Convert the sort operation to a filter operation.
B. Switch to a broadcast join to reduce memory usage.
C. Repartition the DataFrame to a single partition before sorting.
D. Increase the number of shuffle partitions to better distribute data.

Answer: D

Explanation:
Increasing the number of shuffle partitions distributes the data across more tasks, reducing per- task memory pressure during the sort operation. This helps mitigate spilling by lowering memory peak usage per task and improves overall sort performance in large-scale distributed queries.

NEW QUESTION # 223
A data engineer has a Delta table orders with deletion vectors enabled. The engineer executes the following command:
DELETE FROM orders WHERE status = 'cancelled';
What should be the behavior of deletion vectors when the command is executed?

A. Delta automatically removes all cancelled orders permanently.
B. Rows are marked as deleted both in metadata and in files.
C. Files are physically rewritten without the deleted rows.
D. Rows are marked as deleted in metadata, not in files.

Answer: D

Explanation:
Deletion vectors (DVs) in Delta Lake optimize delete operations by marking deleted rows logically in metadata rather than rewriting Parquet files. When a DELETE statement is executed, affected rows are tracked by DVs in the transaction log. The data remains in the underlying files but is filtered out during query reads. This improves performance for frequent deletes and updates since file rewrites are deferred. Physical data removal only occurs when a VACUUM command is later executed. The Databricks documentation confirms: "With deletion vectors, deleted rows are marked in metadata and skipped at read time, avoiding file rewrites." Thus, rows are marked as deleted in metadata--not in files.

NEW QUESTION # 224
A security team wants to enforce data protection for a customer table containing customer PII data. To comply with local policies, sales team members should only see customers from their region, while non-admin users should have email addresses masked. Which implementation approach should be used when using Unity Catalog row filters and column masks?

A. Implement row filters with SQL UDFs based on user region only since column masks cannot be combined with row filters on the same table, then apply them be recreating the table with DROP TABLE and CREATE TABLE SET ROW FILTER commands.
B. Create SQL UDFs for row filtering based on user region and column masking based on group membership, then apply them using ALTER TABLE SET ROW FILTER and ALTER COLUMN SET MASK commands.
C. Create a view with dynamic WHERE clauses for region filtering and use string replacement functions for email masking using ALTER COLUMN SET MASK command.
D. Use table ACLs to restrict access using tags with GRANT SELECT ON table_name WITH TAG command, and rely on application-level filtering for sensitive data based on user region.

Answer: B

Explanation:
Unity Catalog enforces fine-grained access control by applying SQL UDF-based row filters and column masks directly at the table level. Row filter UDFs can restrict visible rows based on the user's region, while column mask UDFs can dynamically mask sensitive fields like email addresses for non-admin users. Applying them with ALTER TABLE SET ROW FILTER and ALTER COLUMN SET MASK ensures centralized, consistent enforcement of data protection policies across all access paths.

NEW QUESTION # 225
A Data Engineer is building a fraud detection pipeline that calls out to Open AI, via a Python library, and needs to include an access token when using the API. Which Databricks CLI command should the Data Engineer use to create the secret?

A. databricks secrets put-secret KEY SCOPE; dbutils.secrets.get (KEY, SCOPE)
B. databricks tokens put-token KEY SCOPE; dbutils.secrets.get (KEY, SCOPE)
C. databricks tokens put-token SCOPE KEY; dbutils.tokens.get (SCOPE, KEY)
D. databricks secrets put-secret SCOPE KEY; dbutils.secrets.get (SCOPE, KEY)

Answer: D

Explanation:
The Databricks CLI command databricks secrets put-secret <scope> <key> is used to securely store sensitive values such as API access tokens in a secret scope. The secret can then be retrieved at runtime using dbutils.secrets.get(scope, key), ensuring credentials are not hard- coded and remain protected.

NEW QUESTION # 226
Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.
Which statement describes a main benefit that offset this additional effort?

A. Improves the quality of your data
B. Troubleshooting is easier since all steps are isolated and tested individually
C. Yields faster deployment and execution times
D. Validates a complete use case of your application
E. Ensures that all steps interact correctly to achieve the desired end result

Answer: B

NEW QUESTION # 227
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