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

Section	Weight	Objectives
Topic 1: Data Sharing and Federation	5%	- Implement Lakehouse Federation - Manage cross-platform data access - Use Delta Sharing for secure data sharing
Topic 2: Monitoring and Alerting	10%	- Track data lineage and metrics - Monitor pipeline performance and health - Set up alerts and notifications
Topic 3: Ensuring Data Security and Compliance	10%	- Secure data at rest and in transit - Ensure data privacy and compliance - Implement access control and permissions
Topic 4: Cost & Performance Optimisation	13%	- Apply cost management best practices - Improve query and pipeline performance - Optimize compute and storage resources
Topic 5: Developing Code for Data Processing using Python and SQL	22%	- Use Databricks-specific libraries and APIs - Implement complex data processing logic - Write efficient and maintainable code
Topic 6: Debugging and Deploying	10%	- Troubleshoot and debug pipelines - Deploy using Asset Bundles, CLI, and APIs - Implement CI/CD and DevOps practices
Topic 7: Data Governance	7%	- Enforce data policies and standards - Use Unity Catalog for governance - Manage data assets and metadata
Topic 8: Data Transformation, Cleansing, and Quality	10%	- Enforce data quality standards - Implement schema evolution and management - Apply data cleansing and validation rules
Topic 9: Data Modelling	6%	- Design Medallion Architecture - Implement dimensional and relational models - Optimize table design and partitioning
Topic 10: Data Ingestion & Acquisition	7%	- Handle incremental and batch data loads - Use Auto Loader and structured streaming - Ingest data from diverse sources

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q139-Q144):

NEW QUESTION # 139
Given the following PySpark code snippet in a Databricks notebook:
filtered_df = spark.read.format("delta").load("/mnt/data/large_table")
\
.filter("event_date > '2024-01-01'")
filtered_df.count()
The data engineer notices from the Query Profiler that the scan operator for filtered_df is reading almost all files, despite the filter being applied.
What is the probable reason for poor data skipping?

A. The Delta table lacks optimization that enables dynamic file pruning.
B. The filter condition involves a data type excluded from data skipping support.
C. The event_date column is outside the table's partitioning and Z-ordering scheme.
D. The filter is executed only after the full data scan, preventing data skipping.

Answer: C

Explanation:
Delta Lake's data skipping relies on partitioning and clustering (such as Z-ordering) on the filtered columns. If event_date is neither a partition column nor included in the table's clustering strategy, Spark must scan most files because file-level statistics cannot be effectively used to prune irrelevant data.

NEW QUESTION # 140
A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records.
In addition to de-duplicating records within the batch, which of the following approaches allows the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?

A. Rely on Delta Lake schema enforcement to prevent duplicate records.
B. Perform a full outer join on a unique key and overwrite existing data.
C. VACUUM the Delta table after each batch completes.
D. Set the configuration delta.deduplicate = true.
E. Perform an insert-only merge with a matching condition on a unique key.

Answer: E

Explanation:
To deduplicate data against previously processed records as it is inserted into a Delta table, you can use the merge operation with an insert-only clause. This allows you to insert new records that do not match any existing records based on a unique key, while ignoring duplicate records that match existing records. For example, you can use the following syntax:
MERGE INTO target_table USING source_table ON target_table.unique_key = source_table.unique_key WHEN NOT MATCHED THEN INSERT * This will insert only the records from the source table that have a unique key that is not present in the target table, and skip the records that have a matching key. This way, you can avoid inserting duplicate records into the Delta table.

NEW QUESTION # 141
A data engineering team is configuring access controls in Databricks Unity Catalog. They grant the SELECT privilege on the sales catalog to the analyst_group, expecting that members of this group will automatically have SELECT access to all current and future schemas, tables, and views within the catalog. What describes the privilege inheritance behavior in Unity Catalog?

A. Granting SELECT on a catalog automatically applies SELECT to all current and future schemas, tables, and views within that catalog.
B. Granting SELECT at the catalog level applies to existing schemas and tables but not to those created in the future.
C. Privileges in Unity Catalog do not cascade; SELECT must be explicitly granted on each schema and table, even if granted at the catalog level.
D. Privileges granted at the schema level override any catalog-level privileges and prevent access unless explicitly revoked.

Answer: C

Explanation:
In Unity Catalog, privileges are non-cascading--meaning that granting a privilege (like SELECT) on a catalog does not automatically grant the same privilege on contained objects (schemas, tables, or views). Each object type has its own independent access control hierarchy.
According to the Databricks access control documentation: "Privileges do not automatically cascade from catalog to schema or table levels." Administrators must explicitly grant privileges on each level if users need access across objects. This design ensures tighter governance and least-privilege enforcement. Therefore, option B correctly describes Unity Catalog's privilege model, while A and D incorrectly imply automatic inheritance.

NEW QUESTION # 142
The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database.
After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from user. They then modify their code to the following (leaving all other variables unchanged).

Which statement describes what will happen when the above code is executed?

A. The connection to the external table will fail; the string "redacted" will be printed.
B. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the encoded password will be saved to DBFS.
C. The connection to the external table will succeed; the string value of password will be printed in plain text.
D. An interactive input box will appear in the notebook; if the right password is provided, the connection will succeed and the password will be printed in plain text.
E. The connection to the external table will succeed; the string "redacted" will be printed.

Answer: E

Explanation:
This is the correct answer because the code is using the dbutils.secrets.get method to retrieve the password from the secrets module and store it in a variable. The secrets module allows users to securely store and access sensitive information such as passwords, tokens, or API keys. The connection to the external table will succeed because the password variable will contain the actual password value. However, when printing the password variable, the string "redacted" will be displayed instead of the plain text password, as a security measure to prevent exposing sensitive information in notebooks.

NEW QUESTION # 143
Which distribution does Databricks support for installing custom Python code packages?

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

Answer: F

Explanation:
Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from Explanation:
https://learn.microsoft.com/en-us/azure/databricks/workflows/jobs/how-to/use-python-wheels-in- workflows

NEW QUESTION # 144
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