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

Section	Weight	Objectives
Topic 1: Monitoring and Troubleshooting	16%	- Monitoring - Performance Optimization - Troubleshooting
Topic 2: Databricks Lakehouse Platform	24%	- Unity Catalog - Lakehouse Architecture - Data Management - Delta Lake
Topic 3: Data Modeling and Storage	20%	- Data Modeling - Storage Optimization - File Formats
Topic 4: Data Processing	28%	- Structured Streaming - Spark SQL - ETL Pipelines - Data Transformation
Topic 5: Data Quality and Governance	12%	- Data Quality - Governance - Data Lineage

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q203-Q208):

NEW QUESTION # 203
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. 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.
B. 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.
C. Create a separate history table for each pk_id resolve the current state of the table by running a union all filtering the history tables for the most recent state.
D. 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.
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: D

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 # 204
A developer has successfully configured credential for Databricks Repos and cloned a remote Git repository. Hey don not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace.
Use Response to pull changes from the remote Git repository commit and push changes to a branch that appeared as a changes were pulled.

A. Use Repos to merge all differences and make a pull request back to the remote repository.
B. Use Repos to pull changes from the remote Git repository; commit and push changes to a branch that appeared as changes were pulled.
C. Use repos to merge all difference and make a pull request back to the remote repository.
D. Use repos to create a fork of the remote repository commit all changes and make a pull request on the source repository
E. Use Repos to create a new branch commit all changes and push changes to the remote Git repertory.
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Answer: E

Explanation:
In Databricks Repos, when a user does not have privileges to make changes directly to the main branch of a cloned remote Git repository, the recommended approach is to create a new branch within the Databricks workspace. The developer can then make changes in this new branch, commit those changes, and push the new branch to the remote Git repository. This workflow allows for isolated development without affecting the main branch, enabling the developer to propose changes via a pull request from the new branch to the main branch in the remote repository. This method adheres to common Git collaboration workflows, fostering code review and collaboration while ensuring the integrity of the main branch.

NEW QUESTION # 205
Which distribution does Databricks support for installing custom Python code packages?

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

Answer: D

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

NEW QUESTION # 206
What describes a primary technical challenge in ensuring consistent PII masking across all nodes in large-scale, distributed Databricks batch and streaming pipelines?

A. PII masking is only required for direct identifiers.
B. Masking functions must be standardized and managed through Unity Catalog, with enforcement applied across all relevant datasets to avoid any data inconsistency.
C. Dynamic data masking is applied only at rest, so it does not affect query performance.
D. Native masking in Databricks automatically synchronizes with all downstream external Databricks systems.

Answer: B

Explanation:
Consistent PII masking in distributed batch and streaming pipelines requires centrally defined and governed masking logic. Standardizing masking functions and enforcing them through Unity Catalog ensures that the same rules are applied uniformly across all datasets and execution paths, preventing inconsistencies across nodes and workloads.

NEW QUESTION # 207
A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
assert(myIntegrate(lambda x: x*x, 0, 3) [0] == 9)
Which kind of the test does the above line exemplify?

A. functional
B. Unit
C. Manual
D. Integration
E. End-to-end

Answer: B

Explanation:
A unit test is designed to verify the correctness of a small, isolated piece of code, typically a single function. Testing a mathematical function that calculates the area under a curve is an example of a unit test because it is testing a specific, individual function to ensure it operates as expected.

NEW QUESTION # 208
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