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

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

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q158-Q163):

NEW QUESTION # 158
Which statement describes a key benefit of an end-to-end test?

A. It closely simulates real world usage of your application.
B. It pinpoint errors in the building blocks of your application.
C. It makes it easier to automate your test suite
D. It provides testing coverage for all code paths and branches.

Answer: A

Explanation:
End-to-end testing is a methodology used to test whether the flow of an application, from start to finish, behaves as expected. The key benefit of an end-to-end test is that it closely simulates real- world, user behavior, ensuring that the system as a whole operates correctly.

NEW QUESTION # 159
A nightly job ingests data into a Delta Lake table using the following code:

The next step in the pipeline requires a function that returns an object that can be used to manipulate new records that have not yet been processed to the next table in the pipeline.
Which code snippet completes this function definition?

A. def new_records():
B.
C. return spark.readStream.load("bronze")
D. return spark.readStream.table("bronze")
E. return spark.read.option("readChangeFeed", "true").table ("bronze")

Answer: B

Explanation:
https://docs.databricks.com/en/delta/delta-change-data-feed.html
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NEW QUESTION # 160
A data engineer is using Auto Loader to read incoming JSON data as it arrives. They have configured Auto Loader to quarantine invalid JSON records but notice that over time, some records are being quarantined even though they are well-formed JSON.
The code snippet is:
df = (spark.readStream
.format("cloudFiles")
.option("cloudFiles.format", "json")
.option("badRecordsPath", "/tmp/somewhere/badRecordsPath")
.schema("a int, b int")
.load("/Volumes/catalog/schema/raw_data/"))
What is the cause of the missing data?

A. The engineer forgot to set the option "cloudFiles.quarantineMode" = "rescue".
B. The badRecordsPath location is accumulating many small files.
C. The source data is valid JSON but does not conform to the defined schema in some way.
D. At some point, the upstream data provider switched everything to multi-line JSON.

Answer: C

Explanation:
Auto Loader quarantines records that cannot be parsed according to the specified schema, even if the JSON itself is well formed. If incoming records contain additional fields, missing fields, or incompatible data types compared to the declared schema, they are treated as invalid and routed to the bad records path.

NEW QUESTION # 161
Which statement describes Delta Lake Auto Compaction?

A. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.
B. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
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C. Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
D. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.
E. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.

Answer: E

Explanation:
This is the correct answer because it describes the behavior of Delta Lake Auto Compaction, which is a feature that automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones. Auto Compaction runs as an asynchronous job after a write to a table has succeeded and checks if files within a partition can be further compacted. If yes, it runs an optimize job with a default target file size of 128 MB. Auto Compaction only compacts files that have not been compacted previously.

NEW QUESTION # 162
A Delta Lake table in the Lakehouse named customer_parsams is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
Immediately after each update succeeds, the data engineer team would like to determine the difference between the new version and the previous of the table. Given the current implementation, which method can be used?
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A. Execute a query to calculate the difference between the new version and the previous version using Delta Lake's built-in versioning and time travel functionality.
B. Parse the Spark event logs to identify those rows that were updated, inserted, or deleted.
C. Execute DESCRIBE HISTORY customer_churn_params to obtain the full operation metrics for the update, including a log of all records that have been added or modified.
D. Parse the Delta Lake transaction log to identify all newly written data files.

Answer: A

Explanation:
Delta Lake provides built-in versioning and time travel capabilities, allowing users to query previous snapshots of a table. This feature is particularly useful for understanding changes between different versions of the table. In this scenario, where the table is overwritten nightly, you can use Delta Lake's time travel feature to execute a query comparing the latest version of the table (the current state) with its previous version. This approach effectively identifies the differences (such as new, updated, or deleted records) between the two versions. The other options do not provide a straightforward or efficient way to directly compare different versions of a Delta Lake table.

NEW QUESTION # 163
......

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