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

Section	Objectives
Data Ingestion and Transformation	- Ingest data using Apache Spark and Databricks - Transform and clean datasets using Spark SQL and DataFrame APIs - Handle batch and streaming data pipelines
Production Pipelines and Orchestration	- Build and manage workflows using Databricks Jobs - Pipeline reliability and fault tolerance - Automate ETL pipelines and scheduling
Data Modeling and Storage	- Design scalable data lakehouse architectures - Schema evolution and data partitioning strategies - Delta Lake table design and optimization
Security, Governance, Monitoring, and Optimization	- Cost optimization and performance tuning - Monitor and optimize Spark workloads - Implement Unity Catalog governance and access control

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Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q16-Q21):

16. Frage
An hourly batch job is configured to ingest data files from a cloud object storage container where each batch represent all records produced by the source system in a given hour. The batch job to process these records into the Lakehouse is sufficiently delayed to ensure no late-arriving data is missed. The user_id field represents a unique key for the data, which has the following schema:
user_id BIGINT, username STRING, user_utc STRING, user_region STRING, last_login BIGINT, auto_pay BOOLEAN, last_updated BIGINT New records are all ingested into a table named account_history which maintains a full record of all data in the same schema as the source. The next table in the system is named account_current and is implemented as a Type 1 table representing the most recent value for each unique user_id.
Assuming there are millions of user accounts and tens of thousands of records processed hourly, which implementation can be used to efficiently update the described account_current table as part of each hourly batch job?

A. Overwrite the account current table with each batch using the results of a query against the account history table grouping by user id and filtering for the max value of last updated.
B. Filter records in account history using the last updated field and the most recent hour processed, as well as the max last iogin by user id write a merge statement to update or insert the most recent value for each user id.
C. Use Delta Lake version history to get the difference between the latest version of account history and one version prior, then write these records to account current.
D. Use Auto Loader to subscribe to new files in the account history directory; configure a Structured Streaminq trigger once job to batch update newly detected files into the account current table.
E. Filter records in account history using the last updated field and the most recent hour processed, making sure to deduplicate on username; write a merge statement to update or insert the

Antwort: B

Begründung:
most recent value for each username.
Explanation:
This is the correct answer because it efficiently updates the account current table with only the most recent value for each user id. The code filters records in account history using the last updated field and the most recent hour processed, which means it will only process the latest batch of data. It also filters by the max last login by user id, which means it will only keep the most recent record for each user id within that batch. Then, it writes a merge statement to update or insert the most recent value for each user id into account current, which means it will perform an upsert operation based on the user id column. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Upsert into a table using merge" section.

17. Frage
Which method can be used to determine the total wall-clock time it took to execute a query?

A. In the Spark UI, take the sum of all task durations that ran across all stages for all jobs associated with that query.
B. Open the Query Profiler associated with that query and use the Total wall-clock duration metric.
C. Open the Query Profiler associated with that query and use the Aggregated task time metric.
D. In the Spark UI, take the job duration of the longest-running job associated with that query.

Antwort: B

Begründung:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
The Query Profiler in Databricks SQL and notebooks provides a detailed breakdown of query performance metrics. The "Total wall-clock duration" metric directly represents the total elapsed time from query start to completion, including all execution, planning, and waiting stages. In contrast, "Aggregated task time" reflects the cumulative duration across all parallel tasks, which does not equal the total elapsed wall time since tasks often run concurrently. Using job duration from Spark UI can underestimate or overestimate runtime when queries span multiple jobs. Therefore, the Query Profiler's total wall-clock duration is the officially documented method to determine actual query execution time.

18. Frage
You are currently working to ingest millions of files that get uploaded to the cloud object storage for consumption, and you are asked to build a process to ingest this data, the schema of the file is expected to change over time, and the ingestion process should be able to handle these changes automatically. Which of the following method can be used to ingest the data incrementally?

A. AUTO APPEND
B. Structured Streaming
C. COPY INTO
D. AUTO LOADER
E. Checkpoint

Antwort: D

Begründung:
Explanation
The answer is AUTO LOADER,
Use Auto Loader instead of the COPY INTO SQL command when:
*You want to load data from a file location that contains files in the order of millions or higher. Auto Loader can discover files more efficiently than the COPY INTO SQL command and can split file processing into multiple batches.
*COPY INTO only directory listing but AUTO LOADER supports File notification method where the Auto Loader continues to ingest files as they arrive in cloud object storage lever-aging cloud provider(Queues and triggers) and Spark's structured streaming.
*Your data schema evolves frequently. Auto Loader provides better support for schema in-ference and evolution. See Configuring schema inference and evolution in Auto Loader.

19. Frage
What is the main difference between the below two commands?
1.INSERT OVERWRITE table_name
2.SELECT * FROM table
1.CREATE OR REPLACE TABLE table_name
2.AS SELECT * FROM table

A. Both are same and results in identical outcomes
B. INSERT OVERWRITE maintains historical data versions by de-fault, CREATE OR REPLACEclears the historical data versions by default
C. INSERT OVERWRITE clears historical data versions by de-fault, CREATE OR REPLACE maintains the historical data versions by default
D. INSERT OVERWRITE replaces data and schema by default, CREATE OR REPLACEreplaces data by default
E. INSERT OVERWRITE replaces data by default, CREATE OR REPLACE replaces data and Schema by default

Antwort: E

Begründung:
Explanation
The main difference between INSERT OVERWRITE and CREATE OR REPLACE TABLE(CRAS) is that CRAS can modify the schema of the table, i.e it can add new columns or change data types of existing columns. By default INSERT OVERWRITE only overwrites the data.
INSERT OVERWRITE can also be used to overwrite schema, only when
spark.databricks.delta.schema.autoMerge.enabled is set true if this option is not enabled and if there is a schema mismatch command will fail.

20. Frage
A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch named dev-2.3.9 is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?

A. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9
B. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
C. Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository
D. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.
E. Merge all changes back to the main branch in the remote Git repository and clone the repo again

Antwort: D

Begründung:
This is the correct answer because it will allow the developer to update their local repository with the latest changes from the remote repository and switch to the desired branch. Pulling changes will not affect the current branch or create any conflicts, as it will only fetch the changes and not merge them. Selecting the dev-2.3.9 branch from the dropdown will checkout that branch and display its contents in the notebook. Verified Reference: [Databricks Certified Data Engineer Professional], under "Databricks Tooling" section; Databricks Documentation, under "Pull changes from a remote repository" section.

21. Frage
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