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

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

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q233-Q238):

NEW QUESTION # 233
A developer has successfully configured their credentials for Databricks Repos and cloned a remote Git repository. They do not have privileges to make changes to the main branch, which is the only branch currently visible in their workspace. Which approach allows this user to share their code updates without the risk of overwriting the work of their teammates?

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

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 # 234
The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes.
Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

A. Schedule a job to execute the pipeline once hour on a new job cluster.
B. Schedule a Structured Streaming job with a trigger interval of 60 minutes.
C. Schedule a jo to execute the pipeline once and hour on a dedicated interactive cluster.
D. Configure a job that executes every time new data lands in a given directory.

Answer: A

Explanation:
Scheduling a job to execute the data processing pipeline once an hour on a new job cluster is the most cost-effective solution given the scenario. Job clusters are ephemeral in nature; they are spun up just before the job execution and terminated upon completion, which means you only incur costs for the time the cluster is active. Since the total processing time is only 10 minutes, a new job cluster created for each hourly execution minimizes the running time and thus the cost, while also fulfilling the requirement for hourly data updates for the business reporting team's dashboards.

NEW QUESTION # 235
A Delta Lake table representing metadata about content from user has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE Based on the above schema, which column is a good candidate for partitioning the Delta Table?

A. Post_time
B. Date
C. User_id
D. Post_id
E. latitude

Answer: B

Explanation:
Partitioning a Delta Lake table improves query performance by organizing data into partitions based on the values of a column. In the given schema, the date column is a good candidate for partitioning for several reasons:
Time-Based Queries: If queries frequently filter or group by date, partitioning by the date column can significantly improve performance by limiting the amount of data scanned. Granularity: The date column likely has a granularity that leads to a reasonable number of partitions (not too many and not too few). This balance is important for optimizing both read and write performance.
Data Skew: Other columns like post_id or user_id might lead to uneven partition sizes (data skew), which can negatively impact performance.
Partitioning by post_time could also be considered, but typically date is preferred due to its more manageable granularity.

NEW QUESTION # 236
A data engineer wants to join a stream of advertisement impressions (when an ad was shown) with another stream of user clicks on advertisements to correlate when impressions led to monetizable clicks.
In the code below, Impressions is a streaming DataFrame with a watermark ("event_time", "10 minutes")

The data engineer notices the query slowing down significantly.
Which solution would improve the performance?

A. Joining on event time constraint: clickTime == impressionTime using a leftOuter join
B. Joining on event time constraint: clickTime >= impressionTime AND clickTime <= impressionTime interval 1 hour
C. Joining on event time constraint: clickTime >= impressionTime - interval 3 hours and removing watermarks
D. Joining on event time constraint: clickTime + 3 hours < impressionTime - 2 hours

Answer: B

NEW QUESTION # 237
A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the marketing group executes the following query:
SELECT * FROM email_ltv
Which statement describes the results returned by this query?

A. Three columns will be returned, but one column will be named "redacted" and contain only null values.
B. The email, age. and ltv columns will be returned with the values in user ltv.
C. Only the email and itv columns will be returned; the email column will contain all null values.
D. Only the email and ltv columns will be returned; the email column will contain the string
"REDACTED" in each row.
E. The email and ltv columns will be returned with the values in user itv.

Answer: D

Explanation:
The code creates a view called email_ltv that selects the email and ltv columns from a table called user_ltv, which has the following schema: email STRING, age INT, ltv INT. The code also uses the CASE WHEN expression to replace the email values with the string "REDACTED" if the user is not a member of the marketing group. The user who executes the query is not a member of the marketing group, so they will only see the email and ltv columns, and the email column will contain the string "REDACTED" in each row.

NEW QUESTION # 238
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