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

Section	Objectives
Topic 1: Data Ingestion and Transformation	- Handle batch and streaming data pipelines - Ingest data using Apache Spark and Databricks - Transform and clean datasets using Spark SQL and DataFrame APIs
Topic 2: Security, Governance, Monitoring, and Optimization	- Implement Unity Catalog governance and access control - Cost optimization and performance tuning - Monitor and optimize Spark workloads
Topic 3: Production Pipelines and Orchestration	- Automate ETL pipelines and scheduling - Build and manage workflows using Databricks Jobs - Pipeline reliability and fault tolerance
Topic 4: Data Modeling and Storage	- Delta Lake table design and optimization - Schema evolution and data partitioning strategies - Design scalable data lakehouse architectures

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q162-Q167):

NEW QUESTION # 162
A data engineer, while designing a Pandas UDF to process financial time-series data with complex calculations that require maintaining state across rows within each stock symbol group, must ensure the function is efficient and scalable. Which approach will solve the problem with minimum overhead while preserving data integrity?

A. Use a grouped-aggregate Pandas UDF that processes each stock symbol group independently, maintaining state through intermediate aggregation results that get passed between successive UDF calls via broadcast variables.
B. Use applyInPandas on a Spark DataFrame so that each stock symbol group is received as a pandas DataFrame, allowing processing within each group while maintaining state variables local to each group's processing function.
C. Use a scalar_iter Pandas UDF with iterator-based processing, implementing state management through persistent storage (Delta tables) that gets updated after each batch to maintain continuity across iterator chunks.
D. Use a scalar Pandas UDF that processes the entire dataset at once, implementing custom partitioning logic within the UDF to group by stock symbol and maintain state using global variables shared across all executor processes.

Answer: B

Explanation:
applyInPandas is the documented grouped Pandas API for processing each group as a pandas DataFrame.
Spark passes all columns for each group together, which allows per-group state to be maintained naturally inside the function. By contrast, scalar Pandas UDFs are batch-oriented Series-to-Series operations, not group- state processing tools. ( Apache Spark ) This is why option C is the intended best answer among the listed choices. Option A adds unnecessary external persistence overhead, option B relies on unsupported global executor state, and option D misuses grouped aggregation semantics for row-by-row stateful logic. Spark also documents applyInPandas specifically as a grouped operation, while scalar Pandas UDFs process row batches and concatenate results rather than preserving grouped state semantics. ( Apache Spark )
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NEW QUESTION # 163
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?
def new_records():

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

Answer: B

Explanation:
https://docs.databricks.com/en/delta/delta-change-data-feed.html

NEW QUESTION # 164
You are asked to set up an alert to notify in an email every time a KPI indicater increases beyond a threshold value, team also asked you to include the actual value in the alert email notification.

A. Setup an alert but use the default template to notify the message in email's subject
B. Use custom email hook to customize the message
C. Use the webhook destination instead so alert message can be customized
D. Use notebook and python code to run every minute, using python variables to capture send the information in an email
E. Setup an alert but use the custom template to notify the message in email's subject

Answer: E

Explanation:
Explanation
Alerts support custom template supports using variables to customize the default message, set up the query to compare the KPI current value to the threshold and use the variable QUE-RY_RESULT_VALUE to display the value in the email notification.
here is a simple alert, that uses variables in the custom template to present these values in the email notification message, when the alert is fired these variables get replaced with actual values.
Alert with custom template
Graphical user interface, application Description automatically generated

When you enable preview you can see how the alert looks when you substitute the variables.
Graphical user interface, text, application, email Description automatically generated

Below are additional template variables available to you with the custom template.
Alerts | Databricks on AWS
Graphical user interface, text, application, email Description automatically generated

NEW QUESTION # 165
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. 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.
B. 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
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. 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.
E. 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.

Answer: A

Explanation:
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.

NEW QUESTION # 166
A Structured Streaming job deployed to production has been resulting in higher than expected cloud storage costs. At present, during normal execution, each micro-batch of data is processed in less than 3 seconds; at least 12 times per minute, a micro-batch is processed that contains 0 records. The streaming write was configured using the default trigger settings. The production job is currently scheduled alongside many other Databricks jobs in a workspace with instance pools provisioned to reduce start-up time for jobs with batch execution. Holding all other variables constant and assuming records need to be processed in less than 10 minutes, which adjustment will meet the requirement?

A. Set the trigger interval to 500 milliseconds; setting a small but non-zero trigger interval ensures that the source is not queried too frequently.
B. Use the trigger once option and configure a Databricks job to execute the query every 10 minutes; this approach minimizes costs for both compute and storage.
C. Set the trigger interval to 10 minutes; each batch calls APIs in the source storage account, so decreasing trigger frequency to the maximum allowable threshold should minimize this cost.
D. Set the trigger interval to 3 seconds; the default trigger interval is consuming too many records per batch, resulting in spill to disk that can increase volume costs.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Exact extract: "If no trigger is specified, the default processing-time trigger runs micro-batches as fast as possible." Exact extract: "Trigger once processes all available data once and then stops." Exact extract: "Job clusters are created for a job run and terminate when the job completes." The default "as fast as possible" trigger creates many empty micro-batches which repeatedly list/query cloud storage, inflating storage/metadata API costs. Switching to trigger(once=True) and scheduling the job to run every 10 minutes processes all available data in one batch, then stops. This both meets the <10-minute freshness requirement and minimizes compute (cluster can shut down between runs) and storage API calls (one batch per run instead of continual empty batches).
Reference:

NEW QUESTION # 167
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