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

Section	Objectives
Topic 1: 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
Topic 2: Production Pipelines and Orchestration	- Automate ETL pipelines and scheduling - Pipeline reliability and fault tolerance - Build and manage workflows using Databricks Jobs
Topic 3: Data Modeling and Storage	- Delta Lake table design and optimization - Design scalable data lakehouse architectures - Schema evolution and data partitioning strategies
Topic 4: 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 Sample Questions (Q109-Q114):

NEW QUESTION # 109
A Delta Lake table with Change Data Feed (CDF) enabled in the Lakehouse named customer_churn_params 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. The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours. Which approach would simplify the identification of these changed records?

A. Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.
B. Modify the overwrite logic to include a field populated by calling current_timestamp() as data are being written; use this field to identify records written on a particular date.
C. Replace the current overwrite logic with a MERGE statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the Change Data Feed.
D. Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Exact extract: "Change data feed (CDF) provides row-level change information for Delta tables." Exact extract: "Use table_changes to query the set of rows that were inserted, updated, or deleted between two versions (or timestamps)." Exact extract: "MERGE INTO updates and inserts only the rows that changed." Overwriting the table nightly makes it difficult to isolate just the changed rows. With CDF enabled, if you update the table using MERGE so only changed rows are touched, you can read exactly those changed rows from CDF for the last 24 hours and score only them, which is simpler and more efficient.
Reference:

NEW QUESTION # 110
While investigating a data issue, you wanted to review yesterday's version of the table using below command, while querying the previous version of the table using time travel you realized that you are no longer able to view the historical data in the table and you could see it the table was updated yesterday based on the table history(DESCRIBE HISTORY table_name) command what could be the reason why you can not access this data?
SELECT * FROM table_name TIMESTAMP AS OF date_sub(current_date(), 1)

A. Time travel is disabled
B. Time travel must be enabled before you query previous data
C. You currently do not have access to view historical data
D. By default, historical data is cleaned every 180 days in DELTA
E. A command VACUUM table_name RETAIN 0 was ran on the table

Answer: E

Explanation:
Explanation
The answer is, VACUUM table_name RETAIN 0 was ran
The VACUUM command recursively vacuums directories associated with the Delta table and re-moves data files that are no longer in the latest state of the transaction log for the table and are older than a retention threshold. The default is 7 Days.
When VACUUM table_name RETAIN 0 is ran all of the historical versions of data are lost time travel can only provide the current state.

NEW QUESTION # 111
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE.
Three datasets are defined against Delta Lake table sources using LIVE TABLE . The table is configured to
run in Development mode using the Triggered Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after
clicking Start to update the pipeline?

A. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will
persist after the pipeline is stopped to allow for additional testing
B. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to
allow for additional testing
C. All datasets will be updated once and the pipeline will shut down. The compute resources will be
terminated
D. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will
be deployed for the update and terminated when the pipeline is stopped
E. All datasets will be updated continuously and the pipeline will not shut down. The compute resources
will persist with the pipeline

Answer: B

NEW QUESTION # 112
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 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.
B. 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.
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. 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.
E. 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.

Answer: B

Explanation:
This is the correct answer because it meets the requirements of maintaining a full record of all values that have ever been valid in the source system and recreating the current table state with only the most recent value for each record. The code ingests all log information into a bronze table, which preserves the raw CDC data as it is. Then, it uses merge into to perform an upsert operation on a silver table, which means it will insert new records or update or delete existing records based on the change type and the pk_id columns. This way, the silver table will always reflect the current state of the source table, while the bronze table will keep the history of all changes. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Upsert into a table using merge" section.

NEW QUESTION # 113
A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:

A batch job is attempting to insert new records to the table, including a record where latitude = 45.50 and longitude = 212.67.
Which statement describes the outcome of this batch insert?

A. The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.
B. The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.
C. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.
D. The write will fail completely because of the constraint violation and no records will be inserted into the target table.
E. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.

Answer: D

Explanation:
The CHECK constraint is used to ensure that the data inserted into the table meets the specified conditions. In this case, the CHECK constraint is used to ensure that the latitude and longitude values are within the specified range. If the data does not meet the specified conditions, the write operation will fail completely and no records will be inserted into the target table. This is because Delta Lake supports ACID transactions, which means that either all the data is written or none of it is written. Therefore, the batch insert will fail when it encounters a record that violates the constraint, and the target table will not be updated. Reference:
Constraints: https://docs.delta.io/latest/delta-constraints.html
ACID Transactions: https://docs.delta.io/latest/delta-intro.html#acid-transactions

NEW QUESTION # 114
......

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