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

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

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q37-Q42):

NEW QUESTION # 37
The Delta Live Table Pipeline is configured to run in Production mode using the continuous Pipe-line Mode.
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 be deployed for the update and terminated when the pipeline is stopped
B. 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
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 continuously and the pipeline will not shut down. The compute resources will persist with the pipeline (Correct)
E. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing

Answer: D

Explanation:
Explanation
The answer is,
All datasets will be updated continuously and the pipeline will not shut down. The compute re-sources will persist with the pipeline until it is shut down since the execution mode is chosen to be continuous. It does not matter if the pipeline mode is development or production, pipeline mode only matters during the pipeline initialization.
DLT pipeline supports two modes Development and Production, you can switch between the two based on the stage of your development and deployment lifecycle.
Development and production modes
Development:
When you run your pipeline in development mode, the Delta Live Tables system:
*Reuses a cluster to avoid the overhead of restarts.
*Disables pipeline retries so you can immediately detect and fix errors.
Production:
In production mode, the Delta Live Tables system:
*Restarts the cluster for specific recoverable errors, including memory leaks and stale cre-dentials.
*Retries execution in the event of specific errors, for example, a failure to start a cluster.
Use the buttons in the Pipelines UI to switch between develop-ment and production modes. By default,

pipelines run in development mode.
Switching between development and production modes only controls cluster and pipeline execution behavior.
Storage locations must be configured as part of pipeline settings and are not affected when switching between modes.
Delta Live Tables supports two different modes of execution:
Triggered pipelines update each table with whatever data is currently available and then stop the cluster running the pipeline. Delta Live Tables automatically analyzes the dependencies between your tables and starts by computing those that read from external sources. Tables within the pipe-line are updated after their dependent data sources have been updated.
Continuous pipelines update tables continuously as input data changes. Once an update is started, it continues to run until manually stopped. Continuous pipelines require an always-running cluster but ensure that downstream consumers have the most up-to-date data Please review additional DLT concepts using the below link
https://docs.databricks.com/data-engineering/delta-live-tables/delta-live-tables-concepts.html#delta-live-tables-c

NEW QUESTION # 38
Two of the most common data locations on Databricks are the DBFS root storage and external object storage mounted with dbutils.fs.mount().
Which of the following statements is correct?

A. Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.
B. DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems.
C. By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.
D. The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.
E. The DBFS root stores files in ephemeral block volumes attached to the driver, while mounted directories will always persist saved data to external storage between sessions.

Answer: B

Explanation:
DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems 1 . DBFS is not a physical file system, but a layer over the object storage that provides a unified view of data across different data sources 1 . By default, the DBFS root is accessible to all users in the workspace, and the access to mounted data sources depends on the permissions of the storage account or container 2 . Mounted storage volumes do not need to have full public read and write permissions, but they do require a valid connection string or access key to be provided when mounting 3
. Both the DBFS root and mounted storage can be accessed when using %sh in a Databricks notebook, as long as the cluster has FUSE enabled 4 . The DBFS root does not store files in ephemeral block volumes attached to the driver, but in the object storage associated with the workspace 1 . Mounted directories will persist saved data to external storage between sessions, unless they are unmounted or deleted 3
. References: DBFS , Work with files on Azure Databricks , Mounting cloud object storage on Azure Databricks , Access DBFS with FUSE

NEW QUESTION # 39
When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

A. Cluster: Existing All-Purpose Cluster;Retries: None;Maximum Concurrent Runs: 1
B. Cluster: New Job Cluster;Retries: Unlimited;Maximum Concurrent Runs: Unlimited
C. Cluster: Existing All-Purpose Cluster;Retries: Unlimited;Maximum Concurrent Runs: 1
D. Cluster: New Job Cluster;Retries: None;Maximum Concurrent Runs: 1
E. Cluster: New Job Cluster;Retries: Unlimited;Maximum Concurrent Runs: 1

Answer: E

Explanation:
Maximum concurrent runs: Set to 1. There must be only one instance of each query concurrently active. Retries: Set to Unlimited. https://docs.databricks.com/en/structured-streaming/query-recovery.html

NEW QUESTION # 40
The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table namedusers.

Assuming thatuser_idis a unique identifying key and thatdelete_requestscontains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?

A. No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.
B. Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.
C. Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.
D. No; the Delta Lake delete command only provides ACID guarantees when combined with the merge into command.
E. No; the Delta cache may return records from previous versions of the table until the cluster is restarted.

Answer: A

Explanation:
Explanation
The code uses the DELETE FROM command to delete records from the users table that match a condition based on a join with another table called delete_requests, which contains all users that have requested deletion.
The DELETE FROM command deletes records from a Delta Lake table by creating a new version of the table that does not contain the deleted records. However, this does not guarantee that the records to be deleted are no longer accessible, because Delta Lake supports time travel, which allows querying previous versions of the table using a timestamp or version number. Therefore, files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files from physical storage.
Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Delete from a table" section; Databricks Documentation, under "Remove files no longer referenced by a Delta table" section.

NEW QUESTION # 41
A table is registered with the following code:

Bothusersandordersare Delta Lake tables. Which statement describes the results of queryingrecent_orders?

A. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
B. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
C. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
D. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
E. All logic will execute when the table is definedand store the result of joiningtables to the DBFS; this stored data will be returned when the table is queried.

Answer: D

Explanation:
Explanation
This is the correct answer because Delta Lake supports time travel, which allows users to query data as of a specific version or timestamp. The code uses the VERSION AS OF syntax to specify the version of each source table to be used in the join. The result of querying recent_orders will be the same as joining those versions of the source tables at query time. The query will use snapshot isolation, which means it will use a consistent snapshot of the table at the time the query began, regardless of any concurrent updates or deletes.
Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Query an older snapshot of a table (time travel)" section.

NEW QUESTION # 42
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