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

Section	Objectives
Topic 1: Security, Governance, Monitoring, and Optimization	- Cost optimization and performance tuning - Monitor and optimize Spark workloads - Implement Unity Catalog governance and access control
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 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 4: Data Modeling and Storage	- Schema evolution and data partitioning strategies - Design scalable data lakehouse architectures - Delta Lake table design and optimization

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q120-Q125):

NEW QUESTION # 120
Which of the following techniques structured streaming uses to ensure recovery of failures during stream processing?

A. Checkpointing and Watermarking
B. The stream will failover to available nodes in the cluster
C. Write ahead logging and watermarking
D. Checkpointing and write-ahead logging
E. Delta time travel
F. Checkpointing and Idempotent sinks

Answer: D

Explanation:
Explanation
The answer is Checkpointing and write-ahead logging.
Structured Streaming uses checkpointing and write-ahead logs to record the offset range of data being processed during each trigger interval.

NEW QUESTION # 121
The data engineering team has configured a job to process customer requests to be forgotten (have their data deleted). All user data that needs to be deleted is stored in Delta Lake tables using default table settings.
The team has decided to process all deletions from the previous week as a batch job at 1am each Sunday. The total duration of this job is less than one hour. Every Monday at 3am, a batch job executes a series ofVACUUMcommands on all Delta Lake tables throughout the organization.
The compliance officer has recently learned about Delta Lake's time travel functionality. They are concerned that this might allow continued access to deleted data.
Assuming all delete logic is correctly implemented, which statement correctly addresses this concern?

A. Because the default data retention threshold is 7 days, data files containing deleted records will be retained until the vacuum job is run 8 days later.
B. Because Delta Lake time travel provides full access to the entire history of a table, deleted records can always be recreated by users with full admin privileges.
C. Because the vacuum command permanently deletes all files containing deleted records, deleted records may be accessible with time travel for around 24 hours.
D. Because Delta Lake's delete statements have ACID guarantees, deleted records will be permanently purged from all storage systems as soon as a delete job completes.
E. Because the default data retention threshold is 24 hours, data files containing deleted records will be retained until the vacuum job is run the following day.

Answer: C

Explanation:
Explanation
This is the correct answer because Delta Lake's delete statements do not physically remove the data files that contain the deleted records, but only mark them as logically deleted in the transaction log. These files are still accessible with time travel until they are permanently deleted by the vacuum command. The default data retention threshold for vacuum is 7 days, but in this case it is overridden by setting it to 24 hours in each vacuum command. Therefore, deleted records may be accessible with time travel for around 24 hours after they are deleted, until they are vacuumed. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; [Databricks Documentation], under "Optimizations - Vacuum" section.

NEW QUESTION # 122
A junior data engineer on your team has implemented the following code block.

The view new_events contains a batch of records with the same schema as the events Delta table. The event_id field serves as a unique key for this table.
When this query is executed, what will happen with new records that have the same event_id as an existing record?

A. They are inserted.
B. They are merged.
C. They are updated.
D. They are deleted.
E. They are ignored.

Answer: E

Explanation:
This is the correct answer because it describes what will happen with new records that have the same event_id as an existing record when the query is executed. The query uses the INSERT INTO command to append new records from the view new_events to the table events. However, the INSERT INTO command does not check for duplicate values in the primary key column (event_id) and does not perform any update or delete operations on existing records. Therefore, if there are new records that have the same event_id as an existing record, they will be ignored and not inserted into the table events. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Append data using INSERT INTO" section.
"If none of the WHEN MATCHED conditions evaluate to true for a source and target row pair that matches the merge_condition, then the target row is left unchanged." https://docs.databricks.com/en/sql/language-manual/delta-merge-into.html#:~:text=If%20none%20of%20the%20WHEN%20MATCHED%20conditions%20evaluate%20to%20true%20for%20a%20source%20and%20target%20row%20pair%20that%20matches%20the%20merge_condition%2C%20then%20the%20target%20row%20is%20left%20unchanged.

NEW QUESTION # 123
A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

A. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
B. Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.
C. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.
D. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.
E. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.

Answer: E

Explanation:
Explanation
The events are that a data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs, and a DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens. The workspace audit logs are logs that record user activities in a Databricks workspace, such as creating, updating, or deleting objects like clusters, jobs, notebooks, or tables. The workspace audit logs also capture the identity of the user who performed each activity, as well as the time and details of the activity. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events in the workspace audit logs. Verified References: [Databricks Certified Data Engineer Professional], under
"Databricks Workspace" section; Databricks Documentation, under "Workspace audit logs" section.

NEW QUESTION # 124
Which of the following approaches can the data engineer use to obtain a version-controllable con-figuration of the Job's schedule and configuration?

A. They can submit the Job once on an all-purpose cluster.
B. They can download the XML description of the Job from the Job's page
C. They can link the Job to notebooks that are a part of a Databricks Repo.
D. They can download the JSON equivalent of the job from the Job's page.
E. They can submit the Job once on a Job cluster.

Answer: A

NEW QUESTION # 125
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