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Free PDF Databricks - Pass-Sure Databricks-Certified-Professional-Data-Engineer - Databricks Certified Professional Data Engineer Exam Test Pdf

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The Databricks Certified Professional Data Engineer Exam certification exam covers a range of topics, including data ingestion, transformation, and storage, ETL processes, data modeling, and machine learning. Candidates are tested on their ability to use Databricks tools and technologies to solve real-world data engineering problems. Databricks-Certified-Professional-Data-Engineer Exam also evaluates the candidate's understanding of best practices for data engineering, including security, scalability, and cost optimization. By passing the Databricks Certified Professional Data Engineer exam, candidates can demonstrate their proficiency in Databricks data engineering technologies and enhance their job prospects in the field.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q93-Q98):

NEW QUESTION # 93
A data engineer needs to create a database called customer360 at the loca-tion /customer/customer360. The
data engineer is unsure if one of their colleagues has already created the database.
Which of the following commands should the data engineer run to complete this task?

A. CREATE DATABASE customer360 DELTA LOCATION '/customer/customer360';
B. CREATE DATABASE IF NOT EXISTS customer360 LOCATION '/customer/customer360';
C. CREATE DATABASE IF NOT EXISTS customer360 DELTA LOCATION '/customer/customer360';
D. CREATE DATABASE customer360 LOCATION '/customer/customer360';
E. CREATE DATABASE IF NOT EXISTS customer360;

Answer: B

NEW QUESTION # 94
The business intelligence team has a dashboard configured to track various summary metrics for retail stories.
This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?

A. Populate the dashboard by configuring a nightly batch job to save the required to quickly update the dashboard with each query.
B. Define a view against the products_per_order table and define the dashboard against this view.
C. Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.
D. Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.

Answer: A

Explanation:
Given the requirement for daily refresh of data and the need to ensure quick response times for interactive queries while controlling costs, a nightly batch job to pre-compute and save the required summary metrics is the most suitable approach.
* By pre-aggregating data during off-peak hours, the dashboard can serve queries quickly without requiring on-the-fly computation, which can be resource-intensive and slow, especially with many users.
* This approach also limits the cost by avoiding continuous computation throughout the day and instead leverages a batch process that efficiently computes and stores the necessary data.
* The other options (A, C, D) either do not address the cost and performance requirements effectively or are not suitable for the use case of less frequent data refresh and high interactivity.
References:
* Databricks Documentation on Batch Processing: Databricks Batch Processing
* Data Lakehouse Patterns: Data Lakehouse Best Practices

NEW QUESTION # 95
Once a cluster is deleted, below additional actions need to performed by the administrator

A. Remove logs
B. Remove virtual machines but storage and networking are automatically dropped
C. Remove networking but Virtual machines and storage disks are automatically dropped
D. No action needs to be performed. All resources are automatically removed.
E. Drop storage disks but Virtual machines and networking are automatically dropped

Answer: D

Explanation:
Explanation
What is Delta?
Delta lake is
* Open source
* Builds up on standard data format
* Optimized for cloud object storage
* Built for scalable metadata handling
Delta lake is not
* Proprietary technology
* Storage format
* Storage medium
* Database service or data warehouse

NEW QUESTION # 96
A data engineer is configuring a Lakeflow Declarative Pipeline to process CDC (Change Data Capture) data from a source. The source events sometimes arrive out of order, and multiple updates may occur with the same update_timestamp but with different update_sequence_id.
What should the data engineer do to ensure events are sequenced correctly?

A. O Set track_history_column_list to [event_timestamp, event_id] in AUTO CDC APIs.
B. O Use a window function to sort update_sequence_id within the same partition, i.e., update_timestamp in the LDP pipeline.
C. O Use dropDuplicates() to remove out-of-order and duplicate records in LDP.
D. O Use SEQUENCE BY STRUCT(event_timestamp, update_sequence_id) in AUTO CDC APIs.

Answer: D

Explanation:
Comprehensive and Detailed Explanation from Databricks Documentation:
When handling CDC data, sequencing is critical because updates may arrive out of order or multiple changes may occur for the same record at the same timestamp. Databricks' AUTO CDC APIs provide built-in constructs to handle ordering logic.
The correct mechanism is to use the SEQUENCE BY clause in the CDC configuration. Specifically, when both update_timestamp and update_sequence_id exist, the recommended approach is:
SEQUENCE BY STRUCT(event_timestamp, update_sequence_id)
This ensures that within the same record key, the engine applies updates in the exact sequence they occurred, resolving conflicts where multiple updates share the same timestamp but differ in sequence ID.
Option A (track_history_column_list) is used for historical tracking and auditing changes, not for sequencing logic. It ensures lineage but does not enforce correct event order.
Option B (dropDuplicates()) only removes exact duplicates; it cannot guarantee sequencing correctness when multiple updates exist.
Option C is correct: SEQUENCE BY STRUCT(event_timestamp, update_sequence_id) explicitly enforces ordering, as recommended by the CDC pipeline guidelines.
Option D (window function) would be a manual approach in Spark Structured Streaming, but Lakeflow Declarative Pipelines already provide native CDC sequencing support, making this unnecessary.
Thus, the best practice per Databricks CDC documentation is to use Option C with SEQUENCE BY STRUCT.

NEW QUESTION # 97
A junior data engineer has manually configured a series of jobs using the Databricks Jobs UI. Upon reviewing their work, the engineer realizes that they are listed as the "Owner" for each job. They attempt to transfer
"Owner" privileges to the "DevOps" group, but cannot successfully accomplish this task.
Which statement explains what is preventing this privilege transfer?

A. Other than the default "admins" group, only individual users can be granted privileges on jobs.
B. Only workspace administrators can grant "Owner" privileges to a group.
C. Databricks jobs must have exactly one owner; "Owner" privileges cannot be assigned to a group.
D. A user can only transfer job ownership to a group if they are also a member of that group.
E. The creator of a Databricks job will always have "Owner" privileges; this configuration cannot be changed.

Answer: C

Explanation:
The reason why the junior data engineer cannot transfer "Owner" privileges to the "DevOps" group is that Databricks jobs must have exactly one owner, and the owner must be an individual user, not a group. A job cannot have more than one owner, and a job cannot have a group as an owner. The owner of a job is the user who created the job, or the user who was assigned the ownership by another user. The owner of a job has the highest level of permission on the job, and can grant or revoke permissions to other users or groups. However, the owner cannot transfer the ownership to a group, only to another user. Therefore, the junior data engineer's attempt to transfer "Owner" privileges to the "DevOps" group is not possible. References:
* Jobs access control: https://docs.databricks.com/security/access-control/table-acls/index.html
* Job permissions: https://docs.databricks.com/security/access-control/table-acls/privileges.html#job- permissions

NEW QUESTION # 98
......

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