Latest Databricks Databricks-Certified-Professional-Data-Engineer Test Vce - Databricks-Certified-Professional-Data-Engineer Dumps

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Databricks-Certified-Professional-Data-Engineer certification exam is a comprehensive test that covers all aspects of data engineering with Databricks. Databricks-Certified-Professional-Data-Engineer exam is designed to test the candidate's knowledge of Databricks architecture, data engineering concepts, data processing with Databricks, and data storage with Databricks. Databricks-Certified-Professional-Data-Engineer Exam also tests the candidate's ability to design, implement, and maintain data engineering solutions using Databricks.

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Databricks is a cloud-based data engineering platform that allows organizations to process large amounts of data quickly and efficiently. The platform leverages Apache Spark to perform data processing tasks and offers a wide range of tools and services to support data engineering workflows. Databricks also provides certification programs for data professionals who want to demonstrate their expertise in using the platform. One of these certifications is the Databricks Certified Professional Data Engineer exam.

Databricks Certified Professional Data Engineer Exam Sample Questions (Q112-Q117):

NEW QUESTION # 112
A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

A. Spill resulting from attached volume storage being too small.
B. Credential validation errors while pulling data from an external system.
C. Task queueing resulting from improper thread pool assignment.
D. Network latency due to some cluster nodes being in different regions from the source data
E. Skew caused by more data being assigned to a subset of spark-partitions.

Answer: E

Explanation:
This is the correct answer because skew is a common situation that causes increased duration of the overall job. Skew occurs when some partitions have more data than others, resulting in uneven distribution of work among tasks and executors. Skew can be caused by various factors, such as skewed data distribution, improper partitioning strategy, or join operations with skewed keys. Skew can lead to performance issues such as long-running tasks, wasted resources, or even task failures due to memory or disk spills. Verified Reference: [Databricks Certified Data Engineer Professional], under "Performance Tuning" section; Databricks Documentation, under "Skew" section.

NEW QUESTION # 113
The data architect has mandated that all tables in the Lakehouse should be configured as external (also known as "unmanaged") Delta Lake tables.
Which approach will ensure that this requirement is met?

A. When configuring an external data warehouse for all table storage, leverage Databricks for all ELT.
B. When the workspace is being configured, make sure that external cloud object storage has been mounted.
C. When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.
D. When data is saved to a table, make sure that a full file path is specified alongside the Delta format.
E. When a database is being created, make sure that the LOCATION keyword is used.

Answer: C

Explanation:
Explanation
To create an external or unmanaged Delta Lake table, you need to use the EXTERNAL keyword in the CREATE TABLE statement. This indicates that the table is not managed by the catalog and the data files are not deleted when the table is dropped. You also need to provide a LOCATION clause to specify the path where the data files are stored. For example:
CREATE EXTERNAL TABLE events ( date DATE, eventId STRING, eventType STRING, data STRING) USING DELTA LOCATION '/mnt/delta/events'; This creates an external Delta Lake table named events that references the data files in the '/mnt/delta/events' path. If you drop this table, the data files will remain intact and you can recreate the table with the same statement.
References:
https://docs.databricks.com/delta/delta-batch.html#create-a-table
https://docs.databricks.com/delta/delta-batch.html#drop-a-table

NEW QUESTION # 114
You are asked to setup two tasks in a databricks job, the first task runs a notebook to download the data from a remote system, and the second task is a DLT pipeline that can process this data, how do you plan to configure this in Jobs UI

A. Add first step in the DLT pipeline and run the DLT pipeline as triggered mode in JOBS UI
B. Jobs UI does not support DTL pipeline, setup the first task using jobs UI and setup the DLT to run in continuous mode.
C. Jobs UI does not support DTL pipeline, setup the first task using jobs UI and setup the DLT to run in trigger mode.
D. Single job cannot have a notebook task and DLT Pipeline task, use two different jobs with linear dependency.
E. Single job can be used to setup both notebook and DLT pipeline, use two different tasks with linear dependency.

Answer: E

Explanation:
Explanation
The answer is Single job can be used to set up both notebook and DLT pipeline, use two different tasks with linear dependency, Here is the JOB UI
1.Create a notebook task
2.Create DLT task
a.add notebook task as dependency
3.Final view
Create the notebook task
Graphical user interface, text, application, email Description automatically generated

DLT task
Graphical user interface, text, application, email Description automatically generated

Final view
Graphical user interface, text, application, PowerPoint Description automatically generated

Bottom of Form
Top of Form

NEW QUESTION # 115
Given the following error traceback (from display(df.select(3* " heartrate " ))) which shows AnalysisException: cannot resolve ' heartrateheartrateheartrate ' , which statement describes the error being raised?

A. There is a type error because a column object cannot be multiplied.
B. There is a type error because a DataFrame object cannot be multiplied.
C. There is a syntax error because the heartrate column is not correctly identified as a column.
D. There is no column in the table named heartrateheartrateheartrate.

Answer: D

Explanation:
* Exact extract: "select() expects column names or Column expressions." References: PySpark DataFrame select; Column expressions and col().

NEW QUESTION # 116
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 once and the pipeline will shut down. The compute resources will persist to allow for additional testing
B. 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
C. All datasets will be updated continuously and the pipeline will not shut down. The compute resources will persist with the pipeline (Correct)
D. 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
E. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated

Answer: C

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 # 117
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