Independence Day Limited Time 65% Discount Offer - Ends in 0d 00h 00m 00s - Coupon code: 65pass65

Good News !!! Databricks-Certified-Professional-Data-Engineer Databricks Certified Data Engineer Professional Exam is now Stable and With Pass Result

Databricks-Certified-Professional-Data-Engineer Practice Exam Questions and Answers

Databricks Certified Data Engineer Professional Exam

Last Update 4 days ago
Total Questions : 96

Databricks Certified Data Engineer Professional Exam is stable now with all latest exam questions are added 4 days ago. Incorporating Databricks-Certified-Professional-Data-Engineer practice exam questions into your study plan is more than just a preparation strategy.

Databricks-Certified-Professional-Data-Engineer exam questions often include scenarios and problem-solving exercises that mirror real-world challenges. Working through Databricks-Certified-Professional-Data-Engineer dumps allows you to practice pacing yourself, ensuring that you can complete all Databricks Certified Data Engineer Professional Exam practice test within the allotted time frame.

Databricks-Certified-Professional-Data-Engineer PDF

Databricks-Certified-Professional-Data-Engineer PDF (Printable)
$42
$119.99

Databricks-Certified-Professional-Data-Engineer Testing Engine

Databricks-Certified-Professional-Data-Engineer PDF (Printable)
$49
$139.99

Databricks-Certified-Professional-Data-Engineer PDF + Testing Engine

Databricks-Certified-Professional-Data-Engineer PDF (Printable)
$61.95
$176.99
Question # 1

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?

Options:

A.  

Task queueing resulting from improper thread pool assignment.

B.  

Spill resulting from attached volume storage being too small.

C.  

Network latency due to some cluster nodes being in different regions from the source data

D.  

Skew caused by more data being assigned to a subset of spark-partitions.

E.  

Credential validation errors while pulling data from an external system.

Discussion 1
Question # 2

The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalidlatitudeandlongitudevalues in theactivity_detailstable have been breaking their ability to use other geolocation processes.

A junior engineer has written the following code to addCHECKconstraints to the Delta Lake table:

Question # 2

A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.

Which statement explains the cause of this failure?

Options:

A.  

Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.

B.  

The activity details table already exists; CHECK constraints can only be added during initial table creation.

C.  

The activity details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.

D.  

The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.

E.  

The current table schema does not contain the field valid coordinates; schema evolution will need to be enabled before altering the table to add a constraint.

Discussion 0
Question # 3

Which statement describes integration testing?

Options:

A.  

Validates interactions between subsystems of your application

B.  

Requires an automated testing framework

C.  

Requires manual intervention

D.  

Validates an application use case

E.  

Validates behavior of individual elements of your application

Discussion 0
Question # 4

Which statement describes Delta Lake Auto Compaction?

Options:

A.  

An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 G

B.  

B.  

Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.

C.  

Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.

D.  

Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.

E.  

An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 M

B.  

Discussion 0
Question # 5

Incorporating unit tests into a PySpark application requires upfront attention to the design of your jobs, or a potentially significant refactoring of existing code.

Which statement describes a main benefit that offset this additional effort?

Options:

A.  

Improves the quality of your data

B.  

Validates a complete use case of your application

C.  

Troubleshooting is easier since all steps are isolated and tested individually

D.  

Yields faster deployment and execution times

E.  

Ensures that all steps interact correctly to achieve the desired end result

Discussion 0
Question # 6

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

Options:

A.  

Cluster: New Job Cluster;

Retries: Unlimited;

Maximum Concurrent Runs: Unlimited

B.  

Cluster: New Job Cluster;

Retries: None;

Maximum Concurrent Runs: 1

C.  

Cluster: Existing All-Purpose Cluster;

Retries: Unlimited;

Maximum Concurrent Runs: 1

D.  

Cluster: Existing All-Purpose Cluster;

Retries: Unlimited;

Maximum Concurrent Runs: 1

E.  

Cluster: Existing All-Purpose Cluster;

Retries: None;

Maximum Concurrent Runs: 1

Discussion 0
Question # 7

A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, usingdisplay()calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.

Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

Options:

A.  

Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.

B.  

The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.

C.  

Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.

D.  

Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.

E.  

The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.

Discussion 0
Question # 8

Each configuration below is identical to the extent that each cluster has 400 GB total of RAM, 160 total cores and only one Executor per VM.

Given a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?

Options:

A.  

• Total VMs; 1

• 400 GB per Executor

• 160 Cores / Executor

B.  

• Total VMs: 8

• 50 GB per Executor

• 20 Cores / Executor

C.  

• Total VMs: 4

• 100 GB per Executor

• 40 Cores/Executor

D.  

• Total VMs:2

• 200 GB per Executor

• 80 Cores / Executor

Discussion 0
Question # 9

Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

Options:

A.  

spark.sql.files.maxPartitionBytes

B.  

spark.sql.autoBroadcastJoinThreshold

C.  

spark.sql.files.openCostInBytes

D.  

spark.sql.adaptive.coalescePartitions.minPartitionNum

E.  

spark.sql.adaptive.advisoryPartitionSizeInBytes

Discussion 0
Question # 10

A table nameduser_ltvis being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.

Theuser_ltvtable has the following schema:

email STRING, age INT, ltv INT

The following view definition is executed:

Question # 10

An analyst who is not a member of the marketing group executes the following query:

SELECT * FROM email_ltv

Which statement describes the results returned by this query?

Options:

A.  

Three columns will be returned, but one column will be named "redacted" and contain only null values.

B.  

Only the email and itv columns will be returned; the email column will contain all null values.

C.  

The email and ltv columns will be returned with the values in user itv.

D.  

The email, age. and ltv columns will be returned with the values in user ltv.

E.  

Only the email and ltv columns will be returned; the email column will contain the string "REDACTED" in each row.

Discussion 0
Get Databricks-Certified-Professional-Data-Engineer dumps and pass your exam in 24 hours!

Free Exams Sample Questions