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

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.

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.

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

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.  

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

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

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.

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

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

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.