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Databricks-Certified-Professional-Data-Engineer Exam Dumps - Databricks Certified Data Engineer Professional Exam

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Question # 4

The Databricks workspace administrator has configured interactive clusters for each of the data engineering groups. To control costs, clusters are set to terminate after 30 minutes of inactivity. Each user should be able to execute workloads against their assigned clusters at any time of the day.

Assuming users have been added to a workspace but not granted any permissions, which of the following describes the minimal permissions a user would need to start and attach to an already configured cluster.

A.

"Can Manage" privileges on the required cluster

B.

Workspace Admin privileges, cluster creation allowed. "Can Attach To" privileges on the required cluster

C.

Cluster creation allowed. "Can Attach To" privileges on the required cluster

D.

"Can Restart" privileges on the required cluster

E.

Cluster creation allowed. "Can Restart" privileges on the required cluster

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Question # 5

The Databricks CLI is used to trigger a run of an existing job by passing the job_id parameter. The response indicating the job run request was submitted successfully includes a field run_id. Which statement describes what the number alongside this field represents?

A.

The job_id and number of times the job has been run are concatenated and returned.

B.

The globally unique ID of the newly triggered run.

C.

The job_id is returned in this field.

D.

The number of times the job definition has been run in this workspace.

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Question # 6

An hourly batch job is configured to ingest data files from a cloud object storage container where each batch represent all records produced by the source system in a given hour. The batch job to process these records into the Lakehouse is sufficiently delayed to ensure no late-arriving data is missed. The user_id field represents a unique key for the data, which has the following schema:

user_id BIGINT, username STRING, user_utc STRING, user_region STRING, last_login BIGINT, auto_pay BOOLEAN, last_updated BIGINT

New records are all ingested into a table named account_history which maintains a full record of all data in the same schema as the source. The next table in the system is named account_current and is implemented as a Type 1 table representing the most recent value for each unique user_id.

Assuming there are millions of user accounts and tens of thousands of records processed hourly, which implementation can be used to efficiently update the described account_current table as part of each hourly batch job?

A.

Use Auto Loader to subscribe to new files in the account history directory; configure a Structured Streaminq trigger once job to batch update newly detected files into the account current table.

B.

Overwrite the account current table with each batch using the results of a query against the account history table grouping by user id and filtering for the max value of last updated.

C.

Filter records in account history using the last updated field and the most recent hour processed, as well as the max last iogin by user id write a merge statement to update or insert the most recent value for each user id.

D.

Use Delta Lake version history to get the difference between the latest version of account history and one version prior, then write these records to account current.

E.

Filter records in account history using the last updated field and the most recent hour processed, making sure to deduplicate on username; write a merge statement to update or insert the

most recent value for each username.

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Question # 7

A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.

Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.

Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?

A.

Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.

B.

Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.

C.

Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.

D.

Both commands will fail. No new variables, tables, or views will be created.

E.

Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.

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Question # 8

A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.

The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.

The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.

Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

A.

Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.

B.

Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.

C.

Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.

D.

By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.

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