최신Databricks Certified Data Engineer Associate - Databricks-Certified-Data-Engineer-Associate무료샘플문제

A. WHERE customer_id <=> :cid

B. WHERE COALESCE(customer_id, '') = COALESCE(:cid, '')

C. WHERE customer_id <> :cid OR customer_id IS NULL

D. WHERE customer_id = :cid

A. CONSTRAINT valid_location EXPECT (location = NULL)

B. CONSTRAINT valid_location EXPECT (location != NULL) ON VIOLATION FAIL UPDATE

C. CONSTRAINT valid_location EXPECT (location != NULL) ON VIOLATION FAIL

D. CONSTRAINT valid_location EXPECT (location != NULL) ON DROP ROW

A. New lead data engineer

B. Workspace administrator

C. Databricks account representative

D. Original data engineer

E. This transfer is not possible

A. USE CATALOG / USE SCHEMA privilege on the schema

B. Schema Owner

C. Table Owner

D. Catalog Owner

A. Database

B. View

C. Table

D. Temporary view

E. Function

A. 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.

B. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.

C. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.

D. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.

A. Deploy a dedicated, manually managed cluster optimized by in-house IT staff.

B. Implement a hybrid approach with scheduled batch jobs on custom cloud VMs.

C. Utilize Databricks serverless compute that automatically optimizes resources and abstracts cluster management.

D. Use an auto-scaling cluster configured and monitored by the user.

A. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.

B. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.

C. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.

D. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.

E. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist until the pipeline is shut down.

A. Store historical data solely for auditing purposes

B. lngest raw data in its original state

C. Validate, clean, and deduplicate data for further processing

D. Aggregate and enrich data for business analytics

A. Automatic data quality checks, built-in support for schema evolution, and declarative pipeline development

B. Supports only batch processing, no data versioning, and high infrastructure costs

C. Requires custom code for data quality checks, no support for streaming data, and complex pipeline maintenance

D. Manual schema enforcement, high operational overhead, and limited scalability

A. In Databricks, audit logs output events in a CSV format.

B. In Databricks, audit logs output events in an XML format.

C. In Databricks, audit logs output events in a plain text format. In Databricks, audit logs output events in a JSON format.

A. SELECT
get_json_object(user_profile, 'user_id') AS user_id,
get_json_object(user_profile, 'name') AS name,
get_json_object(user_profile, 'age') AS age,
get_json_object(user_profile, 'email') AS email
FROM bronze_table;

B. SELECT
from_json(
user_profile,
'user_id STRING, name STRING, age STRING, email STRING'
) AS parsed_profile
FROM bronze_table;

C. SELECT
get_json_object(user_profile, '$.user_id') AS user_id,
get_json_object(user_profile, '$.name') AS name,
CAST(get_json_object(user_profile, '$.age') AS INT) AS age,
get_json_object(user_profile, '$.email') AS email
FROM bronze_table;

D. SELECT
user_profile.user_id AS user_id,
user_profile.name AS name,
CAST(user_profile.age AS INT) AS age,
user_profile.email AS email
FROM bronze_table;

A. The table was managed

B. The table's data was larger than 10 GB

C. The table was external

D. The table's data was smaller than 10 GB

E. The table did not have a location

A. Databricks Connect

B. Lakehouse Federation

C. MLflow

D. Delta Lake

A. View

B. External table

C. Streaming table

D. Materialized view