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Databricks-Certified-Data-Engineer-Associate덤프샘플문제 - Databricks-Certified-Data-Engineer-Associate퍼펙트 덤프 최신 데모문제

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최신 Databricks Certification Databricks-Certified-Data-Engineer-Associate 무료샘플문제 (Q218-Q223):

질문 # 218
A dataset has been defined using Delta Live Tables and includes an expectations clause:
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION DROP ROW What is the expected behavior when a batch of data containing data that violates these constraints is processed?

A. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.
B. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
C. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.
D. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
E. Records that violate the expectation cause the job to fail.

정답：D

설명：
Delta Live Tables expectations are optional clauses that apply data quality checks on each record passing through a query. An expectation consists of a description, a boolean statement, and an action to take when a record fails the expectation. The ON VIOLATION clause specifies the action to take, which can be one of the following: warn, drop, or fail. The drop action means that invalid records are dropped from the target dataset before data is written to the target. The failure is reported as a metric for the dataset, which can be viewed by querying the Delta Live Tables event log. The event log contains information such as the number of records that violate an expectation, the number of records dropped, and the number of records written to the target dataset. Reference:
Manage data quality with Delta Live Tables
Monitor Delta Live Tables pipelines
Delta Live Tables SQL language reference

질문 # 219
Which of the following Git operations must be performed outside of Databricks Repos?

A. Push
B. Merge
C. Pull
D. Clone
E. Commit

정답：B

설명：
Explanation
For following tasks, work in your Git provider:
Create a pull request.
Resolve merge conflicts.
Merge or delete branches.
Rebase a branch.
https://docs.databricks.com/repos/index.html

질문 # 220
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE.
Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Production mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

A. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
B. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.
C. 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.
D. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
E. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.

정답：C

질문 # 221
In order for Structured Streaming to reliably track the exact progress of the processing so that it can handle any kind of failure by restarting and/or reprocessing, which of the following two approaches is used by Spark to record the offset range of the data being processed in each trigger?

A. Structured Streaming cannot record the offset range of the data being processed in each trigger.
B. Replayable Sources and Idempotent Sinks
C. Write-ahead Logs and Idempotent Sinks
D. Checkpointing and Write-ahead Logs
E. Checkpointing and Idempotent Sinks

정답：D

설명：
Structured Streaming uses checkpointing and write-ahead logs to record the offset range of the data being processed in each trigger. This ensures that the engine can reliably track the exact progress of the processing and handle any kind of failure by restarting and/or reprocessing. Checkpointing is the mechanism of saving the state of a streaming query to fault-tolerant storage (such as HDFS) so that it can be recovered after a failure. Write-ahead logs are files that record the offset range of the data being processed in each trigger and are written to the checkpoint location before the processing starts. These logs are used to recover the query state and resume processing from the last processed offset range in case of a failure. Reference: Structured Streaming Programming Guide, Fault Tolerance Semantics

질문 # 222
A data engineer needs to provide access to a group named manufacturing-team. The team needs privileges to create tables in the quality schema.
Which set of SQL commands will grant a group named manufacturing-team to create tables in a schema named production with the parent catalog named manufacturing with the least privileges?

A. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
B. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE CATALOG ON CATALOG manufacturing TO manufacturing-team;
C. GRANT USE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;
D. GRANT CREATE TABLE ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT CREATE SCHEMA ON SCHEMA manufacturing.quality TO manufacturing-team; GRANT USE CATALOG ON CATALOG manufacturing TO manufacturing-team;

정답：A

설명：
To create a table within a schema, a principal must have CREATE TABLE on the schema, USE SCHEMA on that schema, and USE CATALOG on the parent catalog. This combination ensures the group has just enough privileges to create objects in that schema without excessive permissions like CREATE SCHEMA or CREATE CATALOG.
Reference Source: Databricks Unity Catalog Privilege Model - "Privileges Required to Create a Table."

질문 # 223
......

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