検証済みのDatabricks-Certified-Data-Engineer-Associateテスト問題集と解答で正確な156問 [Q38-Q62]

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検証済みのDatabricks-Certified-Data-Engineer-Associateテスト問題集と解答で正確な156問

Databricks Databricks-Certified-Data-Engineer-AssociateテストエンジンPDFで全問無料問題集

GAQM Databricks-Certified-Data-Engineer-Associate（Databricks Certified Data Engineer Associate）認定試験は、データの専門家にとって高度に尊敬される認定です。この試験では、DataBricksを使用してデータパイプラインの設計、構築、維持における個人の能力を測定します。この認証は、データエンジニアリングの基礎、データビックのアーキテクチャ、データモデリング、データ処理など、幅広いトピックをカバーしています。

GAQM Databricks-Certified-Data-Engineer-Associate試験は、Databricksを使用してデータパイプラインを構築・維持するための候補者の熟練度を検証する認定です。 Databricksは、Apache Sparkを使用して大量のデータを簡単に処理できるクラウドベースのデータプラットフォームです。この認定は、スケーラブルで信頼性が高く、パフォーマンスが優れたデータパイプラインを構築することで専門知識を証明したいデータエンジニアを対象としています。

質問38、 Which of the following tools is used by Auto Loader process data incrementally?

Checkpointing

Spark Structured Streaming

Data Explorer

Unity Catalog

Databricks SQL

質問39、 A data engineering team has two tables. The first table march_transactions is a collection of all retail transactions in the month of March. The second table april_transactions is a collection of all retail transactions in the month of April. There are no duplicate records between the tables.
Which of the following commands should be run to create a new table all_transactions that contains all records from march_transactions and april_transactions without duplicate records?

CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INNER JOIN SELECT * FROM april_transactions;

CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
UNION SELECT * FROM april_transactions;

CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
OUTER JOIN SELECT * FROM april_transactions;

CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INTERSECT SELECT * from april_transactions;

CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
MERGE SELECT * FROM april_transactions;

質問40、 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?

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

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

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.

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

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

質問41、 A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also used by other processes. As a result, the files should be kept as is and will accumulate in the directory. The data engineer needs to identify which files are new since the previous run in the pipeline, and set up the pipeline to only ingest those new files with each run.
Which of the following tools can the data engineer use to solve this problem?

Unity Catalog

Data Explorer

Databricks SQL

Delta Lake

Auto Loader

質問42、 A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The cade block used by the data engineer is below:

If the data engineer only wants the query to execute a micro-batch to process data every 5 seconds, which of the following lines of code should the data engineer use to fill in the blank?

trigger(“5 seconds”)

trigger()

trigger(once=”5 seconds”)

trigger(processingTime=”5 seconds”)

trigger(continuous=”5 seconds”)

質問43、 A data engineer is tasked with building a nightly batch ETL pipeline that processes very large volumes of raw JSON logs from a data lake into Delta tables for reporting. The data arrives in bulk once per day, and the pipeline takes several hours to complete. Cost efficiency is important, but performance and reliability of completing the pipeline are the highest priorities.
Which type of Databricks cluster should the data engineer configure?

A lightweight single-node cluster with low worker node count to reduce costs.

A high-concurrency cluster designed for interactive SQL workloads.

An all-purpose cluster always kept running to ensure low-latency job startup times.

A job cluster configured to autoscale across multiple workers during the pipeline run.

質問44、 A data engineer is attempting to drop a Spark SQL table my_table and runs the following command:
DROP TABLE IF EXISTS my_table;
After running this command, the engineer notices that the data files and metadata files have been deleted from the file system.
Which of the following describes why all of these files were deleted?

The table was managed

The table’s data was smaller than 10 GB

The table’s data was larger than 10 GB

The table was external

The table did not have a location

質問45、 A data engineer is running code in a Databricks Repo that is cloned from a central Git repository. A colleague of the data engineer informs them that changes have been made and synced to the central Git repository. The data engineer now needs to sync their Databricks Repo to get the changes from the central Git repository.
Which of the following Git operations does the data engineer need to run to accomplish this task?

Merge

Push

Pull

Commit

Clone

質問46、 An engineering manager uses a Databricks SQL query to monitor ingestion latency for each data source. The manager checks the results of the query every day, but they are manually rerunning the query each day and waiting for the results.
Which of the following approaches can the manager use to ensure the results of the query are updated each day?

They can schedule the query to refresh every 1 day from the SQL endpoint’s page in Databricks SQL.

They can schedule the query to refresh every 12 hours from the SQL endpoint’s page in Databricks SQL.

They can schedule the query to refresh every 1 day from the query’s page in Databricks SQL.

They can schedule the query to run every 1 day from the Jobs UI.

They can schedule the query to run every 12 hours from the Jobs UI.

質問47、 A data engineer needs to apply custom logic to string column city in table stores for a specific use case. In order to apply this custom logic at scale, the data engineer wants to create a SQL user-defined function (UDF).
Which of the following code blocks creates this SQL UDF?

質問48、 Which of the following approaches should be used to send the Databricks Job owner an email in the case that the Job fails?

Manually programming in an alert system in each cell of the Notebook

Setting up an Alert in the Job page

Setting up an Alert in the Notebook

There is no way to notify the Job owner in the case of Job failure

MLflow Model Registry Webhooks

質問49、 A data engineer needs to combine sales data from an on-premises PostgreSQL database with customer data in Azure Synapse for a comprehensive report. The goal is to avoid data duplication and ensure up-to-date information How should the data engineer achieve this using Databricks?

Develop custom ETL pipelines to ingest data into Databricks

Use Lakehouse Federation to query both data sources directly

Manually synchronize data from both sources into a single database

Export data from both sources to CSV files and upload them to Databricks

質問50、 A data engineer needs access to a table new_table, but they do not have the correct permissions. They can ask the table owner for permission, but they do not know who the table owner is.
Which of the following approaches can be used to identify the owner of new_table?

Review the Permissions tab in the table’s page in Data Explorer

All of these options can be used to identify the owner of the table

Review the Owner field in the table’s page in Data Explorer

Review the Owner field in the table’s page in the cloud storage solution

There is no way to identify the owner of the table

質問51、 A data engineer and data analyst are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL. The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables.
Which of the following changes will need to be made to the pipeline when migrating to Delta Live Tables?

None of these changes will need to be made

The pipeline will need to stop using the medallion-based multi-hop architecture

The pipeline will need to be written entirely in SQL

The pipeline will need to use a batch source in place of a streaming source

The pipeline will need to be written entirely in Python

質問52、 A data engineer has realized that they made a mistake when making a daily update to a table. They need to use Delta time travel to restore the table to a version that is 3 days old. However, when the data engineer attempts to time travel to the older version, they are unable to restore the data because the data files have been deleted.
Which of the following explains why the data files are no longer present?

The VACUUM command was run on the table

The TIME TRAVEL command was run on the table

The DELETE HISTORY command was run on the table

The OPTIMIZE command was nun on the table

The HISTORY command was run on the table

質問53、 A data engineering team has noticed that their Databricks SQL queries are running too slowly when they are submitted to a non-running SQL endpoint. The data engineering team wants this issue to be resolved.
Which of the following approaches can the team use to reduce the time it takes to return results in this scenario?

They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to
“Reliability Optimized.”

They can turn on the Auto Stop feature for the SQL endpoint.

They can increase the cluster size of the SQL endpoint.

They can turn on the Serverless feature for the SQL endpoint.

They can increase the maximum bound of the SQL endpoint’s scaling range

質問54、 A new data engineering team team has been assigned to an ELT project. The new data engineering team will need full privileges on the table sales to fully manage the project.
Which of the following commands can be used to grant full permissions on the database to the new data engineering team?

GRANT ALL PRIVILEGES ON TABLE sales TO team;

GRANT SELECT CREATE MODIFY ON TABLE sales TO team;

GRANT SELECT ON TABLE sales TO team;

GRANT USAGE ON TABLE sales TO team;

GRANT ALL PRIVILEGES ON TABLE team TO sales;

質問55、 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 Development 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?

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

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.

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

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

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

質問56、 A data analyst has created a Delta table sales that is used by the entire data analysis team. They want help from the data engineering team to implement a series of tests to ensure the data is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following commands could the data engineering team use to access sales in PySpark?

spark.delta.table(“sales”)

spark.sql(“sales”)

spark.table(“sales”)

SELECT * FROM sales

There is no way to share data between PySpark and SQL.

質問57、 Which of the following data lakehouse features results in improved data quality over a traditional data lake?

A data lakehouse provides storage solutions for structured and unstructured data.

A data lakehouse supports ACID-compliant transactions.

A data lakehouse allows the use of SQL queries to examine data.

A data lakehouse stores data in open formats.

A data lakehouse enables machine learning and artificial Intelligence workloads.

質問58、 A data organization leader is upset about the data analysis team’s reports being different from the data engineering team’s reports. The leader believes the siloed nature of their organization’s data engineering and data analysis architectures is to blame.
Which of the following describes how a data lakehouse could alleviate this issue?

Both teams would autoscale their work as data size evolves

Both teams would use the same source of truth for their work

Both teams would reorganize to report to the same department

Both teams would be able to collaborate on projects in real-time

Both teams would respond more quickly to ad-hoc requests

質問59、 Which of the following data workloads will utilize a Gold table as its source?

A job that enriches data by parsing its timestamps into a human-readable format

A job that aggregates uncleaned data to create standard summary statistics

A job that cleans data by removing malformatted records

A job that queries aggregated data designed to feed into a dashboard

A job that ingests raw data from a streaming source into the Lakehouse

質問60、 A data engineer has a single-task Job that runs each morning before they begin working. After identifying an upstream data issue, they need to set up another task to run a new notebook prior to the original task.
Which of the following approaches can the data engineer use to set up the new task?

They can clone the existing task in the existing Job and update it to run the new notebook.

They can create a new task in the existing Job and then add it as a dependency of the original task.

They can create a new task in the existing Job and then add the original task as a dependency of the new task.

They can create a new job from scratch and add both tasks to run concurrently.

They can clone the existing task to a new Job and then edit it to run the new notebook.

質問61、 A data engineer needs to ingest from both streaming and batch sources for a firm that relies on highly accurate data. Occasionally, some of the data picked up by the sensors that provide a streaming input are outside the expected parameters. If this occurs, the data must be dropped, but the stream should not fail.
Which feature of Delta Live Tables meets this requirement?

Monitoring

Change Data Capture

Expectations

Error Handling

質問62、 A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.

The code block used by the data engineer is below:
Which line of code should the data engineer use to fill in the blank if the data engineer only wants the query to execute a micro-batch to process data every 5 seconds?

trigger(“5 seconds”)

trigger(continuous=”5 seconds”)

trigger(once=”5 seconds”)

trigger(processingTime=”5 seconds”)

GAQM Databricks-Certified-Data-Engineer-Associate（Databricks認定データエンジニアアソシエイト）試験は、Databricksを使用してビッグデータ処理システムを構築および管理する専門家のスキルを検証するために設計されています。Databricksは、ビッグデータワークロードを処理するためのクラウドベースの環境を提供する統合データ分析プラットフォームです。この試験は、データエンジニアリング、データ処理、データストレージ、データ分析など、幅広いトピックをカバーしています。

100%合格率保証付きの素晴らしいDatabricks-Certified-Data-Engineer-Associate試験問題PDF：https://www.goshiken.com/Databricks/Databricks-Certified-Data-Engineer-Associate-mondaishu.html

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検証済みのDatabricks-Certified-Data-Engineer-Associateテスト問題集と解答で正確な156問 [Q38-Q62]

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