DEA-C01の無料問題集（132題）、DEA-C01認定試験参考書

Snowflake DEA-C01 問題集

試験コード：DEA-C01

試験名称：SnowPro Advanced: Data Engineer Certification Exam

最近更新時間：2025-01-02

問題と解答：全132問

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無料問題集DEA-C01 資格取得

質問 1：
A lab uses IoT sensors to monitor humidity, temperature, and pressure for a project. The sensors send 100 KB of data every 10 seconds. A downstream process will read the data from an Amazon S3 bucket every 30 seconds.
Which solution will deliver the data to the S3 bucket with the LEAST latency?
A. Use Amazon Kinesis Data Streams to deliver the data to the S3 bucket. Configure the stream to use 5 provisioned shards.
B. Use Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) and Amazon Kinesis Data Firehose to deliver the data to the S3 bucket. Use a 5 second buffer interval for Kinesis Data Firehose.
C. Use Amazon Kinesis Data Streams and Amazon Kinesis Data Firehose to deliver the data to the S3 bucket. Use the default buffer interval for Kinesis Data Firehose.
D. Use Amazon Kinesis Data Streams and call the Kinesis Client Library to deliver the data to the S3 bucket. Use a 5 second buffer interval from an application.
正解：B

質問 2：
A data engineer has a one-time task to read data from objects that are in Apache Parquet format in an Amazon S3 bucket. The data engineer needs to query only one column of the data.
Which solution will meet these requirements with the LEAST operational overhead?
A. Prepare an AWS Glue DataBrew project to consume the S3 objects and to query the required column.
B. Configure an AWS Lambda function to load data from the S3 bucket into a pandas dataframe.
Write a SQL SELECT statement on the dataframe to query the required column.
C. Use S3 Select to write a SQL SELECT statement to retrieve the required column from the S3 objects.
D. Run an AWS Glue crawler on the S3 objects. Use a SQL SELECT statement in Amazon Athena to query the required column.
正解：C
解説: (Topexam メンバーにのみ表示されます)

質問 3：
A company uses Amazon Redshift for its data warehouse. The company must automate refresh schedules for Amazon Redshift materialized views.
Which solution will meet this requirement with the LEAST effort?
A. Use an AWS Lambda user-defined function (UDF) within Amazon Redshift to refresh the materialized views.
B. Use the query editor v2 in Amazon Redshift to refresh the materialized views.
C. Use Apache Airflow to refresh the materialized views.
D. Use an AWS Glue workflow to refresh the materialized views.
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 4：
A healthcare company uses Amazon Kinesis Data Streams to stream real-time health data from wearable devices, hospital equipment, and patient records.
A data engineer needs to find a solution to process the streaming data. The data engineer needs to store the data in an Amazon Redshift Serverless warehouse. The solution must support near real-time analytics of the streaming data and the previous day's data.
Which solution will meet these requirements with the LEAST operational overhead?
A. Load data into Amazon Kinesis Data Firehose. Load the data into Amazon Redshift.
B. Use the streaming ingestion feature of Amazon Redshift.
C. Use the Amazon Aurora zero-ETL integration with Amazon Redshift.
D. Load the data into Amazon S3. Use the COPY command to load the data into Amazon Redshift.
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 5：
A company receives .csv files that contain physical address data. The data is in columns that have the following names: Door_No, Street_Name, City, and Zip_Code. The company wants to create a single column to store these values in the following format:

Which solution will meet this requirement with the LEAST coding effort?
A. Use AWS Glue DataBrew to read the files. Use the NEST_TO_MAP transformation to create the new column.
B. Use AWS Glue DataBrew to read the files. Use the NEST_TO_ARRAY transformation to create the new column.
C. Write a Lambda function in Python to read the files. Use the Python data dictionary type to create the new column.
D. Use AWS Glue DataBrew to read the files. Use the PIVOT transformation to create the new column.
正解：A

質問 6：
A company uses an Amazon QuickSight dashboard to monitor usage of one of the company's applications. The company uses AWS Glue jobs to process data for the dashboard. The company stores the data in a single Amazon S3 bucket. The company adds new data every day.
A data engineer discovers that dashboard queries are becoming slower over time. The data engineer determines that the root cause of the slowing queries is long-running AWS Glue jobs.
Which actions should the data engineer take to improve the performance of the AWS Glue jobs?
(Choose two.)
A. Adjust AWS Glue job scheduling frequency so the jobs run half as many times each day.
B. Convert the AWS Glue schema to the DynamicFrame schema class.
C. Partition the data that is in the S3 bucket. Organize the data by year, month, and day.
D. Increase the AWS Glue instance size by scaling up the worker type.
E. Modify the IAM role that grants access to AWS glue to grant access to all S3 features.
正解：C,D

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Snowflake DEA-C01 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Data Transformation: The SnowPro Advanced: Data Engineer exam evaluates skills in using User-Defined Functions (UDFs), external functions, and stored procedures. It assesses the ability to handle semi-structured data and utilize Snowpark for transformations. This section ensures Snowflake engineers can effectively transform data within Snowflake environments, critical for data manipulation tasks.
トピック 2	Performance Optimization: This topic assesses the ability to optimize and troubleshoot underperforming queries in Snowflake. Candidates must demonstrate knowledge in configuring optimal solutions, utilizing caching, and monitoring data pipelines. It focuses on ensuring engineers can enhance performance based on specific scenarios, crucial for Snowflake Data Engineers and Software Engineers.
トピック 3	Data Movement: Snowflake Data Engineers and Software Engineers are assessed on their proficiency to load, ingest, and troubleshoot data in Snowflake. It evaluates skills in building continuous data pipelines, configuring connectors, and designing data sharing solutions.
トピック 4	Security: The Security topic of the DEA-C01 test covers the principles of Snowflake security, including the management of system roles and data governance. It measures the ability to secure data and ensure compliance with policies, crucial for maintaining secure data environments for Snowflake Data Engineers and Software Engineers.

参照：https://learn.snowflake.com/en/certifications/snowpro-advanced-dataengineer/

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