完璧なDEA-C01トレーニング資料 &合格スムーズDEA-C01最新テスト |ユニークなDEA-C01専門知識SnowPro Advanced: Data Engineer Certification Exam

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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	Storage and Data Protection: The topic tests the implementation of data recovery features and the understanding of Snowflake's Time Travel and micro-partitions. Engineers are evaluated on their ability to create new environments through cloning and ensure data protection, highlighting essential skills for maintaining Snowflake data integrity and accessibility.
トピック 2	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 3	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 4	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 5	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.

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Snowflake DEA-C01最新テスト & DEA-C01専門知識

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Snowflake SnowPro Advanced: Data Engineer Certification Exam 認定 DEA-C01 試験問題 (Q340-Q345):

質問 # 340
A company stores a large dataset in an Amazon S3 bucket. A data engineer frequently runs complex queries on the dataset by using Amazon Athena. The data engineer needs to optimize query performance and optimize costs for queries that are run multiple times with the same parameters. Which solution will meet these requirements?

A. Configure query result reuse settings in the Athena workgroup.
B. Convert the dataset to JSON format before running Athena queries.
C. Use Amazon Redshift Spectrum to query the data in Amazon S3.
D. Use Amazon EMR to pre-process the data before running Athena queries.

正解：A

解説：
Athena query result reuse allows repeated queries with the same text and parameters to return previously computed results instead of scanning the source data again. This improves performance for repeated queries and reduces query costs by avoiding unnecessary data scans.

質問 # 341
A company is migrating a legacy application to an Amazon S3 based data lake. A data engineer reviewed data that is associated with the legacy application. The data engineer found that the legacy data contained some duplicate information.
The data engineer must identify and remove duplicate information from the legacy application data.
Which solution will meet these requirements with the LEAST operational overhead?

A. Write a custom extract, transform, and load (ETL) job in Python. Use the DataFrame.drop_duplicates() function by importing the Pandas library to perform data deduplication.
B. Write a custom extract, transform, and load (ETL) job in Python. Import the Python dedupe library. Use the dedupe library to perform data deduplication.
C. Write an AWS Glue extract, transform, and load (ETL) job. Import the Python dedupe library. Use the dedupe library to perform data deduplication.
D. Write an AWS Glue extract, transform, and load (ETL) job. Use the FindMatches machine learning (ML) transform to transform the data to perform data deduplication.

正解：D

質問 # 342
A company wants to ingest streaming data into an Amazon Redshift data warehouse from an Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster. A data engineer needs to develop a solution that provides low data access time and that optimizes storage costs.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create an external schema that maps to the MSK cluster. Create a materialized view that references the external schema to consume the streaming data from the MSK topic.
B. Create an Amazon S3 bucket. Ingest the data from Amazon MSK. Create an event-driven AWS Lambda function to load the data from the S3 bucket to a new Amazon Redshift table.
C. Create an external schema that maps to the streaming data source. Create a new Amazon Redshift table that references the external schema.
D. Develop an AWS Glue streaming extract, transform, and load (ETL) job to process the incoming data from Amazon MSK. Load the data into Amazon S3. Use Amazon Redshift Spectrum to read the data from Amazon S3.

正解：D

解説：
By using a serverless Glue streaming job to continuously pull your MSK records, transform them as needed, and land them in Parquet (or another columnar) files in S3, you:
1. Optimize storage costs ?your data sits in S3, where you pay pennies per GB-month and can tier it further with lifecycle rules.
2. Get low-latency access ?Redshift Spectrum lets you query S3-backed tables with millisecond planning time, so freshly landed data becomes queryable almost immediately.
3. Minimize ops overhead ?you don't have to stand up or manage any EC2-based brokers, Lambda polling loops, or custom connector infrastructure.
Glue's managed streaming runtime handles checkpointing, autoscaling, and fault tolerance for you.
Once the data lands in S3, you simply define an external schema in Redshift that points at the Glue Data Catalog database where your streaming job writes tables. Analysts can then query the
"live" dataset via Spectrum as if it were inside Redshift, meeting both your performance and cost goals with minimal operational effort.

質問 # 343
A financial company wants to implement a data mesh. The data mesh must support centralized data governance, data analysis, and data access control. The company has decided to use AWS Glue for data catalogs and extract, transform, and load (ETL) operations.
Which combination of AWS services will implement a data mesh? (Choose two.)

A. Use AWS Glue DataBrew for centralized data governance and access control.
B. Use Amazon RDS for data storage. Use Amazon EMR for data analysis.
C. Use Amazon Aurora for data storage. Use an Amazon Redshift provisioned cluster for data analysis.
D. Use Amazon S3 for data storage. Use Amazon Athena for data analysis.
E. Use AWS Lake Formation for centralized data governance and access control.

正解：D、E

質問 # 344
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. Use the streaming ingestion feature of Amazon Redshift.
B. Load data into Amazon Kinesis Data Firehose. Load the data into 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：A

解説：
https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-streaming-ingestion.html Use the Streaming Ingestion Feature of Amazon Redshift: Amazon Redshift recently introduced streaming data ingestion, allowing Redshift to consume data directly from Kinesis Data Streams in near real-time. This feature simplifies the architecture by eliminating the need for intermediate steps or services, and it is specifically designed to support near real-time analytics. The operational overhead is minimal since the feature is integrated within Redshift.

質問 # 345
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