시험준비에가장좋은DEA-C01최신버전인기시험자료덤프데모다운로드

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Snowflake DEA-C01 시험요강:

주제	소개
주제 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.
주제 2	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.
주제 3	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.
주제 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.
주제 5	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.

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최신 SnowPro Advanced DEA-C01 무료샘플문제 (Q326-Q331):

질문 # 326
You have been tasked with migrating an on-premises MySQL database to Amazon Aurora PostgreSQL using AWS Database Migration Service (DMS). The stakeholder emphasizes that the source database must remain fully operational during the migration process.
Which of the following statements about DMS is accurate with respect to this scenario?

A. AWS DMS supports both full-load and continuous replication, allowing the source MySQL database to remain operational during migration.
B. AWS DMS can convert the MySQL database schema directly to PostgreSQL without any manual intervention.
C. When using DMS, the target Amazon Aurora PostgreSQL instance cannot be accessed or queried until the migration is complete.
D. AWS DMS requires the source MySQL database to be version 5.7 or higher for migrating to Amazon Aurora PostgreSQL.
E. AWS DMS only supports full-load migrations, which would require downtime for the source database.

정답：A

질문 # 327
Can Masking policies be applied to virtual columns?

A. TRUE
B. FALSE

정답：B

질문 # 328
A company has an Amazon Redshift data warehouse that users access by using a variety of IAM roles. More than 100 users access the data warehouse every day.
The company wants to control user access to the objects based on each user's job role, permissions, and how sensitive the data is.
Which solution will meet these requirements?

A. Use the column-level security (CLS) feature of Amazon Redshift.
B. Use the role-based access control (RBAC) feature of Amazon Redshift.
C. Use the row-level security (RLS) feature of Amazon Redshift.
D. Use dynamic data masking policies in Amazon Redshift.

정답：B

설명：
Role-based access control (RBAC) in Amazon Redshift enables administrators to define roles based on job functions and then assign these roles to users. RBAC allows for granular permissions management based on job roles, aligning well with the need to control access according to each user's role, permissions, and the sensitivity of data. This approach simplifies managing access for a large number of users by using predefined roles instead of individual permissions.

질문 # 329
A company processes 500 GB of audience and advertising data daily, storing CSV files in Amazon S3 with schemas registered in AWS Glue Data Catalog. They need to convert these files to Apache Parquet format and store them in an S3 bucket.
The solution requires a long-running workflow with 15 GiB memory capacity to process the data concurrently, followed by a correlation process that begins only after the first two processes complete.
Which solution will meet these requirements with the LEAST operational overhead?

A. Use AWS Glue workflows to run the first two processes in parallel. Ensure that the third process starts after the first two processes have finished.
B. Use Amazon EMR to run each process in the workflow. Create an Amazon Simple Queue Service (Amazon SQS) queue to handle messages that indicate the completion of the first two processes. Configure an AWS Lambda function to process the SQS queue by running the third process.
C. Use Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate the workflow by using AWS Glue. Configure AWS Glue to begin the third process after the first two processes have finished.
D. Use AWS Step Functions to orchestrate a workflow that uses multiple AWS Lambda functions.Ensure that the third process starts after the first two processes have finished.

정답：A

설명：
AWS Glue workflows natively orchestrate multiple Glue jobs in parallel and support dependency triggers so the third job starts only after the first two finish. Glue jobs provide the required ~15 GiB memory capacity and easily convert CSV to Parquet using the Glue Data Catalog, delivering a fully managed, low-operations solution.

질문 # 330
An ecommerce company processes millions of orders each day. The company uses AWS Glue ETL to collect data from multiple sources, clean the data, and store the data in an Amazon S3 bucket in CSV format by using the S3 Standard storage class. The company uses the stored data to conduct daily analysis.
The company wants to optimize costs for data storage and retrieval.
Which solution will meet this requirement?

A. Transition the data to Amazon S3 Glacier Flexible Retrieval.
B. Transition the data from Amazon S3 to an Amazon Aurora cluster.
C. Configure AWS Glue ETL to use Amazon EMR to process incoming data in parallel.
D. Configure AWS Glue ETL to transform the incoming data to Apache Parquet format.

정답：D

설명：
Converting the CSV files into Apache Parquet during your Glue ETL jobs dramatically reduces both storage size (because Parquet is a compressed, columnar format) and query cost (because analytics engines only scan the columns you need). This change requires no new infrastructure and pays off immediately for your daily analysis workloads.

질문 # 331
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