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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Objectives
Data Engineering Fundamentals on Snowflake	- Snowflake architecture for data engineering 1. Virtual warehouses and compute scaling 2. Storage and compute separation - Data ingestion concepts 1. Continuous ingestion concepts 2. Batch loading strategies
Performance Optimization	- Warehouse optimization 1. Scaling strategies 2. Concurrency management - Query performance tuning 1. Caching mechanisms 2. Clustering and micro-partitions
Data Loading and Integration	- Snowpipe and continuous ingestion 1. Event-driven loading 2. Automated ingestion workflows - Bulk data loading 1. File format handling 2. COPY INTO command usage
Security, Governance, and Data Sharing	- Access control 1. Role-based access control (RBAC) 2. Object-level permissions - Data governance 1. Secure data sharing concepts 2. Data masking policies
Semi-Structured and Advanced Data Handling	- Semi-structured data processing 1. JSON, Avro, Parquet handling 2. VARIANT data type usage - Advanced transformation techniques 1. Complex transformations in SQL 2. Flattening nested data
Data Pipelines and Transformation	- Streams and Tasks 1. Change Data Capture (CDC) with Streams 2. Automating pipelines with Tasks - ETL/ELT design patterns in Snowflake 1. Data orchestration concepts 2. Staging and transformation layers

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DEA-C01合格問題 & DEA-C01模擬問題

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Snowflake SnowPro Advanced: Data Engineer Certification Exam 認定 DEA-C01 試験問題 (Q308-Q313):

質問 # 308
To help manage STAGE storage costs, Data engineer recommended to monitor stage files and re-move them from the stages once the data has been loaded and the files which are no longer needed. Which option he can choose to remove these files either during data loading or afterwards?

A. Files no longer needed, can be removed using the REMOVE command.
B. Script can be used during data loading & post data loading with DELETE command.
C. He can choose to remove stage files during data loading (using the COPY INTO <table> command).
D. Files no longer needed, can be removed using the PURGE=TRUE command.

正解：C、D

解説：
Explanation
Managing Data Files
Staged files can be deleted from a Snowflake stage (user stage, table stage, or named stage) using the following methods:
Files that were loaded successfully can be deleted from the stage during a load by specifying the PURGE copy option in the COPY INTO <table> command.
After the load completes, use the REMOVE command to remove the files in the stage.
Removing files ensures they aren't inadvertently loaded again. It also improves load performance, because it reduces the number of files that COPY commands must scan to verify whether existing files in a stage were loaded already.

質問 # 309
A global ecommerce company processes customer transactions, inventory updates, and user activity logs across multiple AWS services. The company needs a scalable, fully managed, and event-driven orchestration solution to coordinate complex extract, transform, and load (ETL) workflows. The solution must use AWS Glue and Amazon EMR to process data. The data will be stored in Amazon Redshift and Amazon S3. The solution must support dependency management, automated retries, and data pipeline monitoring. Which solution will meet these requirements?

A. Create AWS Lambda functions for each step of the workflow Configure Amazon EventBridge to invoke AWS Glue jobs. Configure the Lambda functions to process and move data through the pipeline.
B. Use AWS Step Functions to define an express workflow that invokes the data transformation and loading tasks across Amazon EMR and AWS Glue.
C. Create an AWS Lambda function that runs each step of the workflow. Create an Amazon EventBridge scheduled rule to invoke the function every day.
D. Use Apache Airflow on Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to create Directed Acyclic Graphs (DAGs) to manage ETL workflows.

正解：D

解説：
Amazon Managed Workflows for Apache Airflow is a fully managed orchestration service built specifically for coordinating complex ETL workflows with dependencies, retries, scheduling, and monitoring. Airflow DAGs can natively orchestrate AWS Glue and Amazon EMR jobs and manage data movement into Amazon Redshift and Amazon S3, making it the best fit for a scalable, event-driven ETL orchestration requirement.

質問 # 310
A company is planning to upgrade its Amazon Elastic Block Store (Amazon EBS) General Purpose SSD storage from gp2 to gp3. The company wants to prevent any interruptions in its Amazon EC2 instances that will cause data loss during the migration to the upgraded storage.
Which solution will meet these requirements with the LEAST operational overhead?

A. Change the volume type of the existing gp2 volumes to gp3. Enter new values for volume size, IOPS, and throughput.
B. Use AWS DataSync to create new gp3 volumes. Transfer the data from the original gp2 volumes to the new gp3 volumes.
C. Create new gp3 volumes. Gradually transfer the data to the new gp3 volumes. When the transfer is complete, mount the new gp3 volumes to the EC2 instances to replace the gp2 volumes.
D. Create snapshots of the gp2 volumes. Create new gp3 volumes from the snapshots. Attach the new gp3 volumes to the EC2 instances.

正解：A

質問 # 311
A company uses an Amazon S3 Standard bucket to maintain a self-managed transactional data lake that uses Apache Iceberg tables. The data lake ingests data both in real time and in batches.
Users report slow performance for real-time tables. A data engineer reviews the real-time tables and notices that the tables are made up of many small data files The data engineer must improve the performance of the real-time tables.
Which solution will meet this requirement?

A. Delete S3 objects that are not linked from the Iceberg table.
B. Archive historic snapshots.
C. Expire historic snapshots.
D. Apply compaction.

正解：D

解説：
Compaction merges many small data files into fewer, larger files, which reduces file count and metadata overhead in Apache Iceberg tables, directly improving query performance for real-time workloads.

質問 # 312
A company maintains a central Amazon Redshift data warehouse that aggregates daily transactional data from Amazon RDS for PostgreSQL and Amazon Aurora MySQL. A data engineer notices that some complex transformation queries take hours to finish. The data engineer wants to optimize query performance to reduce query execution time as much as possible.
Which solution will meet this requirement?

A. Use materialized views in Amazon Redshift for frequently queried data patterns.
B. Increase the concurrency scaling quota for the Redshift cluster.
C. Export the tables to an Amazon S3 bucket. Use Amazon Athena to query the data in the bucket.
D. Use Amazon Redshift Spectrum to create external tables based on the Redshift tables.

正解：A

解説：
Amazon Redshift materialized views precompute and store results of complex queries, allowing subsequent queries to run much faster by reading from the precomputed data. This significantly reduces execution time for frequently used transformation patterns.

質問 # 313
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