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Snowflake DEA-C01 Exam Syllabus Topics:

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

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Snowflake SnowPro Advanced: Data Engineer Certification Exam 認定 DEA-C01 試験問題 (Q222-Q227):

質問 # 222
A company uses Amazon Redshift for its data warehouse. A data engineer must query a table named orders.complete_orders_history, which contains 100 columns. The query must return all columns except columns named companyId and unique_system_id. Which Amazon Redshift SQL statement will meet this requirement?

正解：B

解説：
Amazon Redshift supports the SELECT * EXCLUDE syntax, which allows returning all columns from a table except the specified ones. This is the most efficient and direct way to exclude specific columns like company_id and unique_system_id without listing all remaining columns.

質問 # 223
A media company uploads large video files to Amazon S3 for processing. After processing, the company needs to keep the original files for 90 days in case the files require reprocessing. After
90 days, the company can delete the files to reduce storage costs. The company stores the processed videos in a different S3 bucket. Which S3 Lifecycle configuration will meet these requirements for the original files MOST cost-effectively?

A. Store the files in S3 Standard for 90 days. Transition the files to S3 Glacier Flexible Retrieval for long-term storage. Then expire the files.
B. Store the files in S3 Intelligent-Tiering for 90 days. Enable versioning. Add S3 Lifecycle management to expire the files.
C. Store the files in S3 Standard for 90 days. Implement S3 Lifecycle management to expire the files.
D. Store the files in S3 Standard for 90 days. Enable versioning. Enable Object Lock on the files for
90 days. Then expire the files.

正解：C

解説：
Keeping the files in S3 Standard for 90 days and using an S3 Lifecycle expiration rule is the most cost-effective option among the choices because the objects only need to be retained for 90 days and then deleted. Adding Glacier Flexible Retrieval would introduce archival and restore behavior intended for longer-term storage, and AWS notes that S3 Glacier Flexible Retrieval is a long-term archival class with a 90-day minimum storage duration. S3 Intelligent-Tiering also adds monitoring and automation charges, and Object Lock/versioning add unnecessary cost and controls beyond the stated requirement.
Reference:
https://docs.aws.amazon.com/AmazonS3/latest/userguide/glacier-storage-classes.html
https://docs.aws.amazon.com/AmazonS3/latest/userguide/lifecycle-expire-general- considerations.html
https://docs.aws.amazon.com/AmazonS3/latest/userguide/intelligent-tiering.html

質問 # 224
Assuming that the session parameter USE_CACHED_RESULT is set to false, what are characteristics of Snowflake virtual warehouses in terms of the use of Snowpark?

A. Creating a DataFrame from a staged file with the read () method will start a virtual warehouse
B. Calling a Snowpark stored procedure to query the database with session, call () will start a virtual warehouse
C. Creating a DataFrame from a table will start a virtual warehouse
D. Transforming a DataFrame with methods like replace () will start a virtual warehouse -

正解：C

解説：
Explanation
Creating a DataFrame from a table will start a virtual warehouse because it requires reading data from Snowflake. The other options will not start a virtual warehouse because they either operate on local data or use an existing session to query Snowflake.

質問 # 225
A company stores employee data in Amazon Resdshift. A table names Employee uses columns named Region ID, Department ID, and Role ID as a compound sort key.
Which queries will MOST increase the speed of query by using a compound sort key of the table?
(Choose two.)

A. Select *from Employee where Department ID=20 and Region ID='North America';
B. Select *from Employee where Region ID='North America';
C. Select *from Employee where Region ID='North America' and Role ID=50;
D. Select *from Employee where Region ID='North America' and Department ID=20;
E. Select *from Employee where Role ID=50;

正解：C、D

解説：
To maximize the speed of queries by using the compound sort key (Region ID, Department ID, and Role ID) in the Employee table in Amazon Redshift, the queries should align with the order of the columns in the sort key.

質問 # 226
When using the CURRENT_ROLE and CURRENT_USER functions with secure UDFs that will be shared with Snowflake accounts, Snowflake returns a NULL value for these functions?

A. TRUE
B. FALSE

正解：A

解説：
Explanation
When using the CURRENT_ROLE and CURRENT_USER functions with secure UDFs that will be shared with Snowflake accounts, Snowflake returns a NULL value for these functions. The rea-son is that the owner of the data being shared does not typically control the users or roles in the ac-count with which the UDF is being shared.

質問 # 227
......

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