認定するDEA-C01模擬問題試験-試験の準備方法-一番優秀なDEA-C01復習資料

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Snowflake DEA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Data Ingestion	25%	- Ingest from various sources: cloud storage, on-premises, APIs, data lakes - Use COPY INTO, external tables, Snowpipe, Snowpipe Streaming - Handle file formats, compression, encoding, and schema detection
Data Governance, Security & Sharing	10%	- Apply access control, masking, encryption, and compliance - Track lineage, audit, and maintain data integrity - Share data securely across accounts and platforms
Data Pipeline Orchestration & Automation	20%	- Build, schedule, monitor, and manage workflows - Integrate with external orchestration tools - Error handling, retry logic, and dependency management
Data Transformation & Processing	30%	- Use streams, tasks, dynamic tables for pipelines and CDC - Work with semi-structured and unstructured data - Design and implement transformations: SQL, stored procedures, user-defined functions - Implement data quality, validation, and schema evolution
Performance Optimization & Scalability	15%	- Design scalable compute and storage solutions - Monitor and tune workloads and resource usage - Optimize warehouses, clustering, partitioning, and query performance

>> DEA-C01模擬問題 <<

更新するDEA-C01模擬問題試験-試験の準備方法-信頼的なDEA-C01復習資料

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Snowflake SnowPro Advanced: Data Engineer Certification Exam 認定 DEA-C01 試験問題 (Q119-Q124):

質問 # 119
A company is developing a log streaming pipeline that uses Amazon Data Firehose. The pipeline streams Amazon CloudWatch Logs data to an Amazon S3 bucket. The company's analytics team needs to use the data in audits. The pipeline must deliver only the relevant logs to the S3 bucket in a compatible format for the team's analysis. Which solution will meet these requirements and maintain reliable performance?

A. Set the S3 bucket rules to allow logs from only specific timestamp ranges. Create an AWS Lambda function that converts the log files to the desired format. Use an S3 trigger to invoke the Lambda function.
B. Create a subscription filter in the CloudWatch Logs log group that uses the Firehose delivery stream as the destination. Create an AWS Lambda function that converts the log files to the desired format. Configure Firehose to invoke the Lambda function.
C. Create a subscription filter in the CloudWatch Logs log group. Configure the filter to monitor the Firehose stream. Create an AWS Lambda function to convert the log files to the desired format.
Configure Firehose to invoke the Lambda function.
D. Tag the CloudWatch Logs log groups that the analytics team needs. Configure Firehose to ingest only the tagged log groups. Configure Firehose to write the output in the desired format.

正解：B

解説：
A CloudWatch Logs subscription filter can send only the relevant log events from the log group to an Amazon Data Firehose delivery stream. Firehose can then invoke an AWS Lambda function to transform the records into the format the analytics team needs before delivering them to Amazon S3. This approach is managed, scalable, and reliable for continuous log delivery.

質問 # 120
A company is using Amazon Redshift to build a data warehouse solution. The company is loading hundreds of files into a fact table that is in a Redshift cluster.
The company wants the data warehouse solution to achieve the greatest possible throughput.
The solution must use cluster resources optimally when the company loads data into the fact table.
Which solution will meet these requirements?

A. Use S3DistCp to load multiple files into Hadoop Distributed File System (HDFS). Use an HDFS connector to ingest the data into the Redshift cluster.
B. Use a single COPY command to load the data into the Redshift cluster.
C. Use multiple COPY commands to load the data into the Redshift cluster.
D. Use a number of INSERT statements equal to the number of Redshift cluster nodes. Load the data in parallel into each node.

正解：B

解説：
https://docs.aws.amazon.com/redshift/latest/dg/t_Loading-data-from-S3.html

質問 # 121
Two data engineering teams use separate AWS accounts. Both teams request access to the same datashare in an Amazon Redshift cluster that is in a third AWS account. The datashare is named salesshare.
A data engineer must use the Amazon Redshift SQL interface to grant both data engineering teams' access to the datashare.
Which command or commands will meet this requirement?

A. GRANT USAGE ON DATASHARE salesshare TO NAMESPACE '<account ID 1>';
GRANT USAGE ON DATASHARE salesshare TO NAMESPACE '<account ID 2>';
B. GRANT USAGE ON DATASHARE salesshare TO NAMESPACES '<account ID 1>' AND
'<account ID 2>';
C. GRANT USAGE ON DATASHARE salesshare TO ACCOUNT '<account ID 1>';
GRANT USAGE ON DATASHARE salesshare TO ACCOUNT '<account ID 2>';
D. GRANT USAGE ON DATASHARE salesshare TO ACCOUNTS '<account ID 1>' AND '<account ID 2>';

正解：A

解説：
In Amazon Redshift data sharing, cross-account permissions for a datashare are granted to consumer cluster/serverless namespaces. Granting USAGE on the datashare to each consumer namespace enables both teams' Redshift environments in their respective accounts to create and query databases from the shared data.

質問 # 122
To view/monitor the clustering metadata for a table, Snowflake provides which of the following system functions?

A. SYSTEM$CLUSTERING_DEPTH_KEY
B. SYSTEM$CLUSTERING_INFORMATION (including clustering depth)
C. SYSTEM$CLUSTERING_KEY_INFORMATION (including clustering depth)
D. SYSTEM$CLUSTERING_DEPTH

正解：B、D

解説：
Explanation
SYSTEM$CLUSTERING_DEPTH:
Computes the average depth of the table according to the specified columns (or the clustering key defined for the table). The average depth of a populated table (i.e. a table containing data) is always 1 or more. The smaller the average depth, the better clustered the table is with regards to the speci-fied columns.
Calculate the clustering depth for a table using two columns in the table:
SELECT SYSTEM$CLUSTERING_DEPTH('TPCH_PRODUCT', '(C2, C9)');
SYSTEM$CLUSTERING_INFORMATION:
Returns clustering information, including average clustering depth, for a table based on one or more columns in the table.
SELECT SYSTEM$CLUSTERING_INFORMATION('SAMPLE_TABLE', '(col1, col3)');

質問 # 123
A company has a data processing pipeline that runs multiple SQL queries in sequence against an Amazon Redshift cluster. The company merges with a second company. The original company modifies a query that aggregates sales revenue data to join sales tables from both companies.
The sales table for the first company is named Table S1. The sales table for the second company is named Table S2. Table S1 contains 10 billion records. Table S2 contains 900 million records.
The query becomes slow after the modification. A data engineer must improve the query performance.
Which solutions will meet these requirements? (Choose two.)

A. Use the EVEN distribution style for Table S1. Use the ALL distribution style for Table S2.
B. Use the KEY distribution style for both sales tables. Select a high cardinality column to use for the join.
C. Use the Amazon Redshift query optimizer to review and select optimizations to implement.
D. Use Amazon Redshift Advisor to review and select optimizations to implement.
E. Use the KEY distribution style for both sales tables. Select a low cardinality column to use for the join.

正解：B、D

解説：
Choosing KEY distribution on both tables with a high-cardinality join column colocates matching rows across nodes and avoids data skew, improving join performance.
Redshift Advisor provides automated, actionable recommendations (e.g., distribution and sort keys, stats) to further optimize the slow query with minimal effort.

質問 # 124
......

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