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Snowflake DEA-C01 Exam Syllabus Topics:
| Topic | Details |
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| Topic 1 | - 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.
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| Topic 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.
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| Topic 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.
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| Topic 4 | - 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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| Topic 5 | - 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.
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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q38-Q43):
NEW QUESTION # 38
A data engineer maintains a materialized view that is based on an Amazon Redshift database.
The view has a column named load_date that stores the date when each row was loaded.
The data engineer needs to reclaim database storage space by deleting all the rows from the materialized view.
Which command will reclaim the MOST database storage space?
- A. VACUUM table_name where load_date<=current_date materializedview
- B. DELETE FROM materialized_view_name where load_date<=current_date
- C. TRUNCATE materialized_view_name
- D. DELETE FROM materialized_view_name where 1=1
Answer: C
NEW QUESTION # 39
An online retail company has an application that runs on Amazon EC2 instances that are in a VPC. The company wants to collect flow logs for the VPC and analyze network traffic.
Which solution will meet these requirements MOST cost-effectively?
- A. Publish flow logs to Amazon CloudWatch Logs. Use an Amazon OpenSearch Service cluster for analytics.
- B. Publish flow logs to Amazon CloudWatch Logs. Use Amazon Athena for analytics.
- C. Publish flow logs to Amazon S3 in Apache Parquet format. Use Amazon Athena for analytics.
- D. Publish flow logs to Amazon S3 in text format. Use Amazon Athena for analytics.
Answer: C
Explanation:
Publishing flow logs to Amazon S3 in Apache Parquet format and using Amazon Athena for analytics (D) is the most cost-effective solution. This approach minimizes storage costs due to the efficient compression of Parquet, and optimizes query performance and cost in Athena due to the reduced data size and optimized columnar storage.
NEW QUESTION # 40
A company needs a solution to store and query product data that has variable attributes. The solution must support unpredictable and high-volume queries with single-digit millisecond latency, even during sudden traffic spikes. The solution must retrieve items by a primary identifier named Product ID. The solution must allow flexible queries by secondary attributes named Category and Brand. Which solution will meet these requirements?
- A. Use an Amazon DynamoDB table with on-demand capacity to store product data. Store products by primary key. Use global secondary indexes (GSIs) to store secondary attributes.
- B. Use Amazon ElastiCache (Redis OSS) and Amazon S3 to store product data. Use Amazon Athena to run flexible secondary attribute queries.
- C. Use an Amazon OpenSearch Serverless cluster with dynamic scaling to store product data. Index product data by primary and secondary attributes.
- D. Use Amazon Aurora with a Multi-AZ deployment to store product data. Use read replicas. Create indexes for primary and secondary attributes.
Answer: A
Explanation:
Amazon DynamoDB with on-demand capacity is designed to handle unpredictable, high-volume request patterns and sudden traffic spikes while maintaining single-digit millisecond latency.
Using Product ID as the table's primary key enables efficient item retrieval by the primary identifier, and global secondary indexes on Category and Brand provide flexible, low-latency queries on those secondary attributes.
NEW QUESTION # 41
A company uses an on-premises Microsoft SQL Server database to store financial transaction data. The company migrates the transaction data from the on-premises database to AWS at the end of each month. The company has noticed that the cost to migrate data from the on-premises database to an Amazon RDS for SQL Server database has increased recently.
The company requires a cost-effective solution to migrate the data to AWS. The solution must cause minimal downtown for the applications that access the database.
Which AWS service should the company use to meet these requirements?
- A. AWS DataSync
- B. AWS Direct Connect
- C. AWS Database Migration Service (AWS DMS)
- D. AWS Lambda
Answer: C
NEW QUESTION # 42
A company needs to optimize storage for an Amazon S3 bucket. Objects older than 1 year must be accessible within 5 hours. All versions of the objects must be retained and immutable for 7 years. All versions of the objects must use the write-once-read-many (WORM) model. Which solution will meet these requirements?
- A. Configure S3 Versioning on the bucket and use the S3 Intelligent-Tiering storage class. Configure a lifecycle policy for the bucket to transition objects that are older than 1 year to S3 Glacier Deep Archive. Configure the policy to deletes objects that are older than 7 years.
- B. Configure S3 Versioning on the bucket and use the S3 Intelligent-Tiering storage class. Configure a lifecycle policy for the bucket to transition objects that are older than 1 year to S3 Glacier Flexible Retrieval. Configure the policy to delete objects that are older than 7 years.
- C. Configure S3 Object Lock on the bucket and use the S3 Intelligent-Tiering storage class.
Configure a lifecycle policy for the bucket to transition objects that are older than 1 year to S3 Glacier Deep Archive. Configure the policy to delete objects that are older than 7 years. - D. Configure S3 Object Lock on the bucket and use the S3 Intelligent-Tiering storage class.
Configure a lifecycle policy for the bucket to transition objects that are older than 1 year to S3 Glacier Flexible Retrieval. Configure the policy to delete objects that are older than 7 years.
Answer: D
Explanation:
S3 Object Lock enforces the write-once-read-many model and ensures that all object versions remain immutable for the required 7-year retention period. Transitioning objects older than 1 year to S3 Glacier Flexible Retrieval satisfies the requirement that data be accessible within 5 hours, as this storage class supports retrieval within minutes to a few hours. The lifecycle policy can expire objects after 7 years, aligning with the retention requirement once the Object Lock retention period has ended.
NEW QUESTION # 43
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