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Snowflake DEA-C01 Exam Syllabus Topics:
| Topic | Details |
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| Topic 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.
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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 | - 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 4 | - 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 5 | - 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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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q39-Q44):
NEW QUESTION # 39
A company is migrating on-premises workloads to AWS. The company wants to reduce overall operational overhead. The company also wants to explore serverless options.
The company's current workloads use Apache Pig, Apache Oozie, Apache Spark, Apache Hbase, and Apache Flink. The on-premises workloads process petabytes of data in seconds. The company must maintain similar or better performance after the migration to AWS.
Which extract, transform, and load (ETL) service will meet these requirements?
- A. Amazon Redshift
- B. Amazon EMR
- C. AWS Glue
- D. AWS Lambda
Answer: B
Explanation:
Glue is like the more good-looking one, but weaker brother of EMR. So when it's about petabyte scales, let EMR do the work and have Glue stay away from the action.
NEW QUESTION # 40
A company implements a data mesh that has a central governance account. The company needs to catalog all data in the governance account. The governance account uses AWS Lake Formation to centrally share data and grant access permissions.
The company has created a new data product that includes a group of Amazon Redshift Serverless tables. A data engineer needs to share the data product with a marketing team. The marketing team must have access to only a subset of columns. The data engineer needs to share the same data product with a compliance team. The compliance team must have access to a different subset of columns than the marketing team needs access to.
Which combination of steps should the data engineer take to meet these requirements? (Choose two.)
- A. Create views of the tables that need to be shared. Include only the required columns.
- B. Share the Amazon Redshift data share to the Lake Formation catalog in the governance account.
- C. Create an Amazon Redshift data share that includes the tables that need to be shared.
- D. Share the Amazon Redshift data share to the Amazon Redshift Serverless workgroup in the marketing team's account.
- E. Create an Amazon Redshift managed VPC endpoint in the marketing team's account. Grant the marketing team access to the views.
Answer: B,C
NEW QUESTION # 41
A company has a production AWS account that runs company workloads. The company's security team created a security AWS account to store and analyze security logs from the production AWS account. The security logs in the production AWS account are stored in Amazon CloudWatch Logs.
The company needs to use Amazon Kinesis Data Streams to deliver the security logs to the security AWS account.
Which solution will meet these requirements?
- A. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the production AWS account.
- B. Create a destination data stream in the production AWS account. In the security AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the production AWS account.
- C. Create a destination data stream in the security AWS account. Create an IAM role and a trust policy to grant CloudWatch Logs the permission to put data into the stream. Create a subscription filter in the security AWS account.
- D. Create a destination data stream in the production AWS account. In the production AWS account, create an IAM role that has cross-account permissions to Kinesis Data Streams in the security AWS account.
Answer: A
NEW QUESTION # 42
A company needs to store semi-structured transactional data for an application in a database.
The database must be serverless. The application writes the data infrequently, but it reads the data frequently. The application must retrieve the data within milliseconds. Which solution will meet these requirements with the LEAST operational overhead?
- A. Store the data in an Amazon RDS for MySQL cluster. Configure RDS Optimized Reads for the cluster.
- B. Store the data in an Amazon S3 Standard bucket. Enable S3 Transfer Acceleration.
- C. Store the data in an Amazon DynamoDB table. Configure a DynamoDB Accelerator cache.
- D. Store the data in an Amazon S3 Apache Iceberg table. Enable S3 Transfer Acceleration.
Answer: C
Explanation:
Amazon DynamoDB is a fully managed serverless database that is designed for millisecond response times and works well for semi-structured application data. Because the application reads frequently and writes infrequently, adding DynamoDB Accelerator (DAX) provides an in- memory cache that further reduces read latency with minimal operational overhead.
NEW QUESTION # 43
Which of the following best describes the type of data found in traditional relational databases?
- A. Structured data
- B. Unstructured data
- C. Semi-structured data
- D. Free-form data
Answer: A
NEW QUESTION # 44
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