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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
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.
Topic 2	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.
Topic 3	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.
Topic 4	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.
Topic 5	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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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q49-Q54):

NEW QUESTION # 49
A company is building a data stream processing application. The application runs in an Amazon Elastic Kubernetes Service (Amazon EKS) cluster. The application stores processed data in an Amazon DynamoDB table.
The company needs the application containers in the EKS cluster to have secure access to the DynamoDB table. The company does not want to embed AWS credentials in the containers.
Which solution will meet these requirements?

A. Attach an IAM role to the EKS worker nodes, Grant the IAM role access to DynamoDB. Use the IAM role to set up IAM roles service accounts (IRSA) functionality.
B. Store the AWS credentials in an Amazon S3 bucket. Grant the EKS containers access to the S3 bucket to retrieve the credentials.
C. Create an IAM user that has an access key to access the DynamoDB table. Use Kubernetes secrets that are mounted in a volume of the EKS duster nodes to store the user access key data.
D. Create an IAM user that has an access key to access the DynamoDB table. Use environment variables in the EKS containers to store the IAM user access key data.

Answer: A

Explanation:
IAM roles for service accounts (IRSA) allow assigning fine-grained permissions to EKS pods based on Kubernetes service accounts. This approach ensures that AWS credentials are not hardcoded or stored in environment variables, which enhances security. Instead, the pods running in the EKS cluster assume the IAM role with permissions to access Amazon DynamoDB securely.
This solution meets the requirement of providing secure access without embedding AWS credentials in the containers, while also allowing for more granular access control at the pod level rather than relying on the node-level IAM role.
Storing AWS credentials in S3 is insecure and not a recommended practice. This approach does not meet the security requirement of avoiding embedded AWS credentials.
Storing AWS credentials in environment variables is insecure and not recommended. This exposes credentials within the container, increasing the risk of credential leakage.
Although Kubernetes secrets can provide some protection, storing long-term AWS credentials (access key and secret key) in secrets still presents a security risk. The preferred method is to use temporary credentials via IAM roles (IRSA), which rotate automatically.

NEW QUESTION # 50
A company uses Amazon Redshift for its data warehouse. The company must automate refresh schedules for Amazon Redshift materialized views.
Which solution will meet this requirement with the LEAST effort?

A. Use the query editor v2 in Amazon Redshift to refresh the materialized views.
B. Use Apache Airflow to refresh the materialized views.
C. Use an AWS Glue workflow to refresh the materialized views.
D. Use an AWS Lambda user-defined function (UDF) within Amazon Redshift to refresh the materialized views.

Answer: D

Explanation:
AWS Lambda allows running code in response to triggers without needing to provision or manage servers. However, creating a UDF within Amazon Redshift to call a Lambda function for this purpose involves writing custom code and managing permissions between Lambda and Redshift.

NEW QUESTION # 51
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. Use AWS DataSync to create new gp3 volumes. Transfer the data from the original gp2 volumes to the new gp3 volumes.
B. Create snapshots of the gp2 volumes. Create new gp3 volumes from the snapshots. Attach the new gp3 volumes to the EC2 instances.
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. Change the volume type of the existing gp2 volumes to gp3. Enter new values for volume size, IOPS, and throughput.

Answer: D

NEW QUESTION # 52
A data engineer runs Amazon Athena queries on data that is in an Amazon S3 bucket. The Athena queries use AWS Glue Data Catalog as a metadata table.
The data engineer notices that the Athena query plans are experiencing a performance bottleneck. The data engineer determines that the cause of the performance bottleneck is the large number of partitions that are in the S3 bucket. The data engineer must resolve the performance bottleneck and reduce Athena query planning time.
Which solutions will meet these requirements? (Choose two.)

A. Use Athena partition projection based on the S3 bucket prefix.
B. Create an AWS Glue partition index. Enable partition filtering.
C. Transform the data that is in the S3 bucket to Apache Parquet format.
D. Bucket the data based on a column that the data have in common in a WHERE clause of the user query.
E. Use the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects.

Answer: A,B

Explanation:
https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/ Optimizing Partition Processing using partition projection Processing partition information can be a bottleneck for Athena queries when you have a very large number of partitions and aren't using AWS Glue partition indexing. You can use partition projection in Athena to speed up query processing of highly partitioned tables and automate partition management. Partition projection helps minimize this overhead by allowing you to query partitions by calculating partition information rather than retrieving it from a metastore. It eliminates the need to add partitions' metadata to the AWS Glue table.

NEW QUESTION # 53
An application consumes messages from an Amazon Simple Queue Service (Amazon SQS) queue. The application experiences occasional downtime. As a result of the downtime, messages within the queue expire and are deleted after 1 day. The message deletions cause data loss for the application.
Which solutions will minimize data loss for the application? (Choose two.)

A. Use a delay queue to delay message delivery
B. Attach a dead-letter queue (DLQ) to the SQS queue.
C. Reduce message processing time.
D. Increase the visibility timeout.
E. Increase the message retention period

Answer: B,E

NEW QUESTION # 54
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