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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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.
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	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 5	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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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q97-Q102):

NEW QUESTION # 97
A company has AWS resources in multiple AWS Regions. The company has an Amazon EFS file system in each Region where the company operates. The company's data science team operates within only a single Region. The data that the data science team works with must remain within the team's Region.
A data engineer needs to create a single dataset by processing files that are in each of the company's Regional EFS file systems. The data engineer wants to use an AWS Step Functions state machine to orchestrate AWS Lambda functions to process the data.
Which solution will meet these requirements with the LEAST effort?

A. Deploy the Lambda functions to each Region. Mount the Regional EFS file systems to the Lambda functions. Use the Lambda functions to process the data. Store the output in an Amazon S3 bucket in the data science team's Region.
B. Use AWS DataSync to transfer files from each of the Regional EFS files systems to the file system that is in the data science team's Region.Configure the Lambda functions in the data science team's Region to mount the file system that is in the same Region. Use the Lambda functions to process the data.
C. Configure each of the Regional EFS file systems to replicate data to the data science team's Region. In the data science team's Region, configure the Lambda functions to mount the replica file systems. Use the Lambda functions to process the data.
D. Peer the VPCs that host the EFS file systems in each Region with the VPC that is in the data science team's Region. Enable EFS file locking.
Configure the Lambda functions in the data science team's Region to mount each of the Region specific file systems. Use the Lambda functions to process the data.

Answer: B

Explanation:
AWS DataSync is designed for transferring data between AWS storage services across regions, such as EFS, S3, and other storage systems. By using DataSync, you can efficiently transfer data from each of the Regional EFS file systems to the EFS file system in the data science team's Region.
This allows the data to remain within the data science team's Region while being processed, which meets the requirement that data cannot leave the Region.
Once the data is transferred, Lambda functions in the same Region as the data science team can mount the local EFS file system and process the data, minimizing complexity and the risk of network-related latency or failures from cross-region data processing.
This approach ensures minimal operational effort because DataSync automates the transfer process and works seamlessly with EFS, while keeping the data localized for processing by Lambda functions in the same Region.

NEW QUESTION # 98
A retail company stores customer data in an Amazon S3 bucket. Some of the customer data contains personally identifiable information (PII) about customers. The company must not share PII data with business partners.
A data engineer must determine whether a dataset contains PII before making objects in the dataset available to business partners.
Which solution will meet this requirement with the LEAST manual intervention?

A. Create an AWS Lambda function to identify PII in S3 objects. Schedule the function to run periodically.
B. Configure AWS CloudTrail to monitor S3 PUT operations. Inspect the CloudTrail trails to identify operations that save PII.
C. Create a table in AWS Glue Data Catalog. Write custom SQL queries to identify PII in the table.Use Amazon Athena to run the queries.
D. Configure the S3 bucket and S3 objects to allow access to Amazon Macie. Use automated sensitive data discovery in Macie.

Answer: D

Explanation:
Amazon Macie is a fully managed data security service that automatically discovers, classifies, and protects sensitive data such as personally identifiable information (PII) in Amazon S3.
Macie's automated sensitive data discovery feature can scan S3 objects for PII without requiring manual intervention, making it the most efficient and automated solution for identifying sensitive data before sharing it with business partners.
CloudTrail is useful for monitoring API calls but is not designed to inspect the contents of S3 objects for PII. This would require manual effort to inspect the data saved in S3, which is inefficient.
While Lambda could be used to scan for PII, it would require custom code to identify sensitive data. This approach increases manual effort for development and maintenance compared to using a fully managed service like Macie.
Writing custom SQL queries to identify PII is a manual process and requires the creation and management of Glue tables and Athena queries. This approach adds complexity and manual intervention compared to the automated capabilities of Macie.

NEW QUESTION # 99
A company needs a solution to process streaming data by using Apache Spark in a Kubernetes environment. The solution must support event-driven scaling and optimize resource utilization.
The company needs to integrate the solution with existing Kubernetes infrastructure deployed on Amazon Elastic Kubernetes Service (Amazon EKS).
Which solution will meet these requirements with the LEAST operational overhead?

A. Use Amazon EMR on Amazon EKS with the Kubernetes Cluster Autoscaler to scale Spark applications based on metrics events generated by the Kubernetes Metrics Server.
B. Implement Apache Spark on Amazon EKS with AWS Auto Scaling groups and custom scaling policies based on metrics events collected by the Kubernetes Metrics Server.
C. Use Amazon EMR on Amazon EKS with Kubernetes Event-Driven Autoscaling (KEDA) for event- driven scaling of Spark applications.
D. Deploy self-managed Apache Spark on Amazon EKS by using custom auto-scaling policies based on Kubernetes Event-Driven Autoscaling (KEDA) operators.

Answer: C

Explanation:
Amazon EMR on EKS is the managed AWS service for running Apache Spark on an existing Amazon EKS environment, so it integrates directly with the company's current Kubernetes infrastructure while reducing operational work compared with running Spark yourself. KEDA is specifically designed for event-driven autoscaling in Kubernetes, which satisfies the requirement for event-driven scaling and efficient resource use. This combination provides the managed Spark runtime plus Kubernetes-native event-based scaling with the least operational overhead.
Reference:
https://docs.aws.amazon.com/prescriptive-guidance/latest/patterns/event-driven-auto-scaling-with-eks-pod-identity-and-keda.html

NEW QUESTION # 100
A company has a gaming application that stores data in Amazon DynamoDB tables. A data engineer needs to ingest the game data into an Amazon OpenSearch Service cluster. Data updates must occur in near real time.
Which solution will meet these requirements?

A. Use AWS Step Functions to periodically export data from the Amazon DynamoDB tables to an Amazon S3 bucket. Use an AWS Lambda function to load the data into Amazon OpenSearch Service.
B. Use a custom OpenSearch plugin to sync data from the Amazon DynamoDB tables.
C. Use Amazon DynamoDB Streams to capture table changes. Use an AWS Lambda function to process and update the data in Amazon OpenSearch Service.
D. Configure an AWS Glue job to have a source of Amazon DynamoDB and a destination of Amazon OpenSearch Service to transfer data in near real time.

Answer: C

Explanation:
DynamoDB Streams can capture changes to items in DynamoDB tables (such as inserts, updates, and deletes) in near real-time. An AWS Lambda function can be triggered by these streams, and the function can process the changes and update the data in Amazon OpenSearch Service. This solution meets the requirement for near real-time updates with minimal latency.
The "Use AWS Step Functions to periodically export data from the Amazon DynamoDB tables to an Amazon S3 bucket. Use an AWS Lambda function to load the data into Amazon OpenSearch Service." solution involves exporting data periodically, which does not meet the requirement for near real- time updates. Periodic exports could introduce delays.
AWS Glue is generally used for batch processing, and it is not designed for near real-time data ingestion. It would not be an ideal solution for real-time or low-latency requirements.
The "Use a custom OpenSearch plugin to sync data from the Amazon DynamoDB tables." option suggests building a custom solution, which would involve significant development effort.
Moreover, OpenSearch does not natively support such plugins, making this solution less practical compared to using DynamoDB Streams and Lambda, which are designed to work together.

NEW QUESTION # 101
Pascal, a Data Engineer, have requirement to retrieve the 10 most recent executions of a specified task (completed, still running, or scheduled in the future) scheduled within the last hour, which of the following is the correct SQL Code ?

A. 1.select *
2.from table(information_schema.task_history(
3.scheduled_time_range_start=>dateadd('hour',-1,current_timestamp()),
4.result_limit => 10,
5.task_name=>'MYTASK'));
B. 1.select *
2.from table(information_schema.task_history(
3.scheduled_time_range_start=>dateadd('hour',-1,current_timestamp()),
4.result_limit => 10,query_id IS NOT NULL
5.task_name=>'MYTASK'));
C. 1.select *
2.from table(information_schema.task_history(
3.scheduled_time_range_start=>dateadd('hour',-1,current_timestamp()),
4.result_limit => 11,
5.task_name=>'MYTASK') WHERE query_id IS NOT NULL);
D. 1.select *
2.from table(information_schema.task_history(
3.scheduled_time_range_start=>dateadd('hour',-1,current_timestamp()),
4.result_limit => 10,
5.task_name=>'MYTASK') WHERE query_id IS NOT NULL);

Answer: A

Explanation:
Explanation
To retrieve only tasks that are completed or still running, filter the query using WHERE query_id IS NOT NULL.

NEW QUESTION # 102
......

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