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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
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
  • 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 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 4
  • 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 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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DEA-C01 Questions & Answers & DEA-C01 Study Guide & DEA-C01 Exam Preparation

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q18-Q23):

NEW QUESTION # 18
A company has an application that is deployed on AWS. The application uses Amazon Simple Notification Service (Amazon SNS) with multiple topics. The company's security team needs to be able to audit all Publish and PublishBatch API actions for all the SNS topics. The company's application team and security team must also be able to query the audit data. The company has already established an event data store in AWS CloudTrail Lake to collect all events.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: A

Explanation:
Amazon SNS Publish and PublishBatch actions are logged by AWS CloudTrail as data events for SNS topics, not as management events. Because the company already has an event data store in CloudTrail Lake, the lowest-overhead solution is to enable SNS data events for the topics and use CloudTrail Lake directly to query the audit records.


NEW QUESTION # 19
A data engineer is building a data pipeline on AWS by using AWS Glue extract, transform, and load (ETL) jobs. The data engineer needs to process data from Amazon RDS and MongoDB, perform transformations, and load the transformed data into Amazon Redshift for analytics. The data updates must occur every hour.
Which combination of tasks will meet these requirements with the LEAST operational overhead?
(Choose two.)

Answer: A,B

Explanation:
AWS Glue triggers provide a simple and integrated way to schedule ETL jobs. By configuring these triggers to run hourly, the data engineer can ensure that the data processing and updates occur as required without the need for external scheduling tools or custom scripts. This approach is directly integrated with AWS Glue, reducing the complexity and operational overhead.
AWS Glue supports connections to various data sources, including Amazon RDS and MongoDB.
By using AWS Glue connections, the data engineer can easily configure and manage the connectivity between these data sources and Amazon Redshift. This method leverages AWS Glue's built-in capabilities for data source integration, thus minimizing operational complexity and ensuring a seamless data flow from the sources to the destination (Amazon Redshift).


NEW QUESTION # 20
A company has used an Amazon Redshift table that is named Orders for 6 months. The company performs weekly updates and deletes on the table. The table has an interleaved sort key on a column that contains AWS Regions.
The company wants to reclaim disk space so that the company will not run out of storage space.
The company also wants to analyze the sort key column.
Which Amazon Redshift command will meet these requirements?

Answer: D


NEW QUESTION # 21
A global bank is using Amazon S3 to store all financial transactions for its customers. Data is stored in multiple S3 buckets. The Security Team wants to perform a one-time assessment to determine if any credit card data is stored in one of Amazon's S3 buckets. The team will be using Amazon Macie to make this discovery.
How sensitive data can be discovered?

Answer: B

Explanation:
Amazon Macie can be used to automatically discover, log, and report sensitive data in Amazon S3 buckets. It can be discovered in any of the two ways, Automated Sensitive Data Discovery Job: This job can be used to get broader visibility of sensitive data stored in Amazon S3 buckets. It evaluates S3 buckets daily and identifies data.
Sensitive Data Discovery Job: It can be used to provide a deeper analysis of sensitive data from specific S3 buckets.
For discovering data in any of the above two options, further fine-tuning of the data discovery can be done in the following ways, Managed Data Identifiers: They use built-in criteria and techniques to identify known sensitive data, such as credit card and passport numbers.
Custom Data Identifier: This method uses custom data identifiers, such as regular expressions (regex), to identify any pattern or character matching the sensitive data.
Access List: This can be used to ignore specific names or characters that should not be considered sensitive data.
Reference:
https://docs.aws.amazon.com/macie/latest/user/data-classification.html


NEW QUESTION # 22
A company uses an Amazon Redshift cluster that runs on RA3 nodes. The company wants to scale read and write capacity to meet demand. A data engineer needs to identify a solution that will turn on concurrency scaling.
Which solution will meet this requirement?

Answer: D

Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/concurrency-scaling-queues.html


NEW QUESTION # 23
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