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Snowflake DEA-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Data Engineer Certification Exam
Exam Number:	DEA-C01
Passing Score:	~70% (exact passing score not publicly disclosed by Snowflake)
Related Certifications:	SnowPro Core Certification (Recommended Prerequisite)
Real Exam Qty:	approximately 50 questions
Certificate Validity Period:	2 years (recertification required)
Exam Price:	$375 USD
Exam Format:	Multiple Choice, Multiple Select
Available Languages:	English
Exam Duration:	90 minutes
Sample Questions:	Snowflake DEA-C01 Sample Questions
Exam Way:	Online proctored exam (via Pearson VUE) or in-person testing center
Pre Condition:	SnowPro Core Certification is strongly recommended before attempting this advanced-level exam. Practical experience with Snowflake data engineering projects is essential.
Official Syllabus URL:	https://www.snowflake.com/certification/

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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	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 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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q106-Q111):

NEW QUESTION # 106
A technology company currently uses Amazon Kinesis Data Streams to collect log data in real time. The company wants to use Amazon Redshift for downstream real-time queries and to enrich the log data.
Which solution will ingest data into Amazon Redshift with the LEAST operational overhead?

A. Set up an Amazon Kinesis Data Firehose delivery stream to send data to a Redshift provisioned cluster table.
B. Configure Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to send data directly to a Redshift provisioned cluster table.
C. Set up an Amazon Kinesis Data Firehose delivery stream to send data to Amazon S3. Configure a Redshift provisioned cluster to load data every minute.
D. Use Amazon Redshift streaming ingestion from Kinesis Data Streams and to present data as a materialized view.

Answer: D

Explanation:
Amazon Redshift supports streaming ingestion from Amazon Kinesis Data Streams. The Amazon Redshift streaming ingestion feature provides low-latency, high-speed ingestion of streaming data from Amazon Kinesis Data Streams into an Amazon Redshift materialized view. Amazon Redshift streaming ingestion removes the need to stage data in Amazon S3before ingesting into Amazon Redshift.
https://docs.aws.amazon.com/streams/latest/dev/using-other-services-redshift.html

NEW QUESTION # 107
A company uses an Amazon Redshift cluster as a data warehouse that is shared across two departments. To comply with a security policy, each department must have unique access permissions.
Department A must have access to tables and views for Department A. Department B must have access to tables and views for Department B.
The company often runs SQL queries that use objects from both departments in one query.
Which solution will meet these requirements with the LEAST operational overhead?

A. Group tables and views for each department into dedicated databases. Manage permissions at the database level.
B. Create an IAM user group for each department. Use identity-based IAM policies to grant table and view permissions based on the IAM user group.
C. Group tables and views for each department into dedicated schemas. Manage permissions at the schema level.
D. Update the names of the tables and views to follow a naming convention that contains the department names. Manage permissions based on the new naming convention.

Answer: C

Explanation:
By organizing each department's tables and views into its own schema (for example, dept_a and dept_b), you can grant usage and object privileges at the schema level. Department A's role gets USAGE on schema dept_a and the necessary SELECT rights there (and no rights on dept_b), and vice versa for Department B. Because both schemas live in the same database, analysts can still run cross-department queries by schema-qualifying objects, and you avoid the extra complexity of multiple databases or intricate IAM policies.

NEW QUESTION # 108
A company has developed several AWS Glue extract, transform, and load (ETL) jobs to validate and transform data from Amazon S3. The ETL jobs load the data into Amazon RDS for MySQL in batches once every day. The ETL jobs use a DynamicFrame to read the S3 data.
The ETL jobs currently process all the data that is in the S3 bucket. However, the company wants the jobs to process only the daily incremental data.
Which solution will meet this requirement with the LEAST coding effort?

A. Enable job bookmarks for the ETL jobs to update the state after a run to keep track of previously processed data.
B. Configure the ETL jobs to delete processed objects from Amazon S3 after each run.
C. Enable job metrics for the ETL jobs to help keep track of processed objects in Amazon CloudWatch.
D. Create an ETL job that reads the S3 file status and logs the status in Amazon DynamoDB.

Answer: A

Explanation:
AWS Glue job bookmarks are designed to handle incremental data processing by automatically tracking the state.

NEW QUESTION # 109
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?

A. Enable data events for the SNS topics. Use CloudTrail Lake to query the audit data.
B. Enable management events for the SNS topics. Create a table in AWS Glue Data Catalog. Query the data by using Amazon Athena.
C. Enable data events for the SNS topics. Create a table in AWS Glue Data Catalog. Query the data by using Amazon Athena.
D. Enable management events for the SNS topics. Use CloudTrail Lake to query the audit data.

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 # 110
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. 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.
B. 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.
C. Use a custom OpenSearch plugin to sync data from the Amazon DynamoDB tables.
D. Use Amazon DynamoDB Streams to capture table changes. Use an AWS Lambda function to process and update the data in Amazon OpenSearch Service.

Answer: D

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 # 111
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