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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
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 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
  • 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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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q133-Q138):

NEW QUESTION # 133
A manufacturing company is setting up an IoT monitoring system that generates large, complex data streams. The company wants to store the data in an Amazon S3 data lake for real-time and historical analysis. The company needs a solution that can process data quickly, provide short query times, and use resources efficiently without slowing down data ingestion.
The solution must use a Spark streaming extract, transform, and load (ETL) job on Amazon EMR that is configured to write data to an Iceberg table.
Which solution will meet these requirements?

Answer: B

Explanation:
For a Spark streaming ETL job on Amazon EMR, using merge-on-read is the better fit for high- throughput streaming ingestion because it reduces write overhead and helps avoid slowing down incoming data. AWS guidance notes that merge-on-read improves write performance for streaming workloads, while compaction later improves read performance by rewriting small files into a more efficient layout. Enabling the AWS Glue Data Catalog compaction optimizer therefore helps maintain short query times while preserving efficient ingestion into the Iceberg table.


NEW QUESTION # 134
A company uses AWS Key Management Service (AWS KMS) to encrypt an Amazon Redshift cluster. The company wants to configure a cross-Region snapshot of the Redshift cluster as part of disaster recovery (DR) strategy.
A data engineer needs to use the AWS CLI to create the cross-Region snapshot.
Which combination of steps will meet these requirements? (Choose two.)

Answer: B,C

Explanation:
You must create a customer managed KMS key in the destination Region and then create a snapshot copy grant against that key so Redshift can re- encrypt the incoming snapshot with the destination CMK.
In the source Region you then enable cross-Region snapshot copying and reference the grant name you created in the destination Region. This tells Redshift which CMK (via the grant) to use when copying the encrypted snapshot into the DR Region.


NEW QUESTION # 135
A company is planning to deploy Amazon EMR in AWS to analyze vast amounts of data. Amazon EMR will be using the EMR file system. The project team is looking for options to deploy in the existing VPC created. Access to other AWS resources should be provided most securely.
How can the Amazon EMR cluster be deployed for this requirement?

Answer: B

Explanation:
Amazon EMR clusters can be deployed in the private subnet of the VPC. With the EMR file system, data is stored in Amazon S3 and it also requires access to DynamoDB. Access from private subnets to Amazon S3 and Amazon DynamoDB can be securely provided using the Interface endpoint.
Reference:
https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-clusters-in-a-vpc.html


NEW QUESTION # 136
A company that operates globally must follow regulations that require data from an AWS Region to be accessible only within that Region.
A data engineer is creating a data pipeline that will create resources in the Region where the data engineer works. The data pipeline should have access to data only from the Region where the data engineer works. The pipeline uses Active Directory as an identity and authentication system.
The pipeline uses a custom identity broker application to verify that employees are signed in to Active Directory and to obtain temporary credentials by using the AssumeRole API operation.
Which solution will meet the locality requirements with the LEAST administrative effort?

Answer: D

Explanation:
Using a single IAM role with region-scoped session policies (using the aws:RequestedRegion condition) applied at AssumeRole time lets the identity broker grant temporary credentials limited to the engineer's Region. This enforces Regional access while avoiding per-Region roles or per- user policies, minimizing administration.


NEW QUESTION # 137
A company uses AWS Glue jobs to implement several data pipelines. The pipelines are critical to the company.
The company needs to implement a monitoring mechanism that will alert stakeholders if the pipelines fail.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: B

Explanation:
By creating an EventBridge rule that matches Glue job state-change events where the state is FAILED, you can emit a custom CloudWatch metric without writing any code. Then you define a CloudWatch alarm on that metric to notify an SNS topic upon breach. This approach requires no Lambda functions or log parsing (just the rule, the metric, and the alarm) minimizing operational overhead.


NEW QUESTION # 138
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