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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
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
  • 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 3
  • 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 4
  • 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 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 (Q379-Q384):

NEW QUESTION # 379
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?

Answer: D

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 # 380
A company is using Snowpipe to bring in millions of rows every day of Change Data Capture (CDC) into a Snowflake staging table on a real-time basis The CDC needs to get processedand combined with other data in Snowflake and land in a final table as part of the full data pipeline.
How can a Data engineer MOST efficiently process the incoming CDC on an ongoing basis?

Answer: B

Explanation:
Explanation
The most efficient way to process the incoming CDC on an ongoing basis is to create a stream on the staging table and schedule a task that transforms data from the stream only when the stream has data. A stream is a Snowflake object that records changes made to a table, such as inserts, updates, or deletes. A stream can be queried like a table and can provide information about what rows have changed since the last time the stream was consumed. A task is a Snowflake object that can execute SQL statements on a schedule without requiring a warehouse. A task can be configured to run only when certain conditions are met, such as when a stream has data or when another task has completed successfully. By creating a stream on the staging table and scheduling a task that transforms data from the stream, the Data Engineer can ensure that only new or modified rows are processed and that no unnecessary computations are performed.


NEW QUESTION # 381
A company needs a solution to manage costs for an existing Amazon DynamoDB table. The company also needs to control the size of the table. The solution must not disrupt any ongoing read or write operations. The company wants to use a solution that automatically deletes data from the table after 1 month.
Which solution will meet these requirements with the LEAST ongoing maintenance?

Answer: A

Explanation:
DynamoDB TTL (Time to Live) is a built-in feature that allows you to automatically delete expired items from your table based on a timestamp attribute.
Once TTL is enabled, DynamoDB automatically removes the items after they reach the expiration time, without disrupting any ongoing read or write operations. This solution provides the least ongoing maintenance because it's fully managed by DynamoDB and doesn't require manual interventions or additional Lambda functions.


NEW QUESTION # 382
A financial services company stores financial data in Amazon Redshift. A data engineer wants to run real-time queries on the financial data to support a web-based trading application. The data engineer wants to run the queries from within the trading application.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: D

Explanation:
https://docs.aws.amazon.com/redshift/latest/mgmt/data-api.html


NEW QUESTION # 383
A car sales company maintains data about cars that are listed for sale in an area. The company receives data about new car listings from vendors who upload the data daily as compressed files into Amazon S3. The compressed files are up to 5 KB in size. The company wants to see the most up-to- date listings as soon as the data is uploaded to Amazon S3.
A data engineer must automate and orchestrate the data processing workflow of the listings to feed a dashboard. The data engineer must also provide the ability to perform one-time queries and analytical reporting. The query solution must be scalable.
Which solution will meet these requirements MOST cost-effectively?

Answer: B

Explanation:
AWS Glue is a fully managed ETL (Extract, Transform, Load) service that is cost-effective and scalable for processing incoming data. It can handle the compressed data files efficiently.
S3 Event Notifications and AWS Lambda provide a low-cost, serverless orchestration solution that automatically triggers workflows as soon as new files are uploaded to S3. This ensures real- time or near real-time processing of the data without the need to manage long-running services like EMR.
Amazon Athena is a serverless, scalable query service that allows users to run SQL queries on data directly in S3, which is ideal for one-time queries and analytical reporting without provisioning infrastructure.
Amazon QuickSight provides a cost-effective, fully managed service for visualizing the data, making it suitable for the dashboard requirements.
This solution is cost-effective because it minimizes the need for provisioned infrastructure and uses serverless and managed services like Lambda, Glue, Athena, and QuickSight, all of which automatically scale with demand.


NEW QUESTION # 384
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