2026 Snowflake DEA-C01: SnowPro Advanced: Data Engineer Certification Exam–Pass-Sure Cert

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Snowflake DEA-C01 Exam Overview:

Certification Vendor:Snowflake
Exam Name:SnowPro Advanced: Data Engineer Certification Exam
Exam Number:DEA-C01
Exam Price:$375 USD
Exam Duration:115 minutes
Certificate Validity Period:2 years
Exam Format:Multiple select, Multiple choice
Passing Score:750 / 1000
Related Certifications:SnowPro Advanced: Security Engineer
SnowPro Core
SnowPro Advanced: Administrator
SnowPro Advanced: Architect
SnowPro Advanced: Data Scientist
SnowPro Advanced: Data Analyst
Real Exam Qty:65
Available Languages:English, Japanese
Recommended Training:SnowPro Advanced: Data Engineer Exam Study Guide
Snowflake University Training Courses
Exam Registration:Snowflake Certification Portal
Pearson VUE Registration
Sample Questions:Snowflake DEA-C01 Sample Questions
Exam Way:Online proctored or onsite at Pearson VUE test centers
Pre Condition:Must hold active SnowPro Core Certification; recommended 2+ years hands-on data engineering experience
Official Syllabus URL:https://learn.snowflake.com/en/certifications/snowpro-advanced-dataengineer/

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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q339-Q344):

NEW QUESTION # 339
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: C

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 # 340
A gaming company uses a NoSQL database to store customer information. The company is planning to migrate to AWS.
The company needs a fully managed AWS solution that will handle high online transaction processing (OLTP) workload, provide single-digit millisecond performance, and provide high availability around the world.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: A


NEW QUESTION # 341
An insurance company stores transaction data that the company compressed with gzip.
The company needs to query the transaction data for occasional audits.
Which solution will meet this requirement in the MOST cost-effective way?

Answer: B


NEW QUESTION # 342
A popular e-commerce platform experiences heavy traffic during peak hours, resulting in high latency for frequently accessed product listings. To improve performance and response times, the platform decides to implement caching for the most frequently queried product data. They aim to store the results of these queries in a highly available, low-latency cache to reduce database load and improve overall user experience.
Which AWS service would be most suitable for implementing the caching for the most frequent queries and storing their results as a cache?

Answer: B

Explanation:
Amazon MemoryDB for Redis is a fully managed Redis-compatible, in-memory database service that provides enhanced durability, replication, and performance. It is optimized for caching frequently accessed data and provides sub-millisecond latency, making it suitable for improving response times and reducing database load in high-traffic scenarios such as an e-commerce platform.
References:
https://aws.amazon.com/redshift/
https://aws.amazon.com/dynamodb/dax/
https://aws.amazon.com/s3/
https://aws.amazon.com/memorydb/


NEW QUESTION # 343
A company stores historical customer data in an Amazon Redshift table. A column named Email contains null entries and values that are not email addresses. The quality of the Email column is critical for multiple downstream processes. A data engineer must create an AWS Glue Data Quality rule that fails when the percentage of valid email addresses in the Email column is less than 90%. Which component of an AWS Glue Data Quality rule will meet these requirements?

Answer: B

Explanation:
AWS Glue Data Quality uses the ColumnValues rule with pattern matching to validate that values conform to a required format, such as an email pattern. Setting the threshold to greater than 0.9 ensures that at least 90% of the values in the Email column match the expected email format, causing the rule to fail if data quality drops below this level.


NEW QUESTION # 344
......

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