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Snowflake DEA-C01 Exam Overview:

Certification Vendor:Snowflake
Exam Name:SnowPro Advanced: Data Engineer Certification Exam
Exam Number:DEA-C01
Certificate Validity Period:2 years (recertification required)
Real Exam Qty:approximately 50 questions
Exam Duration:90 minutes
Passing Score:~70% (exact passing score not publicly disclosed by Snowflake)
Available Languages:English
Exam Format:Multiple Choice, Multiple Select
Related Certifications:SnowPro Core Certification (Recommended Prerequisite)
Exam Price:$375 USD
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:

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
  • 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 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
  • 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 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 (Q31-Q36):

NEW QUESTION # 31
A multinational corporation operating across various sectors has deployed its data infrastructure on AWS, utilizing Amazon S3 for object storage, Amazon Redshift for data warehousing, and Amazon RDS for relational databases. The data analyst team, responsible for extracting insights from these diverse data sources, seeks a solution to query and analyze data across all platforms efficiently. They aim for a unified querying platform that seamlessly integrates with different data stores, allowing for holistic analysis without the overhead of data movement or complex transformations.
Which of the following approaches will meet the requirements of the data analyst team with the least operational effort and most cost-friendly? (Select TWO)

Answer: B,C

Explanation:
AWS Glue Data Catalog can be used to create a centralized metadata repository, providing a unified view of the data across different data sources. This approach requires minimal operational effort as it involves creating and managing metadata, and it is cost-friendly compared to other options. Amazon Athena allows federated queries to directly query data stored in respective data sources using standard SQL. It provides a serverless solution for querying data without the need for data movement, resulting in minimal operational effort and cost.
References:
https://aws.amazon.com/glue/features/catalog/
https://aws.amazon.com/athena/
https://aws.amazon.com/redshift/spectrum/
https://aws.amazon.com/blogs/aws/new-query-your-data-lake-with-amazon-aurora-federated-query/
https://aws.amazon.com/emr/


NEW QUESTION # 32
A company is using an AWS Transfer Family server to migrate data from an on-premises environment to AWS. Company policy mandates the use of TLS 1.2 or above to encrypt the data in transit.
Which solution will meet these requirements?

Answer: B

Explanation:
https://docs.aws.amazon.com/transfer/latest/userguide/security-policies.html


NEW QUESTION # 33
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 # 34
Regular views do not cache data, and therefore cannot improve performance by caching?

Answer: B

Explanation:
Explanation
Regular views do not cache data, and therefore cannot improve performance by caching.


NEW QUESTION # 35
A company uses Amazon DataZone as a data governance and business catalog solution. The company stores data in an Amazon S3 data lake. The company uses AWS Glue with an AWS Glue Data Catalog.
A data engineer needs to publish AWS Glue Data Quality scores to the Amazon DataZone portal.
Which solution will meet this requirement?

Answer: D

Explanation:
Data Quality Ruleset: Creating a ruleset with Data Quality Definition Language (DQDL) rules allows for defining and evaluating data quality on specific AWS Glue tables, enabling automated checks on data quality.
Scheduled Execution: Running the ruleset daily ensures that data quality scores are regularly updated.
AWS Glue Data Source in Amazon DataZone: Configuring Amazon DataZone with an AWS Glue data source enables seamless integration, allowing data quality scores from AWS Glue Data Quality to be published to the Amazon DataZone portal.


NEW QUESTION # 36
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