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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:750 / 1000
Available Languages:English, Japanese
Exam Format:Multiple choice, Multiple select
Exam Price:$375 USD
Related Certifications:SnowPro Advanced: Security Engineer
SnowPro Advanced: Data Analyst
SnowPro Advanced: Data Scientist
SnowPro Core
SnowPro Advanced: Architect
SnowPro Advanced: Administrator
Exam Duration:115 minutes
Real Exam Qty:65
Certificate Validity Period:2 years
Recommended Training:Snowflake University Training Courses
SnowPro Advanced: Data Engineer Exam Study Guide
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
  • 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
  • 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 3
  • 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 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 (Q18-Q23):

NEW QUESTION # 18
What is the primary purpose of data lineage in data engineering?

Answer: D


NEW QUESTION # 19
A database contains a table and a stored procedure defined as.

No other operations are affecting the log_table.
What will be the outcome of the procedure call?

Answer: C

Explanation:
Explanation
The stored procedure is defined with a FLOAT return type and a JavaScript language. The body of the stored procedure contains a SQL statement that inserts a row into the log_table with a value of '1' for col1. The body also contains a return statement that returns 1 as a float value. When the stored procedure is called with any VARCHAR parameter, it will execute successfully and insert one record into the log_table and return 1 as a return value. The other options are not correct because:
The log_table will not be empty after the stored procedure call, as it will contain one record inserted by the SQL statement.
The stored procedure will not return NULL as a return value, as it has an explicit return statement that returns 1.


NEW QUESTION # 20
A company stores server logs in an Amazon S3 bucket. The company needs to keep the logs for
1 year. The logs are not required after 1 year.
A data engineer needs a solution to automatically delete logs that are older than 1 year.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: C

Explanation:
An S3 Lifecycle configuration is the best choice for this requirement because it allows the company to define a rule that will automatically delete objects in an S3 bucket after a specified period, in this case, 1 year. This solution provides the least operational overhead because it is a built-in feature of Amazon S3, requires no additional infrastructure or management, and is designed specifically for managing object lifecycles.


NEW QUESTION # 21
A data engineer notices that Amazon Athena queries are held in a queue before the queries run.
How can the data engineer prevent the queries from queueing?

Answer: B

Explanation:
https://aws.amazon.com/blogs/aws/introducing-athena-provisioned-capacity/


NEW QUESTION # 22
A media streaming company is moving its application to AWS. The company wants to avoid data quality issues in user engagement analytics. The company needs to be able to reprocess several days of historical data without duplicates and without interrupting current real-time processing.
Which solution will meet these requirements?

Answer: C

Explanation:
Extended retention in Amazon Kinesis Data Streams allows the company to keep several days of historical records available for replay instead of being limited to the default 24 hours. To avoid duplicates during both replay and live consumption, consumers should be idempotent because Kinesis processing can deliver duplicate records in retry scenarios. Using DynamoDB for checkpointing is the standard fit with Kinesis consumer processing, and using separate processing paths lets the company replay historical data without disrupting ongoing real-time processing.


NEW QUESTION # 23
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