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Snowflake DEA-C01 Exam Overview:

Certification Vendor:Snowflake
Exam Name:SnowPro Advanced: Data Engineer Certification Exam
Exam Number:DEA-C01
Real Exam Qty:Approximately 65 questions
Exam Price:$375 USD
Exam Duration:115 minutes
Certificate Validity Period:2 years
Passing Score:Not publicly disclosed (Snowflake uses scaled scoring)
Exam Format:Multiple select, Scenario-based questions, Multiple choice
Related Certifications:SnowPro Core Certification
Available Languages:English
Recommended Training:Snowflake University Training
SnowPro Advanced Data Engineer Exam Guide
Exam Registration:Snowflake Certification Portal
Sample Questions:Snowflake DEA-C01 Sample Questions
Exam Way:Online proctored or authorized testing center
Pre Condition:Recommended: SnowPro Core Certification or equivalent Snowflake experience
Official Syllabus URL:https://www.snowflake.com/certifications/snowpro-advanced-data-engineer/

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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 5
  • 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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q263-Q268):

NEW QUESTION # 263
A data engineer notices slow query performance on a highly partitioned table that is in Amazon Athena. The table contains daily data for the previous 5 years, partitioned by date.
The data engineer wants to improve query performance and to automate partition management.
Which solution will meet these requirements?

Answer: D

Explanation:
Partition projection eliminates the need for frequent metadata lookups or explicit partition registration in the AWS Glue Data Catalog. By defining a date range in your table's properties (for example, projection.enabled=true, projection.order_date.type=DATE, projection.order_date.range=2020/01/01,NOW, and the appropriate format), Athena will calculate partitions on-the-fly. This both speeds up queries (avoiding catalog calls for thousands of partitions) and removes the need for any external partition-management jobs.


NEW QUESTION # 264
You're working as a data engineer for a financial services company processing real-time stock market data feeds. This data includes stock prices, trading volumes, and market news. You need a robust infrastructure to capture, process, and analyze this real-time data stream to make informed investment decisions. Downtime or data loss in your streaming pipeline can have significant consequences. Which option ensures high availability and fault tolerance with minimal operational overhead?

Answer: C

Explanation:
Amazon MSK (Managed Streaming for Apache Kafka) provides a fully managed Kafka service that ensures high availability and fault tolerance with minimal operational overhead.


NEW QUESTION # 265
A data engineer has two datasets that contain sales information for multiple cities and states. One dataset is named reference, and the other dataset is named primary.
The data engineer needs a solution to determine whether a specific set of values in the city and state columns of the primary dataset exactly match the same specific values in the reference dataset. The data engineer wants to use Data Quality Definition Language (DQDL) rules in an AWS Glue Data Quality job.
Which rule will meet these requirements?

Answer: B

Explanation:
The ReferentialIntegrity rule checks that every (city, state) pair in the primary dataset has a matching (ref_city, ref_state) pair in the reference dataset, and setting the threshold to 1.0 enforces a 100% match rate. This directly validates exact correspondence of those columns without extra overhead.


NEW QUESTION # 266
A data engineer is using Amazon Athena to analyze sales data that is in Amazon S3. The data engineer writes a query to retrieve sales amounts for 2023 for several products from a table named sales_data. However, the query does not return results for all of the products that are in the sales_data table. The data engineer needs to troubleshoot the query to resolve the issue.
The data engineer's original query is as follows:
SELECT product_name, sum(sales_amount)
FROM sales_data
WHERE year = 2023
GROUP BY product_name
How should the data engineer modify the Athena query to meet these requirements?

Answer: D

Explanation:
https://docs.aws.amazon.com/athena/latest/ug/select.html


NEW QUESTION # 267
Tasks may optionally use table streams to provide a convenient way to continuously process new or changed data. A task can transform new or changed rows that a stream surfaces. Each time a task is scheduled to run, it can verify whether a stream contains change data for a table and either consume the change data or skip the current run if no change data exists. Which System Function can be used by Data engineer to verify whether a stream contains changed data for a table?

Answer: D

Explanation:
Explanation
SYSTEM$STREAM_HAS_DATA
Indicates whether a specified stream contains change data capture (CDC) records.


NEW QUESTION # 268
......

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