Ideal Snowflake DEA-C01 Exam Questions For Quick Success Updated 2026

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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)
Exam Duration:90 minutes
Exam Format:Multiple Select, Multiple Choice
Related Certifications:SnowPro Core Certification (Recommended Prerequisite)
Available Languages:English
Passing Score:~70% (exact passing score not publicly disclosed by Snowflake)
Real Exam Qty:approximately 50 questions
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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This is the DEA-C01 PDF format which contains real DEA-C01 exam questions. You can print it and make a hard copy of this PDF file as well which helps you to prepare on the go. It comes in handy format and helps you prepare well with updated SnowPro Advanced: Data Engineer Certification Exam exam questions. Moreover, this PDF has questions that are according to the present content of the test. This PDF format helps you to enhance your understanding of each topic which you need to self-evaluate to boost your Snowflake DEA-C01 Exam Score.

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
  • 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 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
  • 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 5
  • 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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q39-Q44):

NEW QUESTION # 39
A global ecommerce company occasionally receives customer data files in its Amazon S3 data lake. The company needs to automatically detect new data and mask sensitive data before making the data available to the company's analytics team.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: C

Explanation:
Amazon EventBridge can detect new object events in Amazon S3 and trigger an automated workflow. AWS Glue workflows provide a managed way to orchestrate the processing, and AWS Glue DataBrew can detect and mask sensitive data before writing the cleansed output back to Amazon S3. This meets the requirement with the least operational overhead because it uses managed services for both event detection and data masking, whereas Amazon Macie is primarily for discovering and reporting sensitive data in S3 rather than masking it in-place as part of the preparation pipeline.


NEW QUESTION # 40
Elon, a Data Engineer, needs to Split Semi-structured Elements from the Source files and load them as an array into Separate Columns.
Source File:
1.+----------------------------------------------------------------------+
2.| $1 |
3.|----------------------------------------------------------------------|
4.| {"mac_address": {"host1": "197.128.1.1","host2": "197.168.0.1"}}, |
5.| {"mac_address": {"host1": "197.168.2.1","host2": "197.168.3.1"}} |
6.+----------------------------------------------------------------------+ Output: Splitting the Machine Address as below.
1.COL1 | COL2 |
2.|----------+----------|
3.| [ | [ |
4.| "197", | "197", |
5.| "128", | "168", |
6.| "1", | "0", |
7.| "1" | "1" |
8.| ] | ] |
9.| [ | [ |
10.| "197", | "197", |
11.| "168", | "168", |
12.| "2", | "3", |
13.| "1" | "1" |
14.| ] | ]
Which SnowFlake Function can Elon use to transform this semi structured data in the output for-mat?

Answer: B


NEW QUESTION # 41
A company needs to implement real-time analytics for a retail shopping platform. The company wants to capture clickstream data, process the data, and load the data into Amazon Redshift for analysis. The solution must handle hundreds of megabytes of data every second. Which solution will meet these requirements with the LEAST query latency for analytics?

Answer: B

Explanation:
Amazon Kinesis Data Streams with Amazon Redshift streaming ingestion provides the lowest analytics latency because Amazon Redshift can ingest streaming data directly into materialized views without first staging the data in Amazon S3. AWS documentation describes Redshift streaming ingestion from Kinesis as low-latency and high-speed, which makes it the best fit for near real-time clickstream analytics at high throughput.
Reference:
https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-streaming-ingestion.html
https://docs.aws.amazon.com/streams/latest/dev/using-other-services-redshift.html


NEW QUESTION # 42
A finance company receives data from third-party data providers and stores the data as objects in an Amazon S3 bucket.
The company ran an AWS Glue crawler on the objects to create a data catalog. The AWS Glue crawler created multiple tables. However, the company expected that the crawler would create only one table.
The company needs a solution that will ensure the AVS Glue crawler creates only one table.
Which combination of solutions will meet this requirement? (Choose two.)

Answer: C,D


NEW QUESTION # 43
A company wants to analyze sales records that the company stores in a MySQL database. The company wants to correlate the records with sales opportunities identified by Salesforce.
The company receives 2 GB of sales records every day. The company has 100 GB of identified sales opportunities. A data engineer needs to develop a process that will analyze and correlate sales records and sales opportunities. The process must run once each night.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: B

Explanation:
This solution meets the requirements with the least operational overhead by utilizing managed AWS services that simplify the data ingestion, transformation, and orchestration processes:
Amazon AppFlow is a fully managed integration service that allows data ingestion from Salesforce without custom connectors or manual ETL processes.
AWS Glue provides serverless data integration, making it suitable for extracting data from the MySQL database and transforming it as needed.
AWS Step Functions can then be used to orchestrate and automate the nightly process, minimizing the need for complex management.


NEW QUESTION # 44
......

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