Snowflake SnowPro Advanced DEA-C02

Certification exams change, and Getcertkey keeps pace: the DEA-C02 practice question set is reviewed continuously and updated free of charge for 365 days. Your Snowflake SnowPro Advanced: Data Engineer (DEA-C02) preparation stays aligned with the current exam throughout 2026 and beyond.

Snowflake DEA-C02 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Data Engineer
Exam Number:	DEA-C02
Passing Score:	70%
Exam Duration:	115 minutes
Certificate Validity Period:	2 years
Available Languages:	English
Related Certifications:	SnowPro Core SnowPro Architect SnowPro Data Scientist
Real Exam Qty:	100
Exam Price:	$350 USD
Exam Format:	Multiple Choice, Multiple Select
Sample Questions:
Exam Way:	Online proctored exam
Pre Condition:	Recommended: SnowPro Core certification or equivalent hands-on experience with Snowflake
Official Syllabus URL:	https://www.snowflake.com/certification/

Snowflake DEA-C02 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Exam FAQ: What Candidates Ask Most

What is the Snowflake DEA-C02 exam?

The DEA-C02 exam is the official Snowflake exam behind the SnowPro Advanced certification, validating the skills measured by the Snowflake SnowPro Advanced: Data Engineer (DEA-C02) credential. It sits at the Advanced level of the Snowflake certification program. It also connects to SnowPro Core, SnowPro Data Scientist, SnowPro Architect, so the knowledge you build here carries over to those tracks as well.

How many questions are on the DEA-C02 exam, and how much time do I get?

The DEA-C02 exam contains 100 questions to be completed within 115 minutes. Before exam day, divide the available time by the question count to work out a comfortable per-question pace, and mark any item that eats into it so you can return later instead of getting stuck. Timed sessions in the Getcertkey test engines make that pacing automatic — run at least two full-length mock exams under the clock so time pressure never becomes the reason you drop points.

What score do I need to pass the DEA-C02 exam, and what does it cost?

The passing score for the DEA-C02 exam is 70%, and the official registration fee is $350 USD. Retakes are not discounted — every new attempt means paying the full fee again — so it pays to measure yourself before you book. Work through the 354 practice questions on Getcertkey, sit a timed practice test, and schedule your exam only when your scores are consistently comfortable. That simple habit is the cheapest exam strategy there is.

Are there any prerequisites for the DEA-C02 exam?

Recommended: SnowPro Core certification or equivalent hands-on experience with Snowflake Requirements can change when Snowflake revises its certification program, so confirm the current eligibility rules on the official exam page before you register.

Can I try the DEA-C02 practice questions before I buy?

Yes. Getcertkey provides a free DEA-C02 PDF demo so you can review the question style and answer quality before purchasing. Every purchase also includes 365 days of free updates — if Snowflake revises the exam during that period, the updated material reaches you at no cost. Once the free-update year ends, you can extend your update service at a 50% discount.

What if I fail the DEA-C02 exam, and how is my order delivered?

Every Snowflake SnowPro Advanced: Data Engineer (DEA-C02) purchase on Getcertkey is covered by a 100% money-back guarantee with clear conditions: if you take the corresponding exam within 60 days of your purchase and do not pass, you can claim a full refund by submitting a scanned copy of your exam enrollment slip and your official score report as a PDF within two days of the exam date; claims are processed within seven days of submission. The guarantee does not apply to exams taken within three days of purchase, to material that was downloaded but never used in an exam attempt, or to free products and expired orders, and the candidate name must match the payer name. If you would rather not take a refund, you can instead exchange your purchase for two free exam preparation products of equal value and keep the update service on your original product.

Delivery is instant: your download is sent to your email within one minute of payment, with no limit on how many computers you may install the material on. If nothing arrives within two hours, check your spam folder and contact customer service for help.

What topics are covered in the DEA-C02 exam?

The Snowflake SnowPro Advanced: Data Engineer (DEA-C02) exam blueprint is organized into 5 domains. The first three are:

• Performance Optimization — 15% of the exam
• Data Transformation with Snowflake — 30% of the exam
• Data Ingestion and Consumption — 20% of the exam

For the complete domain-by-domain breakdown, scroll up to the full exam topics outline above and use it to plan how you distribute your study time.

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions:

Question 1

You are tasked with creating a UDTF in Snowflake to perform a complex data transformation that requires external libraries (e.g., for advanced string manipulation or data analysis). The transformation involves cleaning and standardizing addresses from a table containing millions of customer records. Which language and approach would be most appropriate and efficient for this scenario?

A. Java UDTF with necessary JAR files uploaded to Snowflake's internal stage, leveraging external libraries for address parsing and standardization.
B. SQL UDF with nested CASE statements for address standardization.
C. JavaScript UDF utilizing regular expressions for simple string replacements.
D. Python UDTF leveraging Anaconda packages (e.g., 'addressparser' , 'pandas') for advanced address parsing and standardization, utilizing Snowflake's optimized execution environment for Python.
E. Scala UDTF leveraging sbt to manage dependencies to achieve address parsing and standardization.

Question 2

You are tasked with creating a resilient data ingestion pipeline using Snowpipe and external tables on AWS S3. The data consists of JSON files, some of which may occasionally contain invalid JSON structures (e.g., missing closing brackets, incorrect data types). You want to ensure that even if some files are corrupted, the valid data is still ingested into your target Snowflake table, and the corrupted files are logged for later investigation. Which of the following steps would BEST achieve this?

A. Set the 'ON ERROR option to 'ABORT STATEMENT in the Snowpipe definition. This will stop the entire Snowpipe process when a JSON error is detected, allowing you to manually investigate and fix the corrupted files before restarting the pipeline.
B. Configure the external table definition with 'VALIDATION MODE = 'RETURN ERRORS" and then create a view on top of the external table that filters out rows where the 'METADATA$FILE ROW NUMBER column contains errors.
C. Use Snowflake's => 'JSON', job_id => function against the external stage before ingesting data with Snowpipe to pre-validate files. Then ingest only validated files to your target table
D. Configure Snowpipe to use the 'ON ERROR = 'SKIP FILE" copy option and then create a separate task to query the 'VALIDATION MODE metadata column in the external table to identify and log the corrupted files.
E. Create a custom error handler using a Snowflake stored procedure that catches the 'JSON PARSER ERROR exception and logs the filename to a separate error table. Use the ERROR = 'CONTINUE" copy option in the Snowpipe definition.

Question 3

You have created a masking policy called which redacts salary information based on the user's role. You have applied this policy to the 'SALARY column in the 'EMPLOYEES table. However, after applying the policy, you notice that even users with the 'ACCOUNTADMIN' role are seeing the masked data, which is not the intended behavior. The intention is that 'ACCOUNTADMIN' and 'SECURITYADMIN' roles should always see the real salary data'. What is the MOST likely cause of this issue and what would you suggest fix that?

A. The 'ACCOUNTADMIN' role does not have the 'OWNERSHIP' privilege on the table. Grant the 'OWNERSHIP' privilege to 'ACCOUNTADMIN' on the 'EMPLOYEES' table.
B. The 'ACCOUNTADMIW and 'SECURITYADMIIV roles do not have the 'APPLY MASKING POLICY privilege. Grant this privilege to the roles.
C. The masking policy is not properly activated. Run the ALTER TABLE EMPLOYEES MODIFY COLUMN SALARY SET MASKING POLICY salary_mask' command again.
D. The masking policy does not explicitly exclude the 'ACCOUNTADMIN' and 'SECURITYADMIN' roles. Modify the masking policy to include a condition that checks for these roles and returns the original value if they are active. e.g., 'CASE WHEN IN ('ACCOUNTADMIN', 'SECURITYADMIN') THEN val ELSE END'
E. The 'ACCOUNTADMIN' and roles need to have 'SELECT' privilege on the 'SNOWFLAKACCOUNT USAGE.MASKING POLICIES view

Question 4

You are developing a JavaScript stored procedure in Snowflake using Snowpark to perform a complex data transformation. This transformation involves multiple steps: filtering, joining with another table, and aggregating data'. You need to ensure that the stored procedure is resilient to failures and can be easily debugged. Which of the following practices would contribute to the robustness and debuggability of your stored procedure? (Select all that apply)

A. Relying solely on try-catch blocks within the stored procedure to handle all potential exceptions.
B. Breaking down the complex transformation into smaller, modular functions within the stored procedure and testing each function independently.
C. Directly manipulating the Snowflake metadata (e.g., table schemas) within the stored procedure for dynamic schema evolution.
D. Using Snowpark's logging capabilities to record intermediate results and error messages at various stages of the transformation.
E. Passing the 'snowflake' binding as an argument to each modular function to facilitate logging and SQL execution within those functions.

Question 5

You have a Snowpark Python application that performs complex calculations on a large dataset stored in Snowflake. The application is currently running slowly. After profiling, you've identified that the UDFs you're using are the bottleneck. These UDFs perform custom data transformations using a third-party Python library which has a significant initialization overhead. Which of the following strategies would be MOST effective to optimize performance, minimizing both runtime and resource consumption?

A. Convert the Snowpark Python application to a Snowpark Java application as Java generally offers better performance than Python.
B. Increase the size of the Snowflake warehouse being used for the Snowpark workload. This will provide more CPU and memory resources.
C. Rewrite the UDFs in SQL using Snowflake's built-in functions to avoid the overhead of Python execution. If the library's functions aren't available, consider creating external functions using a cloud provider's serverless compute service.
D. Implement UDF caching at the Snowflake level by setting the 'VOLATILE property to 'IMMUTABLE or 'STABLE' (if appropriate), and leverage the Snowflake query result cache.
E. Use Snowpark's 'pandas_udf with 'vectorized=True' and pre-initialize the third-party library within the UDF's execution context using a closure or similar technique for reuse across batches.

Solutions: