Exam Name: SnowPro Advanced: Data Scientist Certification Exam
Certification Provider: Snowflake
Corresponding Certification: SnowPro Advanced Certification
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Updated: Sep 03, 2026
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Snowflake SnowPro Advanced: Data Scientist Certification follows objectives published by Snowflake, so preparation should stay aligned with the official outline. Actual4dump organizes 67 DSA-C02 practice questions around those exam areas to support focused review.
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Snowflake DSA-C02 Exam Overview:
| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | SnowPro Advanced: Data Scientist Certification Exam |
| Exam Number: | DSA-C02 |
| Related Certifications: | SnowPro Core Certification SnowPro Advanced: Data Engineer |
| Exam Format: | Multiple select, Multiple choice |
| Available Languages: | English |
| Recommended Training: | Snowflake Training and Certification Courses |
| Exam Registration: | Snowflake Certification Portal |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored exam via Snowflake certification platform or authorized testing provider |
| Pre Condition: | SnowPro Core certification is recommended; familiarity with SQL, Python, and data science workflows is expected. |
| Official Syllabus URL: | https://www.snowflake.com/certifications/ |
Snowflake DSA-C02 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Data Ingestion and Data Preparation | - Data cleaning and transformation in Snowflake - Data loading and ingestion patterns |
| Topic 2: Feature Engineering and Data Processing | - Handling structured and semi-structured data - Feature creation and transformation techniques |
| Topic 3: Model Deployment and Operationalization | - Deploying models in Snowflake environment - Monitoring and maintaining ML workflows |
| Topic 4: Snowflake ML and Data Science Tools | - Applying built-in Snowflake ML capabilities - Using Snowpark for Python |
| Topic 5: Machine Learning in Snowflake | - Model evaluation and iteration - Model development and training workflows |
Snowflake DSA-C02 Exam Questions Answered
The DSA-C02 exam leads to the SnowPro Advanced: Data Scientist certification from Snowflake. It is positioned at the Professional level. The exam validates the knowledge and skills expected of candidates preparing for Snowflake SnowPro Advanced: Data Scientist Certification.
Related certifications include:
- SnowPro Core Certification
- SnowPro Advanced: Data Engineer
SnowPro Core certification is recommended; familiarity with SQL, Python, and data science workflows is expected.
Eligibility rules can change, so confirm the current requirements on the official exam page before registering.
You can register through the official channels listed below:
The available exam delivery method is Online proctored exam via Snowflake certification platform or authorized testing provider.
The official training options include:
After reviewing the recommended training, reinforce what you learned with Actual4dump's 67 practice questions for DSA-C02.
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The current DSA-C02 outline is organized into 5 top-level domains. The first listed areas include:
- Snowflake ML and Data Science Tools
- Data Ingestion and Data Preparation
- Machine Learning in Snowflake
Review the Exam Topics section above for the complete outline and any nested subtopics.
Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:
Question 1
Consider a data frame df with 10 rows and index [ 'r1', 'r2', 'r3', 'row4', 'row5', 'row6', 'r7', 'r8', 'r9', 'row10'].
What does the aggregate method shown in below code do?
g = df.groupby(df.index.str.len())
g.aggregate({'A':len, 'B':np.sum})
A. Computes length of column A
B. Computes Sum of column A values
C. Computes length of column A and Sum of Column B values
D. Computes length of column A and Sum of Column B values of each group
Question 2
Which of the following cross validation versions is suitable quicker cross-validation for very large datasets with hundreds of thousands of samples?
A. Leave-one-out cross-validation
B. All of the above
C. Holdout method
D. k-fold cross-validation
Question 3
Which of the following Snowflake parameter can be used to Automatically Suspend Tasks which are running Data science pipelines after specified Failed Runs?
A. SUSPEND_TASK_AFTER_NUM_FAILURES
B. There is none as such available.
C. SUSPEND_TASK
D. SUSPEND_TASK_AUTO_NUM_FAILURES
Question 4
Which one is not Types of Feature Scaling?
A. Min-Max Scaling
B. Economy Scaling
C. Robust Scaling
D. Standard Scaling
Question 5
Mark the incorrect statement regarding Python UDF?
A. A scalar function (UDF) returns a tabular value for each input row
B. Python UDFs can contain both new code and calls to existing packages
C. A UDF also gives you a way to encapsulate functionality so that you can call it repeatedly from multiple places in code
D. For each row passed to a UDF, the UDF returns either a scalar (i.e. single) value or, if defined as a table function, a set of rows.
Solutions:
| Question 1 Answer: D | Question 2 Answer: C | Question 3 Answer: A | Question 4 Answer: A | Question 5 Answer: A |
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