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Respect from colleagues starts with competence, and competence starts with doing the work. Actual4Labs organizes that work for the DSA-C03 exam: 289 practice questions in three formats, delivered within 1 minute.
| Certification Vendor: | Snowflake |
|---|---|
| Exam Name: | SnowPro Advanced: Data Scientist Certification Exam |
| Exam Number: | DSA-C03 |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 65 |
| Available Languages: | Simplified Chinese, Japanese, English |
| Exam Format: | Multiple choice, Multiple select |
| Exam Duration: | 115 minutes |
| Exam Price: | 375 USD |
| Passing Score: | 750 (scaled score 0–1000) |
| Related Certifications: | SnowPro Advanced: Data Engineer SnowPro Core SnowPro Advanced: Architect |
| Recommended Training: | Snowflake Official Training DSA-C03 Study Guide |
| Exam Registration: | Pearson VUE Registration |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored or onsite test center via Pearson VUE |
| Pre Condition: | 2+ years hands-on experience with Snowflake as Data Scientist; proficiency in SQL, Python, or similar languages recommended |
| Official Syllabus URL: | https://learn.snowflake.com/en/certifications/snowpro-advanced-datascientistC03/ |
| Section | Weight | Objectives |
|---|---|---|
| Model Deployment, Monitoring and Governance | 15% | - Deployment strategies
|
| Data Preparation and Feature Engineering in Snowflake | 25% | - Feature engineering techniques
|
| Generative AI and LLM Capabilities | 15% | - Generative AI use cases
|
| Machine Learning Model Development and Training | 25% | - Model types and selection
|
| Data Science Concepts and Methodologies | 20% | - Statistical and mathematical foundations
|
Online proctored or onsite test center via Pearson VUE Register for the Snowflake SnowPro Advanced: Data Scientist Certification exam through these official channels:
After booking, Actual4Labs delivers your practice questions within 1 minute — preparation starts the same day.
Actual4Labs issues a full refund if you take the Snowflake SnowPro Advanced: Data Scientist Certification exam within 60 days of purchase and do not pass. The policy does not apply if the exam is taken within 3 days of purchase, if the exam was never actually taken, or to free materials or expired orders, and the candidate name must match the payer name. Submit a scan of your enrollment slip and the official Score Report PDF within 2 days of the exam; claims are processed within 7 days. Alternatively, exchange for two free exam products of equal value and keep your original update service. Orders arrive by email within 1 minute — contact support if nothing arrives within 2 hours.
The Snowflake SnowPro Advanced: Data Scientist Certification exam requires 750 (scaled score 0–1000) to pass, and registration costs 375 USD. A failed attempt means paying the same fee again — a readiness check with the Actual4Labs engines before booking is the cheaper option.
The official Snowflake SnowPro Advanced: Data Scientist Certification outline defines 5 domains. The leading three are Data Preparation and Feature Engineering in Snowflake (25%), Generative AI and LLM Capabilities (15%), and Machine Learning Model Development and Training (25%). The full list is in the topics section above, and Actual4Labs's 289 practice questions cover each one.
The Snowflake SnowPro Advanced: Data Scientist Certification exam contains 65 to complete within 115 minutes. Timed simulation in the Actual4Labs Desktop Test Engine teaches that rhythm before exam day does.
2+ years hands-on experience with Snowflake as Data Scientist; proficiency in SQL, Python, or similar languages recommended Since requirements change over time, confirm the latest on the official Snowflake exam page before scheduling.
Three versions of the same study materials: a printable, expert-prepared PDF with free demo download and instant access; a Desktop Test Engine for Windows that simulates the real exam with two practice modes and works offline; and an Online Test Engine for any browser on Windows, Mac, Android, and iOS with test history and performance review. All 289 practice questions are identical across versions. Every purchase includes 365 days of free updates, a 50% renewal discount afterward, and unlimited computer installations.
The DSA-C03 exam is a Snowflake certification exam validating the Snowflake SnowPro Advanced: Data Scientist Certification syllabus shown above. It belongs to these credential paths: SnowPro Core, SnowPro Advanced: Data Engineer, SnowPro Advanced: Architect. Earning it in 2026 demonstrates ability in a form employers recognize — and Actual4Labs prepares candidates with 289 practice questions in PDF, Desktop Test Engine, and Online Test Engine formats.
Snowflake recommends these training resources for the Snowflake SnowPro Advanced: Data Scientist Certification exam:
Follow the training with steady self-testing — the 289 practice questions from Actual4Labs help you apply theory and fill any gaps the training leaves.
You are working with a large dataset of customer transactions in Snowflake. The dataset contains columns like 'customer id' , 'transaction date', 'product category' , and 'transaction_amount'. Your task is to identify fraudulent transactions by detecting anomalies in spending patterns. You decide to use Snowpark for Python to perform time-series aggregation and feature engineering. Given the following Snowpark DataFrame 'transactions_df , which of the following approaches would be MOST efficient for calculating a 7-day rolling average of for each customer, while also handling potential gaps in transaction dates?
Correct Answer: E 🗳️
Explanation: Only visible for Actual4Labs members. You can sign-up / login (it's free).
A retail company is using Snowflake to store transaction data'. They want to create a derived feature called 'customer _ recency' to represent the number of days since a customer's last purchase. The transactions table 'TRANSACTIONS has columns 'customer_id' (INT) and 'transaction_date' (DATE). Which of the following SQL queries is the MOST efficient and scalable way to derive this feature as a materialized view in Snowflake?
Correct Answer: D 🗳️
Explanation: Only visible for Actual4Labs members. You can sign-up / login (it's free).
You've deployed a regression model in Snowflake to predict product sales. After a month, you observe that the RMSE on your validation dataset has increased significantly compared to the initial deployment. Analyzing the prediction errors, you notice a pattern: the model consistently underestimates sales for products with a recent surge in social media mentions. Which of the following actions would be MOST effective in addressing this issue and improving the model's RMSE?
Correct Answer: A 🗳️
Explanation: Only visible for Actual4Labs members. You can sign-up / login (it's free).
You are building a fraud detection model using Snowflake data'. One of the features is 'transaction_amount', which has a highly skewed distribution and contains outlier values. Which scaling technique is most appropriate to handle this situation effectively in Snowflake, considering the need to minimize the impact of outliers and preserve the shape of the distribution as much as possible, before feeding the data into a machine learning model? Assume you have sufficient compute resources.
Correct Answer: B,D 🗳️
Explanation: Only visible for Actual4Labs members. You can sign-up / login (it's free).
You have a table 'PRODUCT SALES in Snowflake with columns: 'PRODUCT (INT), 'SALE_DATE (DATE), 'SALES_AMOUNT (FLOAT), and 'PROMOTION FLAG' (BOOLEAN). You need to perform the following data preparation steps using Snowpark SQLAPI:
Correct Answer: D 🗳️
Explanation: Only visible for Actual4Labs members. You can sign-up / login (it's free).
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Viewing questions 1-5 out of 289 questions.
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