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Snowflake DSA-C03 Exam Overview:
| Certification Vendor: | Snowflake |
| Exam Name: | SnowPro Advanced: Data Scientist Certification Exam |
| Exam Number: | DSA-C03 |
| Exam Format: | Multiple choice, Multiple select |
| Related Certifications: | SnowPro Core SnowPro Advanced: Data Engineer SnowPro Advanced: Architect |
| Exam Price: | 375 USD |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 115 minutes |
| Passing Score: | 750 (scaled score 0–1000) |
| Available Languages: | English, Japanese, Simplified Chinese |
| Real Exam Qty: | 65 |
| Recommended Training: | Snowflake Official Training DSA-C03 Study Guide |
| Exam Registration: | Pearson VUE Registration |
| Sample Questions: | Snowflake DSA-C03 Sample Questions |
| 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/ |
Snowflake DSA-C03 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Science Concepts and Methodologies | 20% | - Data science lifecycle
|
| Data Preparation and Feature Engineering in Snowflake | 25% | - Feature engineering techniques
|
| Generative AI and LLM Capabilities | 15% | - LLM integration in Snowflake
|
| Machine Learning Model Development and Training | 25% | - Training and optimization
|
| Model Deployment, Monitoring and Governance | 15% | - Monitoring and maintenance
|
Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:
1. You are building a data science pipeline in Snowflake to predict customer churn. The pipeline involves extracting data, transforming it using Dynamic Tables, training a model using Snowpark ML, and deploying the model for inference. The raw data arrives in a Snowflake stage daily as Parquet files. You want to optimize the pipeline for cost and performance. Which of the following strategies are MOST effective, considering resource utilization and potential data staleness?
A) Use a single, large Dynamic Table to perform all transformations in one step, relying on Snowflake's optimization to handle dependencies and incremental updates.
B) Load all data into traditional Snowflake tables and use scheduled tasks with stored procedures written in Python to perform the transformations and model training.
C) Schedule all data transformations and model training as a single large Snowpark Python script executed by a Snowflake task, ignoring data freshness requirements.
D) Implement a series of smaller Dynamic Tables, each responsible for a specific transformation step, with well-defined refresh intervals tailored to the data's volatility and the downstream model's requirements.
E) Use a combination of Dynamic Tables for feature engineering and Snowpark ML for model training and deployment, ensuring proper dependency management and refresh intervals for each Dynamic Table based on data freshness requirements.
2. A data scientist needs to calculate the cumulative moving average of sales for each product in a table. The table contains columns: (INT), (DATE), and (NUMBER). The desired output should include the product_id', 'sale_date', and Which of the following Snowflake SQL statements correctly calculates the cumulative moving average for each product using window functions?
A) SELECT product_id, sale_date, daily_sales, AVG(daily_sales) OVER (PARTITION BY product_id) AS cumulative_average FROM sales_by_day;
B) SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date AS cumulative_average FROM sales_by_day;
C) SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS cumulative_average FROM
D) SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date ASC) / OVER (PARTITION BY product_id ORDER BY sale_date ASC) AS cumulative_average FROM sales_by_day;
E) SELECT product_id, sale_date, daily_sales, AVG(daily_sales) OVER (ORDER BY sale_date ASC) AS cumulative_average FROM sales_by_day;
3. You have built and deployed a model to predict the likelihood of loan default using Snowpark and deployed as a Snowflake UDF. You are using a separate Snowflake table 'LOAN APPLICATIONS' as input, which contains current applicant data'. After several weeks in production, you observe that the model's accuracy has significantly dropped. The original training data was collected during a period of low interest rates and stable economic conditions. Which of the following strategies are the MOST effective for identifying potential causes of this performance degradation and determining if a model retrain is necessary, in the context of Snowflake?
A) Compare the distribution of input features in the 'LOAN_APPLICATIONS table to the distribution of the features in the original training dataset using Snowflake's statistical functions (e.g., APPROX_COUNT DISTINCT, &AVG', 'STDDEV'). Significant deviations indicate data drift.
B) Re-run the original model training code with the 'LOAN_APPLICATIONS table as input and compare the resulting model coefficients to the coefficients of the deployed model. Significant differences indicate model decay.
C) Assume the model is no longer valid due to changing economic conditions and immediately retrain the model with the latest available data without further investigation.
D) Monitor the model's precision and recall using a dedicated monitoring dashboard built on top of the model's predictions and actual loan outcomes (once available). Create a Snowflake alert that triggers when either metric falls below a predefined threshold.
E) Regularly sample data from the ' LOAN_APPLICATIONS table and manually compare it to the original training data. This provides a qualitative assessment of potential changes.
4. You are validating a time series forecasting model for daily sales using Snowflake and Snowpark. The residuals plot shows a clear sinusoidal pattern. Which of the following actions should you consider to improve your model? (Select all that apply)
A) Apply a Box-Cox transformation to the target variable (sales) to stabilize the variance.
B) Remove outlier data points to improve overall model performance.
C) Incorporate lagged features representing previous sales values (e.g., sales from the previous day, week, or month).
D) Change the algorithm to a linear regression model, since it is more likely to capture sinusoidal patterns
E) Increase the regularization strength in your model.
5. You have deployed a machine learning model in Snowflake to predict customer churn. The model was trained on data from the past year. After six months of deployment, you notice the model's recall for identifying churned customers has dropped significantly. You suspect model decay. Which of the following Snowflake tasks and monitoring strategies would be MOST appropriate to diagnose and address this model decay?
A) Use Snowflake's data sharing feature to share the model's predictions with a separate analytics team. Let them monitor the overall customer churn rate and notify you if it changes significantly.
B) Implement a Shadow Deployment strategy in Snowflake. Route a small percentage of incoming data to both the existing model and a newly trained model. Compare the predictions from both models using a UDF that calculates the difference in predicted probabilities. Trigger an alert if the differences exceed a certain threshold.
C) Create a Snowflake Task that automatically retrains the model weekly with the most recent six months of data. Monitor the model's performance metrics using Snowflake's query history to track the accuracy of the predictions.
D) Establish a Snowflake pipe to continuously ingest feedback data (actual churn status) into a feedback table. Write a stored procedure to calculate performance metrics (e.g., recall, precision) on a sliding window of recent data. Create a Snowflake Alert that triggers when recall falls below a defined threshold.
E) Back up the original training data to secure storage. Ingest all new data as it comes in. Retrain a new model and compare its performance with the backed-up training data.
Solutions:
| Question # 1 Answer: D,E | Question # 2 Answer: C,D | Question # 3 Answer: A,D | Question # 4 Answer: A,C | Question # 5 Answer: B,D |
Snowflake DSA-C03 Exam Overview:
| Certification Vendor: | Snowflake |
| Exam Name: | SnowPro Advanced: Data Scientist Certification Exam |
| Exam Number: | DSA-C03 |
| Exam Format: | Multiple choice, Multiple select |
| Related Certifications: | SnowPro Core SnowPro Advanced: Data Engineer SnowPro Advanced: Architect |
| Exam Price: | 375 USD |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 115 minutes |
| Passing Score: | 750 (scaled score 0–1000) |
| Available Languages: | English, Japanese, Simplified Chinese |
| Real Exam Qty: | 65 |
| Recommended Training: | Snowflake Official Training DSA-C03 Study Guide |
| Exam Registration: | Pearson VUE Registration |
| Sample Questions: | Snowflake DSA-C03 Sample Questions |
| 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/ |
Snowflake DSA-C03 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Science Concepts and Methodologies | 20% | - Data science lifecycle
|
| Data Preparation and Feature Engineering in Snowflake | 25% | - Feature engineering techniques
|
| Generative AI and LLM Capabilities | 15% | - LLM integration in Snowflake
|
| Machine Learning Model Development and Training | 25% | - Training and optimization
|
| Model Deployment, Monitoring and Governance | 15% | - Monitoring and maintenance
|
Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:
1. You are building a data science pipeline in Snowflake to predict customer churn. The pipeline involves extracting data, transforming it using Dynamic Tables, training a model using Snowpark ML, and deploying the model for inference. The raw data arrives in a Snowflake stage daily as Parquet files. You want to optimize the pipeline for cost and performance. Which of the following strategies are MOST effective, considering resource utilization and potential data staleness?
A) Use a single, large Dynamic Table to perform all transformations in one step, relying on Snowflake's optimization to handle dependencies and incremental updates.
B) Load all data into traditional Snowflake tables and use scheduled tasks with stored procedures written in Python to perform the transformations and model training.
C) Schedule all data transformations and model training as a single large Snowpark Python script executed by a Snowflake task, ignoring data freshness requirements.
D) Implement a series of smaller Dynamic Tables, each responsible for a specific transformation step, with well-defined refresh intervals tailored to the data's volatility and the downstream model's requirements.
E) Use a combination of Dynamic Tables for feature engineering and Snowpark ML for model training and deployment, ensuring proper dependency management and refresh intervals for each Dynamic Table based on data freshness requirements.
2. A data scientist needs to calculate the cumulative moving average of sales for each product in a table. The table contains columns: (INT), (DATE), and (NUMBER). The desired output should include the product_id', 'sale_date', and Which of the following Snowflake SQL statements correctly calculates the cumulative moving average for each product using window functions?
A) SELECT product_id, sale_date, daily_sales, AVG(daily_sales) OVER (PARTITION BY product_id) AS cumulative_average FROM sales_by_day;
B) SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date AS cumulative_average FROM sales_by_day;
C) SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS cumulative_average FROM
D) SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date ASC) / OVER (PARTITION BY product_id ORDER BY sale_date ASC) AS cumulative_average FROM sales_by_day;
E) SELECT product_id, sale_date, daily_sales, AVG(daily_sales) OVER (ORDER BY sale_date ASC) AS cumulative_average FROM sales_by_day;
3. You have built and deployed a model to predict the likelihood of loan default using Snowpark and deployed as a Snowflake UDF. You are using a separate Snowflake table 'LOAN APPLICATIONS' as input, which contains current applicant data'. After several weeks in production, you observe that the model's accuracy has significantly dropped. The original training data was collected during a period of low interest rates and stable economic conditions. Which of the following strategies are the MOST effective for identifying potential causes of this performance degradation and determining if a model retrain is necessary, in the context of Snowflake?
A) Compare the distribution of input features in the 'LOAN_APPLICATIONS table to the distribution of the features in the original training dataset using Snowflake's statistical functions (e.g., APPROX_COUNT DISTINCT, &AVG', 'STDDEV'). Significant deviations indicate data drift.
B) Re-run the original model training code with the 'LOAN_APPLICATIONS table as input and compare the resulting model coefficients to the coefficients of the deployed model. Significant differences indicate model decay.
C) Assume the model is no longer valid due to changing economic conditions and immediately retrain the model with the latest available data without further investigation.
D) Monitor the model's precision and recall using a dedicated monitoring dashboard built on top of the model's predictions and actual loan outcomes (once available). Create a Snowflake alert that triggers when either metric falls below a predefined threshold.
E) Regularly sample data from the ' LOAN_APPLICATIONS table and manually compare it to the original training data. This provides a qualitative assessment of potential changes.
4. You are validating a time series forecasting model for daily sales using Snowflake and Snowpark. The residuals plot shows a clear sinusoidal pattern. Which of the following actions should you consider to improve your model? (Select all that apply)
A) Apply a Box-Cox transformation to the target variable (sales) to stabilize the variance.
B) Remove outlier data points to improve overall model performance.
C) Incorporate lagged features representing previous sales values (e.g., sales from the previous day, week, or month).
D) Change the algorithm to a linear regression model, since it is more likely to capture sinusoidal patterns
E) Increase the regularization strength in your model.
5. You have deployed a machine learning model in Snowflake to predict customer churn. The model was trained on data from the past year. After six months of deployment, you notice the model's recall for identifying churned customers has dropped significantly. You suspect model decay. Which of the following Snowflake tasks and monitoring strategies would be MOST appropriate to diagnose and address this model decay?
A) Use Snowflake's data sharing feature to share the model's predictions with a separate analytics team. Let them monitor the overall customer churn rate and notify you if it changes significantly.
B) Implement a Shadow Deployment strategy in Snowflake. Route a small percentage of incoming data to both the existing model and a newly trained model. Compare the predictions from both models using a UDF that calculates the difference in predicted probabilities. Trigger an alert if the differences exceed a certain threshold.
C) Create a Snowflake Task that automatically retrains the model weekly with the most recent six months of data. Monitor the model's performance metrics using Snowflake's query history to track the accuracy of the predictions.
D) Establish a Snowflake pipe to continuously ingest feedback data (actual churn status) into a feedback table. Write a stored procedure to calculate performance metrics (e.g., recall, precision) on a sliding window of recent data. Create a Snowflake Alert that triggers when recall falls below a defined threshold.
E) Back up the original training data to secure storage. Ingest all new data as it comes in. Retrain a new model and compare its performance with the backed-up training data.
Solutions:
| Question # 1 Answer: D,E | Question # 2 Answer: C,D | Question # 3 Answer: A,D | Question # 4 Answer: A,C | Question # 5 Answer: B,D |
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