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Snowflake DSA-C03 PDF Questions [2026] - Make Your Aspirations Profitable

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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q136-Q141):

NEW QUESTION # 136
You are working with a large sales transaction dataset in Snowflake, stored in a table named 'SALES DATA'. This table contains columns such as 'TRANSACTION_ID (unique identifier), 'CUSTOMER_ID', 'PRODUCT_ID, 'TRANSACTION_DATE' , and 'AMOUNT'. Due to a system error, some transactions were duplicated in the table. Your goal is to remove these duplicates efficiently using Snowpark for Python. You want to use the 'window.partitionBy()' and functions. Which of the following code snippets correctly removes duplicates based on all columns, while also creating a new column 'ROW NUM' to indicate the row number within each partition?

Answer: E

Explanation:
Option A is the correct answer because it correctly partitions the data by all columns using 'sales_df.columns' within the function. It then assigns a row number within each partition using Finally, it filters the data to keep only the first row (ROW_NUM = 1) within each partition, effectively removing duplicates. The removes the temporary column and saves the unique data to a new table. Option B is incorrect because it uses 'orderBy' instead of 'partitionBy' , which does not group identical rows together for duplicate removal. Option C is incorrect because it uses ' F.rank()' instead of 'rank()' assigns the same rank to identical rows within a partition, potentially keeping more than one duplicate. Option D is incorrect because unpacking the dataframe column in partitionby using sales_df.columns causes TypeError: Column is not iterable. Option E is incorrect because passing the entire sales_df to partitionBy is not valid.

NEW QUESTION # 137
You are tasked with deploying a fraud detection model in Snowflake using the Model Registry. The model is trained on a dataset that is updated daily. You need to ensure that your deployed model uses the latest approved version and that you can easily roll back to a previous version if any issues arise. Which of the following approaches would provide the most robust and maintainable solution for model versioning and deployment, considering minimal downtime during updates and rollback?

A. Register each new model version in the Snowflake Model Registry and promote the desired version to 'PRODUCTION' stage. Update a single UDF that dynamically fetches the model based on the 'PRODUCTION' stage metadata.
B. Use Snowflake Tasks to periodically refresh a table containing the latest model weights. The UDF directly queries this table for predictions.
C. Create multiple Snowflake UDFs, each corresponding to a different model version. Manually switch the active UDF by updating application code when a new model is deployed.
D. Store all model versions within a single model registry entry without versioning, overwriting the existing file with each new training run.
E. Deploy a new Snowflake UDF referencing the model file directly in cloud storage every time the model is retrained. Rely on cloud storage versioning for rollback.

Answer: A

Explanation:
Option B provides the most robust and maintainable solution. Registering each model version in the Snowflake Model Registry allows for easy tracking and rollback. Promoting the desired version to 'PRODUCTION' and dynamically fetching the model in a UDF based on this metadata ensures minimal downtime during updates and rollbacks. Option A relies on cloud storage versioning, which is less integrated with Snowflake's metadata management. Option C requires manual UDF switching, which is error-prone. Option D doesn't utilize the Model Registry effectively. Option E eliminates the benefits of version control.

NEW QUESTION # 138
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. 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.
B. 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.
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. 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.
E. 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.

Answer: A,B

Explanation:
Option B is the most comprehensive. It establishes a system for continuous monitoring of model performance using real-world feedback, and alerts you when performance degrades. Option E is also strong because it allows for direct comparison of a new model against the existing model in a production setting, identifying model decay before it significantly impacts performance. Options A and D are insufficient for monitoring as they lack real-world feedback loops for continuous assessment. Simply retraininig frequently does not guarantee model improvements, and option C relies on manual intervention and lacks granular monitoring of the model's specific performance. Shadow Deployment is costly but more robust.

NEW QUESTION # 139
You are using a Snowflake Notebook to analyze customer churn for a telecommunications company. You have a dataset with millions of rows and want to perform feature engineering using a combination of SQL transformations and Python code. Your goal is to create a new feature called 'average_monthly call_duration' which calculates the average call duration for each customer over the last 3 months. You are using the Snowpark DataFrame API within your notebook. Given the following code snippet to start with:

A. Option D
B. Option E
C. Option C
D. Option A
E. Option B

Answer: A,C

Explanation:
Option C and D demonstrate the most efficient approaches using Snowpark DataFrame operations and window functions. Option B is highly inefficient due to the use of UDFs and looping.Option E mixes pandas and snowpark operations which requires intermediate conversion of data into dataframe and it is not recommended for large datasets and is not aligned with Snowpark best practices. Option A just presents the base code and not a solution.

NEW QUESTION # 140
You are a data scientist working for a retail company using Snowflake. You're building a linear regression model to predict sales based on advertising spend across various channels (TV, Radio, Newspaper). After initial EDA, you suspect multicollinearity among the independent variables. Which of the following Snowflake SQL statements or techniques are MOST appropriate for identifying and addressing multicollinearity BEFORE fitting the model? Choose two.

A. Use ' on each independent variable to estimate its uniqueness. If uniqueness is low, multicollinearity is likely.
B. Drop one of the independent variable randomly if they seem highly correlated.
C. Generate a correlation matrix of the independent variables using 'CORR aggregate function in Snowflake SQL and examine the correlation coefficients. Values close to +1 or -1 suggest high multicollinearity.
D. Implement Principal Component Analysis (PCA) using Snowpark Python to transform the independent variables into uncorrelated principal components and then select only the components explaining a certain percentage of the variance.
E. Calculate the Variance Inflation Factor (VIF) for each independent variable using a user-defined function (UDF) in Snowflake that implements the VIF calculation based on R-squared values from auxiliary regressions. This requires fitting a linear regression for each independent variable against all others.

Answer: C,E

Explanation:
Multicollinearity can be identified by calculating the VIF for each independent variable. VIF is calculated by regressing each independent variable against all other independent variables and calculating 1/(1-RA2), where RA2 is the R-squared value from the regression. A high VIF suggests high multicollinearity. Correlation matrices generated with 'CORR can also reveal multicollinearity by showing pairwise correlations between independent variables. PCA using Snowpark is also a viable option, but less direct than VIF and correlation matrix analysis for identifying multicollinearity. APPROX_COUNT_DISTINCT is not directly related to identifying multicollinearity. Randomly dropping variables will also lead to data loss.

NEW QUESTION # 141
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