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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are a data scientist working with a Snowflake table named 'CUSTOMER DATA' that contains a 'PHONE NUMBER' column stored as VARCHAR. The 'PHONE NUMBER' column sometimes contains non-numeric characters like hyphens and parentheses, and in some rows the data is missing. You need to create a new table 'CLEANED CUSTOMER DATA' with a column named 'CLEANED PHONE NUMBER that contains only the numeric part of the phone number (as VARCHAR) and replaces missing or invalid phone numbers with NULL. Which of the following Snowpark Python code snippets achieves this most efficiently, ensuring no errors occur during the data transformation, and considers Snowflake's performance best practices?

A) Option C
B) Option B
C) Option D
D) Option E
E) Option A

2. A data science team is tasked with deploying a pre-built anomaly detection model in Snowflake to identify fraudulent transactions. They need to use Snowflake ML functions and a Snowflake Native App (that houses the model) to achieve this. The Snowflake Native App is installed and available. The transaction data is stored in a table called 'TRANSACTIONS. Which of the following steps are essential to successfully deploy and use this pre-built model within a User Defined Function (UDF) for real-time scoring, assuming the app provides a function named 'ANOMALY SCORE?

A) Ensure the 'TRANSACTIONS' table is shared with the Snowflake Native App's container so the model can directly access the data.
B) Train the pre-built anomaly detection model using Snowflake's ML functions (e.g., 'CREATE MODELS) with the 'TRANSACTIONS' data before creating the UDE
C) Create a UDF that calls the 'ANOMALY _ SCORE function provided by the Snowflake Native App, passing the relevant transaction features as arguments.
D) Grant the USAGE privilege on the Snowflake Native App to the role executing the UDF. This ensures the UDF can access the app's functionality.
E) Create an external function in API Integration instead of UDF.

3. You've built a model in Snowflake to predict the likelihood of a customer clicking on an advertisement. The model outputs a probability score between 0 and 1. You want to determine the optimal threshold to use for converting these probabilities into binary predictions (click/no-click). Your business stakeholders have provided the following information: Cost of showing an ad: $0.10; Revenue generated from a click: $1.00; You have access to a table 'AD_PREDICTIONS' with columns 'CUSTOMER_ID', 'PREDICTED_PROBABILITY' , and 'ACTUAL CLICK' (1 for click, 0 for no click). Which of the following approaches would be the MOST appropriate for selecting the optimal probability threshold to maximize profit, and why?

A) Select a threshold of 0.5, as this is a common default threshold for binary classification problems.
B) Use the precision-recall curve to find the threshold that maximizes the F1 -score, balancing precision and recall.
C) Select a very high probability threshold (e.g., 0.9) to ensure that only the most likely clicks are targeted, minimizing wasted ad spend.
D) Calculate the point on the ROC curve closest to the top-left corner (perfect classification) and use the corresponding threshold. This optimizes for both sensitivity and specificity.
E) Iterate through a range of probability thresholds (e.g., 0.01 to 0.99), and for each threshold, calculate the profit using SQL in Snowflake: 'SELECT SUM(CASE WHEN PREDICTED PROBABILITY threshold THEN CASE WHEN ACTUAL CLICK = 1 THEN 0.9 ELSE -0.1 END ELSE O END) AS Profit FROM AD_PREDICTIONS;' Choose the threshold that maximizes the profit.

4. You are training a fraud detection model on a dataset containing millions of transactions. To ensure robust generalization, you've decided to implement a train-validation-holdout split using Snowflake's capabilities. Given the following requirements: Temporal Split: The dataset contains a 'transaction date' column. You want to ensure that the validation and holdout sets contain transactions after the training data'. This is crucial because fraud patterns evolve over time. Stratified Sampling (Within Training): The training set should maintain the original proportion of fraudulent vs. non-fraudulent transactions. The column indicates if a transaction is fraudulent (1) or not (0). Deterministic Splits: You need a repeatable process to ensure consistency across model iterations. Which of the following SQL code snippets best achieves these requirements, considering performance and best practices within Snowflake?

A) Option C
B) Option B
C) Option D
D) Option E
E) Option A

5. You are a data scientist working for an e-commerce company. You have a table named 'sales_data' with columns 'product_id' , customer_id' , 'transaction_date' , and 'sale_amount'. You need to identify the top 5 products by total sale amount for each month. Which of the following Snowflake SQL queries is the MOST efficient and correct way to achieve this, while also handling potential ties in sale amounts?

A)

B)

C)

D)

E)

Solutions: