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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A financial institution wants to predict fraudulent transactions on credit card data stored in Snowflake. The dataset includes features like transaction amount, merchant ID, location, time of day, and user profile information. The target variable is 'is_fraudulent' (0 or 1). You have trained several binary classification models (Logistic Regression, Random Forest, and Gradient Boosting) using scikit-learn and persisted them using a Snowflake external function for inference. To optimize for both performance (inference speed) and accuracy, which of the following steps should you consider before deploying your model for real-time scoring using the external function? SELECT ALL THAT APPLY.

A) Replace the trained models with a simple rule-based system based solely on transaction amount. If the amount is greater than a threshold, flag it as fraudulent, as this will be faster than calling the external function.
B) Evaluate the models on a representative held-out dataset within Snowflake using SQL queries (e.g., calculating AUC, precision, recall) to choose the model with the best balance of performance and accuracy before deploying it.
C) Increase the batch size of requests sent to the external function to amortize the overhead of invoking the external function itself, even if it increases latency for individual transactions.
D) Normalize or standardize the input features in Snowflake using SQL before passing them to the external function to ensure consistent scaling and potentially improve model performance.
E) Implement feature selection techniques (e.g., using feature importance scores from Random Forest or Gradient Boosting) to reduce the number of features passed to the external function, improving inference speed.

2. You have trained a linear regression model in Snowpark ML to predict house prices. After training, you want to assess the overall feature importance using the model's coefficients. Consider the following Snowflake table containing the coefficients:

Which of the following statements are correct interpretations of these coefficients regarding feature impact?

A) The 'age' feature has an insignificant impact because its coefficient is small.
B) The 'bedrooms' feature has a positive impact on the house price since the coefficient is negative.
C) An increase of one square foot (sqft) in house size is associated with an increase of $120.5 in the predicted house price.
D) Increasing the number of bedrooms is associated with a decrease in the predicted house price.
E) The 'location_score' feature is the most influential predictor in determining house price.

3. A data science team is evaluating different methods for summarizing lengthy customer support tickets using Snowflake Cortex. The goal is to generate concise summaries that capture the key issues and resolutions. Which of the following approaches is/are appropriate for achieving this goal within Snowflake, considering the need for efficiency, cost-effectiveness, and scalability? (Select all that apply)

A) Employing a SQL-based approach using string manipulation functions and keyword extraction techniques to identify important sentences and concatenate them to form a summary.
B) Using the 'SNOWFLAKE.ML.PREDICT' function with a summarization task-specific model provided by Snowflake Cortex, passing the full ticket text as input to generate a summary.
C) Developing a Python UDF that leverages a pre-trained summarization model from a library like 'transformers' and deploying it in Snowflake. Managing the model loading and inference within the UDF.
D) Calling the Snowflake Cortex 'COMPLETE' endpoint with a detailed prompt that instructs the model to summarize the support ticket, explicitly specifying the desired summary length and format.
E) Creating a custom summarization model using a transformer-based architecture like BART or T5, training it on a large dataset of support tickets and summaries within Snowflake using Snowpark ML, and then deploying this custom model for generating summaries via a UDF.

4. You are tasked with deploying a real-time fraud detection model in Snowflake. The model requires very low latency (under 100ms) to prevent fraudulent transactions. The input data is streamed into a Snowflake table. You are considering using either a Scalar or Vectorized Python UDF for scoring. Which of the following approaches and considerations are MOST critical for achieving the desired performance and reliability? Assume the model itself is computationally inexpensive. Select all that apply.

A) Use a Scalar UDF because it has lower overhead per invocation compared to a Vectorized UDF when processing individual transactions.
B) Configure Snowflake's Auto-Suspend feature to aggressively suspend the warehouse when idle, to minimize costs.
C) Utilize Snowflake's Materialized Views to pre-compute frequently used features, reducing the amount of data the UDF needs to process.
D) Pre-load the model into a static variable within the UDF code, ensuring it's only loaded once per worker node.
E) Use a Vectorized UDF with a small 'MAX BATCH_SIZE to minimize latency while still leveraging vectorization benefits.

5. You have a Snowflake table 'PRODUCT_PRICES' with columns 'PRODUCT_ID' (INTEGER) and 'PRICE' (VARCHAR). The 'PRICE' column sometimes contains values like '10.50 USD', '20.00 EUR', or 'Invalid Price'. You need to convert the 'PRICE column to a NUMERIC(10,2) data type, removing currency symbols and handling invalid price strings by replacing them with NULL. Considering both data preparation and feature engineering, which combination of Snowpark SQL and Python code snippets achieves this accurately and efficiently, preparing the data for further analysis?

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

Solutions: