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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are using a Snowflake Notebook to build a churn prediction model. You have engineered several features, and now you want to visualize the relationship between two key features: and , segmented by the target variable 'churned' (boolean). Your goal is to create an interactive scatter plot that allows you to explore the data points and identify any potential patterns.
Which of the following approaches is most appropriate and efficient for creating this visualization within a Snowflake Notebook?

A) Write a stored procedure in Snowflake that generates the visualization data in a specific format (e.g., JSON) and then use a JavaScript library within the notebook to render the visualization.
B) Leverage Snowflake's native support for Streamlit within the notebook to create an interactive application. Query the data directly from Snowflake within the Streamlit app and use Streamlit's plotting capabilities for visualization.
C) Create a static scatter plot using Matplotlib directly within the Snowflake Notebook by converting the data to a Pandas DataFrame. This involves pulling all relevant data into the notebook's environment before plotting.
D) Use the Snowflake Connector for Python to fetch the data, then leverage a Python visualization library like Plotly or Bokeh to generate an interactive plot within the notebook.
E) Use the 'snowflake-connector-python' to pull the data and use 'seaborn' to create static plots.

2. You've built a complex machine learning model using scikit-learn and deployed it as a Python UDF in Snowflake. The UDF takes a JSON string as input, containing several numerical features, and returns a predicted probability However, you observe significant performance issues, particularly when processing large batches of data'. Which of the following approaches would be MOST effective in optimizing the performance of this UDF in Snowflake?

A) Use Snowflake's vectorized UDF feature to process data in micro-batches, minimizing the overhead of repeated Python interpreter initialization.
B) Pre-process the input data outside of the UDF using SQL transformations, reducing the amount of data passed to the UDF and simplifying the Python code.
C) Increase the warehouse size to improve the overall compute resources available for UDF execution.
D) Rewrite the UDF in Java or Scala to leverage the JVM's performance advantages over Python in Snowflake.
E) Serialize the scikit-learn model using 'joblib' instead of 'pickle' for potentially faster deserialization within the UDF.

3. You are using Snowpark Feature Store to manage features for your machine learning models. You've created several Feature Groups and now want to consume these features for training a model. To optimize retrieval, you want to use point-in-time correctness. Which of the following actions/configurations are essential to ensure point-in-time correctness when retrieving features using Snowpark Feature Store?

A) When creating Feature Groups, specify a 'timestamp_key' that represents the event timestamp of the data in the source tables.
B) Explicitly specify a in the call.
C) Use the method on the Feature Store client, providing a dataframe containing the 'primary_keyS and the desired for each record.
D) Ensure that all source tables used by the Feature Groups have Change Data Capture (CDC) enabled.
E) Create an associated Stream on the source tables used for Feature Groups

4. You are building a machine learning model to predict loan defaults. You have a dataset in Snowflake with the following features: 'income' (annual income in USD), 'loan_amount' (loan amount in USD), and 'credit_score' (FICO score). You need to normalize these features before training your model. The data has outliers in both 'income' and 'loan_amount', and 'credit_score' has a roughly normal distribution but you still want to standardize it to have a mean of 0 and standard deviation of 1. You want to perform these normalizations using only SQL in Snowflake (no UDFs). Which of the following SQL transformations are most suitable?

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

5. You have deployed a fraud detection model in Snowflake, predicting fraudulent transactions. Initial evaluations showed high accuracy. However, after a few months, the model's performance degrades significantly. You suspect data drift and concept drift. Which of the following actions should you take FIRST to identify and address the root cause?

A) Revert to a previous version of the model known to have performed well, while investigating the issue in the background.
B) Implement a data quality monitoring system to detect anomalies in input features, alongside calculating population stability index (PSI) to quantify data drift.
C) Immediately retrain the model with the latest available data, assuming data drift is the primary issue.
D) Implement a SHAP (SHapley Additive exPlanations) analysis on recent transactions to understand feature importance shifts and potential concept drift.
E) Increase the model's prediction threshold to reduce false positives, even if it means potentially missing more fraudulent transactions.

Solutions: