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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You've built a machine learning model in scikit-learn and want to deploy it to Snowflake for real-time inference. You have the following options for deploying the model. Select all that apply and are considered a best practice for cost and time optimization:

A) Migrate your entire Snowflake data warehouse to a different platform which better supports real-time ML inference.
B) Use Snowflake's Snowpark Python API to directly load the model from a stage and execute inference using Snowpark DataFrames, which will implicitly handle the distributed processing of the data.
C) Package the scikit-learn model using 'joblib' or 'pickle' , store it in a Snowflake stage, and create a Snowflake UDF (User-Defined Function) in Python to load the model from the stage and perform inference.
D) Create a Snowflake external function that calls a cloud-based (AWS SageMaker, Azure Machine Learning, GCP Vertex A1) endpoint for inference, passing the input data to the endpoint and receiving the prediction back.
E) Implement a custom microservice that reads data from Snowflake, performs inference using the scikit-learn model, and writes the predictions back to Snowflake.

2. A data scientist is using association rule mining with the Apriori algorithm on customer purchase data in Snowflake to identify product bundles. After generating the rules, they obtain the following metrics for a specific rule: Support = 0.05, Confidence = 0.7, Lift = 1.2. Consider that the overall purchase probability of the consequent (right-hand side) of the rule is 0.4. Which of the following statements are CORRECT interpretations of these metrics in the context of business recommendations for product bundling?

A) The lift value of 1.2 suggests a strong negative correlation between the antecedent and consequent, indicating that purchasing the antecedent items decreases the likelihood of purchasing the consequent items.
B) Customers who purchase the items in the antecedent are 70% more likely to also purchase the items in the consequent, compared to the overall purchase probability of the consequent.
C) The rule applies to 5% of all transactions in the dataset, meaning 5% of the transactions contain both the antecedent and the consequent.
D) The confidence of 0.7 indicates that 70% of transactions containing the antecedent also contain the consequent.
E) The lift value of 1.2 indicates that customers are 20% more likely to purchase the consequent items when they have also purchased the antecedent items, compared to the baseline purchase probability of the consequent items.

3. You are building an image classification model within Snowflake to categorize satellite imagery based on land use types (residential, commercial, industrial, agricultural). The images are stored as binary data in a Snowflake table 'SATELLITE IMAGES. You plan to use a pre-trained convolutional neural network (CNN) from a library like TensorFlow via Snowpark Python UDFs. The model requires images to be resized and normalized before prediction. You have a Python UDF named that takes the image data and model as input and returns the predicted class. What steps are crucial to ensure optimal performance and scalability of the image classification process within Snowflake, considering the volume and velocity of incoming satellite imagery?

A) Implement image resizing and normalization directly within the 'classify_image' Python UDF using libraries like OpenCV. Ensure the UDF is vectorized to process images in batches and leverage Snowpark's optimized data transfer capabilities.
B) Utilize Snowflake's external functions to call an image processing service hosted on AWS Lambda or Azure Functions for image resizing and normalization, then pass the processed images to the 'classify_image' UDF.
C) Pre-process the images outside of Snowflake using a separate data pipeline and store the resized and normalized images in a new Snowflake table before running the 'classify_image' UDE
D) Load the entire 'SATELLITE IMAGES table into the UDF for processing, allowing the UDF to handle all image resizing, normalization, and classification tasks sequentially.
E) Use a combination of Snowpark Python UDFs for preprocessing tasks like resizing and normalization, and leverage Snowflake's GPU-accelerated warehouses (if available) to expedite the inference step within the 'classify_image' UDF. Ensure the model weights are efficiently cached.

4. A data scientist is building a linear regression model in Snowflake to predict customer churn based on structured data stored in a table named 'CUSTOMER DATA'. The table includes features like 'CUSTOMER D', 'AGE, 'TENURE MONTHS', 'NUM PRODUCTS', and 'AVG MONTHLY SPEND'. The target variable is 'CHURNED' (1 for churned, 0 for active). After building the model, the data scientist wants to evaluate its performance using Mean Squared Error (MSE) on a held-out test set. Which of the following SQL queries, executed within Snowflake's stored procedure framework, is the MOST efficient and accurate way to calculate the MSE for the linear regression model predictions against the actual 'CHURNED values in the 'CUSTOMER DATA TEST table, assuming the linear regression model is named 'churn _ model' and the predicted values are generated by the MODEL APPLY() function?

A)

B)

C)

D)

E)

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

Solutions: