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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are analyzing website traffic data stored in a Snowflake table named 'WEB EVENTS. This table contains a 'TIMESTAMP' column representing when the event occurred and a 'PAGE VIEWS column indicating the number of page views for that event. You need to identify the day with the highest number of page views and also the day with lowest number of page views along with average number of page views. How can you accomplish this using Snowflake SQL?

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

2. You are tasked with building a predictive model in Snowflake to identify high-value customers based on their transaction history. The 'CUSTOMER_TRANSACTIONS table contains a 'TRANSACTION_AMOUNT column. You need to binarize this column, categorizing transactions as 'High Value' if the amount is above a dynamically calculated threshold (the 90th percentile of transaction amounts) and 'Low Value' otherwise. Which of the following Snowflake SQL queries correctly achieves this binarization, leveraging window functions for threshold calculation and resulting in a 'CUSTOMER SEGMENT column?

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

3. You are building a real-time fraud detection system using Snowpark ML and Dynamic Tables. The raw transaction data arrives continuously in a Snowflake stream. You need to create a data science pipeline that continuously transforms the data, trains a model, and scores new transactions in near real-time. Which combination of Snowflake features provides the BEST solution for achieving low latency and high throughput for this fraud detection system? Select all that apply:

A) Snowflake Tasks with a 'WHEN SYSTEM$STREAM HAS clause to incrementally process new transactions from the stream and update feature tables.
B) Dynamic Tables to continuously transform the raw transaction data into features required by the model, with 'WAREHOUSE SIZE set to 'X-LARGE to ensure sufficient compute resources.
C) Snowpipe with Auto-Ingest to load the raw transaction data into a staging table before processing it with Dynamic Tables.
D) Snowpark ML User-Defined Functions (UDFs) to apply the fraud detection model to incoming transactions, executed using Snowflake's vectorized engine for optimal performance.
E) Scheduled Snowflake tasks to retrain the model every hour based on the most recent transaction data.

4. You have deployed a fraud detection model in Snowflake using Snowpark and are monitoring its performance. You observe a significant drift in the transaction data distribution compared to the data used during training. To address this, you want to implement a retraining strategy. Which of the following steps are MOST critical to automate the retraining process using Snowflake's features?

A) Configure Snowflake's data lineage features to automatically track the input data and model lineage for reproducibility.
B) Develop a Python UDF that periodically calculates drift metrics (e.g., Population Stability Index) and triggers retraining when a threshold is exceeded. Use Snowflake's Task feature to schedule the UDF execution.
C) Replace the existing model artifact in Snowflake's stage with the newly trained model using Snowpark's model registry functionality.
D) Build and deploy a new docker image for each retraining, containing the new model, and update the external function definition to point to the new image.
E) Create a Snowflake Stream on the transaction data table to capture changes since the last training run.

5. 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) Load the entire 'SATELLITE IMAGES table into the UDF for processing, allowing the UDF to handle all image resizing, normalization, and classification tasks sequentially.
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) 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.
D) 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.
E) 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

Solutions: