Snowflake SnowPro Advanced: Data Scientist Certification 認定 DSA-C03 試験問題:

1. You're tasked with building an image classification model on Snowflake to identify defective components on a manufacturing assembly line using images captured by high-resolution cameras. The images are stored in a Snowflake table named 'ASSEMBLY LINE IMAGES', with columns including 'image_id' (INT), 'image_data' (VARIANT containing binary image data), and 'timestamp' (TIMESTAMP NTZ). You have a pre-trained image classification model (TensorFlow/PyTorch) saved in Snowflake's internal stage. To improve inference speed and reduce data transfer overhead, which approach provides the MOST efficient way to classify these images using Snowpark Python and UDFs?

A) Create a Python UDF that takes a single 'image_id' as input, retrieves the corresponding 'image_data' from the table, preprocesses the image, loads the pre-trained model, performs classification, and returns the result. This UDF will be called for each image individually.
B) Create a Python UDF that loads the entire table into memory, preprocesses the images, loads the pre-trained model, and performs classification for all images in a single execution.
C) Create a Java UDF that loads the pre-trained model and preprocesses the images. Call this Java UDF from a Python UDF to perform the image classification. Since Java is faster than Python, this will optimize performance.
D) Create a vectorized Python UDF that takes a batch of 'image_id' values as input, retrieves the corresponding 'image_data' from the 'ASSEMBLY LINE IMAGES table using a JOIN, preprocesses the images in a vectorized manner, loads the pre-trained model once at the beginning, performs classification on the batch, and returns the results.
E) Use Snowflake's external function feature to offload the image classification task to a serverless function hosted on AWS Lambda, passing the and 'image_icf to the function for processing.

2. 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) Implement a custom microservice that reads data from Snowflake, performs inference using the scikit-learn model, and writes the predictions back to Snowflake.
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) 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.

3. You're deploying a pre-built image classification model hosted on a REST API endpoint, and you need to integrate it with Snowflake to classify images stored in cloud storage accessible via an external stage named 'IMAGE STAGE. The API expects image data as a base64 encoded string in the request body. Which SQL query snippet demonstrates the correct approach for calling the external function 'CLASSIFY IMAGE and incorporating the base64 encoding?

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

4. You are tasked with training a machine learning model within Snowflake using a Python UDTF. The UDTF is intended to process incoming sales data, calculate features, and update the model incrementally. The model is a simple linear regression using scikit-learn. Your initial attempt fails with a 'ModuleNotFoundError: No module named 'sklearn" error within the UDTF. You have already confirmed that scikit-learn is available in your Anaconda channel and specified it during session creation. Which of the following actions would MOST directly address this issue and allow the UDTF to successfully import and use scikit-learn?

A) Ensure that the Anaconda channel containing 'sklearn' is explicitly activated at the account level using the 'ALTER ACCOUNT command. Verify the channel is listed in 'SHOW CHANNELS'.
B) Explicitly copy the 'sklearn' directory and its dependencies directly into the same directory as your UDTF definition script on the Snowflake stage, then reference them using relative paths within the UDTF.
C) Recreate the Anaconda environment and ensure that the 'sklearn' package is installed specifically within the environment's 'site-packages' directory. Then, recreate the Snowflake session.
D) Include ' import snowflake.snowpark; session = snowflake.snowpark.session.get_active_session()' within the UDTF code to explicitly initialize the Snowpark session before importing sklearn. Ensure that scikit-learn is included in the 'imports' argument of the 'create_dataframe' method.
E) When creating the UDTF, use the 'PACKAGES' parameter to explicitly specify the 'skiearn' package. For example: 'CREATE OR REPLACE FUNCTION RETURNS TABLE LANGUAGE PYTHON RUNTIME_VERSION = '3.8' PACKAGES = ('snowflake-snowpark-python','scikit-learn') ...

5. You are working with a Snowflake table 'CUSTOMER TRANSACTIONS containing customer IDs, transaction dates, and transaction amounts. You need to identify customers who are likely to churn (stop making transactions) in the next month using a supervised learning model. Which of the following strategies would be MOST appropriate to define the target variable (churned vs. not churned) and create features for this churn prediction problem, suitable for a Snowflake-based machine learning pipeline?

A) Define churn based on a fixed threshold of total transaction value over a predefined period. Feature Engineering should purely consist of time series decomposition using Snowflake's built-in functions.
B) Define churn as customers with zero transactions in the last month. Create features like average transaction amount over the past year, number of transactions in the past month, and recency (time since the last transaction).
C) Define churn as customers who haven't made a transaction in the past 6 months. Create a single feature representing the total number of transactions the customer has ever made.
D) Define churn as customers with no transactions in the next month (the prediction target). Create features including: Recency (days since last transaction), Frequency (number of transactions in the past 3 months), Monetary Value (average transaction amount over the past 3 months), and trend of transaction amounts (using linear regression slope over the past 6 months).
E) Define churn as customers with a significant decrease (e.g., 50%) in transaction amounts compared to the previous month. Create features based on demographic data and customer segmentation information, joined from other Snowflake tables.

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