Snowflake SnowPro Advanced: Data Scientist Certification : DSA-C03

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

1. You've deployed a fraud detection model in Snowflake using Snowpark. You are monitoring its performance and notice a significant decrease in recall, while precision remains high. This means the model is missing many fraudulent transactions. The training data was initially balanced, but you suspect that recent changes in user behavior have skewed the distribution of fraudulent vs. non-fraudulent transactions in production. Which of the following actions are MOST appropriate to address this issue and improve the model's performance, considering best practices for model retraining within the Snowflake ecosystem?

A) Retrain the model using the original training data. Since the precision is high, the model's fundamental logic is still sound. A larger training dataset isn't necessary.
B) Adjust the model's classification threshold to be more sensitive, even if it means accepting a slightly lower precision. This can be done directly within Snowflake using a SQL UDF that transforms the model's output probabilities.
C) Immediately shut down the model to prevent further inaccurate classifications. Investigate why the recall is low before any retraining is performed.
D) Implement a data drift monitoring system in Snowflake to automatically detect changes in the input features of the model. Trigger an automated retraining pipeline when significant drift is detected. This retraining should include recent production data with updated labels, but only if label data collection can be automated.
E) Retrain the model using a dataset that includes recent production data, being sure to re-balance the dataset to maintain a roughly equal number of fraudulent and non-fraudulent transactions. Prioritize transactions from the last month.

2. You are a data scientist working for an e-commerce company. You have a table named 'sales_data' with columns 'product_id' , customer_id' , 'transaction_date' , and 'sale_amount'. You need to identify the top 5 products by total sale amount for each month. Which of the following Snowflake SQL queries is the MOST efficient and correct way to achieve this, while also handling potential ties in sale amounts?

A)

B)

C)

D)

E)

3. You are deploying a time series forecasting model in Snowflake. You need to log the performance metrics (e.g., MAE, RMSE) of the model after each prediction run to the Snowflake Model Registry. Which of the following steps are necessary to achieve this?

A) Create a separate table in Snowflake to store the performance metrics and use SQL "INSERT statements to log the metrics after each prediction run.
B) Leverage Snowflake's Event Tables to capture and store metrics data generated during model evaluation and prediction workflows and then access via stored procedures that log to the Model Registry.
C) Use the method with the 'metrics' parameter to log the metrics directly during model registration.
D) You must create a custom logging solution outside of Snowflake using external services and then integrate those logs back into Snowflake via external functions and Model Registry APIs
E) Use the method to log individual metrics to the Model Registry associated with a specific model version after the prediction run.

4. You are troubleshooting an external function in Snowflake that calls a model hosted on Google Cloud A1 Platform. The external function consistently returns 'SQL compilation error: External function error: HTTP 400 Bad Request'. You have verified the API integration is correctly configured, and the Google Cloud project has the necessary permissions. Which of the following is the most likely cause of this error, and how would you best diagnose it?

A) The request payload being sent by Snowflake exceeds the maximum size limit allowed by Google Cloud AI Platform. Diagnose by reducing the size of the input data and testing again.
B) The Google Cloud AI Platform model is unavailable or experiencing issues. Diagnose by checking the Google Cloud status dashboard for AI Platform outages.
C) The issue is most likely due to incorrect data types being passed from Snowflake to the Google Cloud A1 Platform model. Diagnose by examining the input data being sent to the function and comparing it to the model's expected input schema.
D) The API integration in Snowflake is missing the necessary authentication credentials for Google Cloud. Diagnose by re-creating the API integration and ensuring the correct service account and scopes are configured.
E) There is a mismatch between the request headers sent by Snowflake and what the Google Cloud AI Platform endpoint expects, specifically the 'Content-Type'. Diagnose by examining the headers being sent by Snowflake and ensuring they match the expected format.

5. You are building a machine learning model using Snowpark Python to predict house prices. The dataset contains a feature column named 'location' which contains free-form text descriptions of house locations. You want to leverage a pre-trained Large Language Model (LLM) hosted externally to extract structured location features like city, state, and zip code from the free-form text within Snowpark. You want to minimize the data transferred out of Snowflake. Which approach is most efficient and secure?

A) Use the Snowflake Connector for Python to directly query the 'location' column and call the external LLM API from the connector. Then write the updated data into a new table.
B) Create a Snowpark User-Defined Function (UDF) that calls the external LLM API. Pass the 'location' column data to the UDF and retrieve the structured location features. Then apply the UDF directly on the Snowpark DataFrame.
C) Use to load the 'location' column data into a Pandas DataFrame, call the external LLM API in your Python script to enrich the location data and then use to store the enriched data back into a Snowflake table.
D) Create a Snowflake External Function that calls the external LLM API. Pass the 'location' column data to the External Function and retrieve the structured location features. Then apply the External Function directly on the Snowpark DataFrame.
E) Use Snowpark's 'createOrReplaceStage' to create an external stage pointing to the LLM API endpoint. Load the 'location' data into this stage and call the LLM API directly from the Snowflake stage using SQL.

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