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Snowflake SnowPro Advanced: Data Scientist Certificationの疑問にお答えします

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

問題 #1

You are tasked with identifying fraudulent transactions from unstructured log data stored in Snowflake. The logs contain various fields, including timestamps, user IDs, and transaction details embedded within free-text descriptions. You plan to use a supervised learning approach, having labeled a subset of transactions as 'fraudulent' or 'not fraudulent.' Which of the following methods best describes the extraction and processing of this data for training a machine learning model within Snowflake?

A. Use regular expressions within a Snowflake UDF to extract relevant information (e.g., amount, item description) from the log descriptions. Convert extracted data into numerical features using one-hot encoding within the UDF. Then, train a model using the extracted numerical features directly within Snowflake using SQL extensions for machine learning.
B. Use a combination of regular expressions and natural language processing (NLP) techniques within Snowflake UDFs to extract key features such as transaction amounts, product categories, and sentiment scores from the log descriptions. Then, combine these extracted features with other structured data (e.g., user demographics) and train a classification model using these features. The NLP steps include tokenization, stop word removal, and TF-IDF vectorization.
C. Treat the unstructured log description as a categorical feature and directly apply one-hot encoding within Snowflake, then train a classification model. Due to high dimensionality perform PCA for dimensionality reduction before training.
D. Export the entire log data to an external machine learning platform (e.g., AWS SageMaker) and perform feature extraction, NLP processing, and model training there. Import the trained model back into Snowflake as a UDF for prediction.
E. Extract the entire log description field and train a word embedding model (e.g., Word2Vec) on the entire dataset. Average the word vectors for each transaction's log description to create a document vector. Train a classification model (e.g., Random Forest) on these document vectors within Snowflake.

問題 #2

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. 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.
C. The Google Cloud AI Platform model is unavailable or experiencing issues. Diagnose by checking the Google Cloud status dashboard for AI Platform outages.
D. 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.
E. 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.

問題 #3

You have trained a linear regression model in Snowpark ML to predict house prices. After training, you want to assess the overall feature importance using the model's coefficients. Consider the following Snowflake table containing the coefficients:

Which of the following statements are correct interpretations of these coefficients regarding feature impact?

A. Increasing the number of bedrooms is associated with a decrease in the predicted house price.
B. The 'bedrooms' feature has a positive impact on the house price since the coefficient is negative.
C. An increase of one square foot (sqft) in house size is associated with an increase of $120.5 in the predicted house price.
D. The 'age' feature has an insignificant impact because its coefficient is small.
E. The 'location_score' feature is the most influential predictor in determining house price.

問題 #4

A data scientist is analyzing website traffic data stored in Snowflake. The data includes daily page views for different pages. The data scientist suspects that the variance of page views for a particular page, 'home', has significantly increased recently. Which of the following steps and Snowflake SQL queries could be used to identify a potential change in the variance of 'home' page views over time (e.g., comparing variance before and after a specific date)? Select all that apply.

A. Option A
B. Option C
C. Option E
D. Option B
E. Option D

問題 #5

You are developing a real-time fraud detection system using Snowflake and an external function. The system involves scoring incoming transactions against a pre-trained TensorFlow model hosted on Google Cloud A1 Platform Prediction. The transaction data resides in a Snowflake stream. The goal is to minimize latency and cost. Which of the following strategies are most effective to optimize the interaction between Snowflake and the Google Cloud A1 Platform Prediction service via an external function, considering both performance and cost?

A. Batch multiple transactions from the Snowflake stream into a single request to the external function. The external function then sends the batched transactions to the Google Cloud A1 Platform Prediction service in a single request. This increases throughput but might introduce latency.
B. Invoke the external function for each individual transaction in the Snowflake stream, sending the transaction data as a single request to the Google Cloud A1 Platform Prediction service.
C. Implement asynchronous invocation of the external function from Snowflake using Snowflake's task functionality. This allows Snowflake to continue processing transactions without waiting for the response from the Google Cloud A1 Platform Prediction service, but requires careful monitoring and handling of asynchronous results.
D. Use a Snowflake pipe to automatically ingest the data from the stream, and then trigger a scheduled task that periodically invokes a stored procedure to train the model externally.
E. Implement a caching mechanism within the external function (e.g., using Redis on Google Cloud) to store frequently accessed model predictions, thereby reducing the number of calls to the Google Cloud A1 Platform Prediction service. This requires managing cache invalidation.

解説：