Instantly download DSA-C03 updated real questions

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. 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.
• B. Migrate your entire Snowflake data warehouse to a different platform which better supports real-time ML inference.
• 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.

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A data scientist is performing exploratory data analysis on a table named 'CUSTOMER TRANSACTIONS. They need to calculate the standard deviation of transaction amounts C TRANSACTION AMOUNT) for different customer segments CCUSTOMER SEGMENT). The 'CUSTOMER SEGMENT column can contain NULL values. Which of the following SQL statements will correctly compute the standard deviation, excluding NULL transaction amounts, and handling NULL customer segments by treating them as a separate segment called 'Unknown'? Consider using Snowflake-specific functions where appropriate.

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

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You are analyzing sensor data collected from industrial machines, which includes temperature readings. You need to identify machines with unusually high temperature variance compared to their peers. You have a table named 'sensor _ readings' with columns 'machine_id', 'timestamp', and 'temperature'. Which of the following SQL queries will help you identify machines with a temperature variance that is significantly higher than the average temperature variance across all machines? Assume 'significantly higher' means more than two standard deviations above the mean variance.

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

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You are training a binary classification model in Snowflake to predict customer churn using Snowpark Python. The dataset is highly imbalanced, with only 5% of customers churning. You have tried using accuracy as the optimization metric, but the model performs poorly on the minority class. Which of the following optimization metrics would be most appropriate to prioritize for this scenario, considering the imbalanced nature of the data and the need to correctly identify churned customers, along with a justification for your choice?

• A. Accuracy - as it measures the overall correctness of the model.
• B. Log Loss (Binary Cross-Entropy) - as it penalizes incorrect predictions proportionally to the confidence of the prediction, suitable for probabilistic outputs.
• C. Area Under the Receiver Operating Characteristic Curve (AUC-ROC) - as it measures the ability of the model to distinguish between the two classes, irrespective of the class distribution.
• D. F 1-Score - as it balances precision and recall, providing a good measure for imbalanced datasets.
• E. Root Mean Squared Error (RMSE) - as it is commonly used for regression problems, not classification.

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You have deployed a fraud detection model in Snowflake that predicts the probability of a transaction being fraudulent. After a month, you observe that the model's precision has significantly dropped. You suspect data drift. Which of the following actions would be MOST effective in identifying and quantifying the data drift in Snowflake, assuming you have access to the transaction data before and after deployment?

• A. Calculate the Jensen-Shannon Divergence between the probability distributions of predicted fraud scores on the training set and the current production data set.
• B. Retrain the model daily with the most recent transaction data without performing any explicit data drift analysis, relying on the model to adapt to the changes.
• C. Use Snowflake's built-in profiling capabilities to generate summary statistics for the training data. Compare these summary statistics with the statistics generated for recent transaction data. If significant differences are observed, assume data drift.
• D. Create a UDF in Snowflake to calculate the Kolmogorov-Smirnov (KS) statistic for each feature between the training data and the recent transaction data. Then, create an alert if the KS statistic exceeds a predefined threshold for any feature.
• E. Periodically sample a small subset of the recent transaction data and manually compare it with the training data using descriptive statistics (mean, standard deviation).

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