Try and practice the latest Snowflake : DSA-C03 real questions & answers

A data scientist is using Snowflake to perform anomaly detection on sensor data from industrial equipment. The data includes timestamp, sensor ID, and sensor readings. Which of the following approaches, leveraging unsupervised learning and Snowflake features, would be the MOST efficient and scalable for detecting anomalies, assuming anomalies are rare events?

• A. Calculate the moving average of sensor readings over a fixed time window using Snowflake SQL and flag data points that deviate significantly from the moving average as anomalies. No ML model needed.
• B. Apply Autoencoders to the sensor data using a Snowflake external function. Data points are considered anomalous if the reconstruction error from the autoencoder exceeds a certain threshold.
• C. Implement an Isolation Forest model. Train the Isolation Forest model on a representative sample of the sensor data and create UDF to score each row in snowflake.
• D. Use K-Means clustering to group sensor readings into clusters and identify data points that are far from the cluster centroids as anomalies. No model training necessary.
• E. Use a Support Vector Machine (SVM) with a radial basis function (RBF) kernel trained on the entire dataset to classify data points as normal or anomalous. Implement the SVM model as a Snowflake UDF.

You are managing a machine learning model lifecycle in Snowflake using the Model Registry. Which of the following statements are true regarding model lineage and governance when utilizing the Model Registry for model versioning and deployment?

• A. The Model Registry automatically tracks the exact SQL queries used to train the model, allowing for full reproducibility of the training process.
• B. Model Registry automatically retrains models based on scheduled data updates, ensuring models are always up-to-date without manual intervention.
• C. The Model Registry provides a central repository to register, version, and manage models, enabling better collaboration and governance across data science teams.
• D. Custom tags and metadata can be associated with each model version, enabling detailed documentation and traceability of model development and deployment.
• E. Integration with Snowflake's RBAC (Role-Based Access Control) allows for granular control over who can register, update, and deploy model versions.

You have trained a classification model in Snowflake using Snowpark ML to predict customer churn. After deploying the model, you observe that the model performs well on the training data but poorly on new, unseen data'. You suspect overfitting. Which of the following strategies can be applied within Snowflake to detect and mitigate overfitting during model validation , considering the model is already deployed and receiving inference requests through a Snowflake UDF?

• A. Since the model is already deployed, the only option is to collect inference requests and compare the distributions of predicted values in each batch with the predicted values on the training set. A large difference indicates overfitting; model must be retrained outside of the validation process.
• B. Implement k-fold cross-validation within the Snowpark ML training pipeline using Snowflake's distributed compute. Track the mean and standard deviation of the performance metrics (e.g., accuracy, Fl-score) across folds. A high variance suggests overfitting. Use this information to tune hyperparameters or select a simpler model architecture before deployment.
• C. Create shadow UDFs that score data using alternative models. Compare the performance metrics (such as accuracy, precision, recall) between the production UDF and shadow UDFs using Snowflake's query capabilities. If shadow models consistently outperform the production model on certain data segments, retrain the production model incorporating those data segments with higher weights.
• D. Calculate the Area Under the Precision-Recall Curve (AUPRC) using Snowflake SQL on both the training and validation datasets. A significant difference indicates overfitting. Then, retrain the model in Snowpark ML with added L1 or L2 regularization, adjusting the regularization strength based on validation set performance, and redeploy the UDF.
• E. Monitor the UDF execution time in Snowflake. A sudden increase in execution time indicates overfitting. Use the 'EXPLAIN' command on the UDF's underlying SQL query to identify performance bottlenecks and rewrite the query for optimization.

You are building a binary classification model in Snowflake to predict customer churn based on historical customer data, including demographics, purchase history, and engagement metrics. You are using the SNOWFLAKE.ML.ANOMALY package. You notice a significant class imbalance, with churn representing only 5% of your dataset. Which of the following techniques is LEAST appropriate to handle this class imbalance effectively within the SNOWFLAKE.ML framework for structured data and to improve the model's performance on the minority (churn) class?

• A. Using the 'sample_weight' parameter in the 'SNOWFLAKE.ML.ANOMALY.FIT function to assign higher weights to the minority class instances during model training.
• B. Applying a SMOTE (Synthetic Minority Over-sampling Technique) or similar oversampling technique to generate synthetic samples of the minority class before training the model outside of Snowflake, and then loading the augmented data into Snowflake for model training.
• C. Adjusting the decision threshold of the trained model to optimize for a specific metric, such as precision or recall, using a validation set. This can be done by examining the probability outputs and choosing a threshold that maximizes the desired balance.
• D. Downsampling the majority class to create a more balanced training dataset within Snowflake using SQL before feeding the data to the modeling function.
• E. Using a clustering algorithm (e.g., K-Means) on the features and then training a separate binary classification model for each cluster to capture potentially different patterns of churn within different customer segments.

You are building a product recommendation system using Snowflake Cortex. You have a table 'PRODUCT DESCRIPTIONS' containing product IDs and textual descriptions. You want to generate vector embeddings for these descriptions to perform similarity searches. However, you need to control the cost and latency of the embedding generation process. Which of the following strategies and considerations are MOST important for optimizing performance and cost when generating vector embeddings in Snowflake Cortex using a UDF?

• A. Optimize the batch size passed to the embedding UDF. Experiment with different batch sizes to find the optimal trade-off between throughput and latency. Too large batches might cause memory issues, while too small batches increase overhead. Consider using a batch size of 64 or 128 as a starting point, adjusting based on your dataset and resource constraints.
• B. Cache the results of the embedding LJDF. Implement a caching mechanism (e.g., using a Snowflake table) to store the embeddings for frequently accessed product descriptions, avoiding redundant embedding calculations. use a materialized view.
• C. Use a larger Snowflake warehouse size. Increasing the warehouse size always linearly reduces embedding generation time and cost.
• D. Use the smallest available Cortex embedding model. Smaller models are always faster and cheaper, regardless of the dataset size.
• E. Partition the 'PRODUCT DESCRIPTIONS' table by product category and generate embeddings for each partition separately. This helps to distribute the workload and reduce the size of the data processed by each UOF call. This makes more sense and is faster to re-create the table.