Study with DSA-C03 Actual Test Questions

You are building a fraud detection model using Snowflake data'. The dataset 'TRANSACTIONS' contains billions of records and is partitioned by 'TRANSACTION DATE'. You want to use cross-validation to evaluate your model's performance on different subsets of the data and ensure temporal separation of training and validation sets. Given the following Snowflake table structure:

Which approach would be MOST appropriate for implementing time-based cross-validation within Snowflake to avoid data leakage and ensure robust model evaluation? (Assume using Snowpark Python to develop)

• A. Use 'SNOWFLAKE.ML.MODEL REGISTRY.CREATE MODEL' with default settings, which automatically handles temporal partitioning based on the insertion timestamp of the data.
• B. Utilize the 'SNOWFLAKE.ML.MODEL REGISTRY.CREATE MODEL' with the 'input_colS argument containing 'TRANSACTION DATE'. Snowflake will automatically infer the temporal nature of the data and perform time-based cross-validation.
• C. Create a UDF that assigns each row to a fold based on the 'TRANSACTION DATE column using a modulo operation. This is then passed to the 'cross_validation' function in Snowpark ML.
• D. Implement a custom splitting function within Snowpark, creating sequential folds based on the 'TRANSACTION DATE column and use that with Snowpark ML's cross_validation. Ensure each fold represents a distinct time window without overlap.
• E. Explicitly define training and validation sets based on date ranges within the Snowpark Python environment, performing iterative training and evaluation within the client environment before deploying a model to Snowflake. No built-in cross-validation used

A Snowflake table named 'SALES DATA contains a 'TRANSACTION DATE column stored as VARCHAR. The data in this column is inconsistent; some rows have dates in 'YYYY-MM-DD' format, others in 'MM/DD/YYYY' format, and some contain invalid date strings like 'N/A'. You need to standardize all dates to 'YYYY-MM-DD' format and store them in a new column called FORMATTED DATE in a new table 'STANDARDIZED_SALES DATA. Which of the following approaches, using Snowpark Python and SQL, most effectively handles these inconsistencies and minimizes errors during data transformation? Select all that apply:

• A. Employing Snowpark's error handling mechanism (e.g., 'try...except' blocks) within a loop to iteratively convert each date string, catching and logging errors, and storing valid dates in a new column.
• B. Creating a view on top of 'SALES_DATA' that implements the conversion logic. This avoids creating a new physical table immediately and allows for experimentation with different conversion strategies before materializing the data.
• C. Using a single 'TO_DATE function with format parameter set to 'AUTO' combined with 'TO_VARCHAR to format the date to 'YYYY-MM-DD'.
• D. Using a series of DATE" and 'TO_VARCHAR SQL functions in Snowpark to attempt converting the date in different formats and then formatting the result to 'YYYY-MM-DD'. Any conversion failing returns NULL.
• E. Using a Snowpark Python UDF to parse each date string individually, handling different formats with conditional logic, and returning a formatted date string. This provides flexibility in handling diverse date formats.

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. 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.
• B. 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.
• C. 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.
• D. 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.
• E. 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.

You are using the NetworkX library in Snowpark Python to analyze social network data stored in a Snowflake table named 'USER CONNECTIONS', which has columns 'USER ID' and 'CONNECTED USER representing connections between users. You want to find the users with the highest 'betweenness centrality' to identify influential nodes in the network. Which Snowpark Python code snippet would correctly calculate and display the top 5 users with the highest betweenness centrality?

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You are building a model deployment pipeline using a CI/CD system that connects to your Snowflake data warehouse from your external IDE (VS Code) and orchestrates model training and deployment. The pipeline needs to dynamically create and grant privileges on Snowflake objects (e.g., tables, views, warehouses) required for the model. Which of the following security best practices should you implement when creating and granting privileges within the pipeline?

• A. Hardcode the credentials of a highly privileged user (e.g., a user with the SECURITYADMIN role) in the pipeline script for authentication.
• B. Use the role within the pipeline script to create and grant all necessary privileges.
• C. Grant the ' SYSADMIN' role to the service account used by the pipeline to ensure it has sufficient privileges.
• D. Grant the 'OWNERSHIP' privilege on all objects to the service account so it can perform any operation.
• E. Create a custom role with minimal required privileges to perform only the necessary operations for the pipeline, and grant this role to a dedicated service account used by the pipeline.