DSA-C03 무료 덤프문제 온라인 액세스

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.Grant the 'OWNERSHIP' privilege on all objects to the service account so it can perform any operation.
• 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.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.
• E.Hardcode the credentials of a highly privileged user (e.g., a user with the SECURITYADMIN role) in the pipeline script for authentication.

You are building a machine learning model to predict customer churn for a telecommunications company. One of the features is 'tariff_plan', which is a string representing different tariff plans (e.g., 'Basic', 'Premium', 'Unlimited'). You need to encode this feature for your model, but you also want to handle potential new tariff plans that might appear in future data'. Which encoding method and Snowflake SQL approach would be MOST suitable to minimize dimensionality and address unseen values effectively, assuming the number of plans is moderately high (around 20-30)?

• A.Target Encoding (Mean Encoding) using Snowflake SQL, calculating the mean churn rate for each tariff plan and using that as the encoded value. Handle unseen values with the global mean churn rate, being mindful of potential target leakage.
• B.Label Encoding using a UDF (User-Defined Function) with a predefined mapping, assigning a new integer to unseen values, and storing the mapping in a separate table in Snowflake.
• C.Hash Encoding (Feature Hashing) using a UDF in Snowflake, with a fixed number of features and a hashing function to map each tariff plan to a feature index, accepting potential collisions. Handle new tariff plans naturally through the hashing function.
• D.Binary Encoding using a UDF to convert each tariff plan into binary code, storing encoded results into snowflake, then splitting the binary representation into separate columns.
• E.One-Hot Encoding using CREATE OR REPLACE VIEW, handling new values by NULLIF('Unknown', tariff_plan) before encoding, potentially leading to a high number of columns.

You are using Snowpark for Python to build a feature engineering pipeline for a machine learning model that predicts customer churn. The data is stored in a Snowflake table called 'CUSTOMER DATA' , and you want to create new features based on time-series data within the table. You need to calculate the 'Recency' feature (days since the last transaction) and 'Frequency' feature (number of transactions in the last 3 months). Considering performance and best practices, which Snowpark approach would you choose?

• A.Write a stored procedure in SQL that calculates 'Recency' and 'Frequency' using SQL window functions, and then call this stored procedure from your Snowpark Python code.
• B.Write custom Python code in a Snowpark UDF to retrieve each transaction for a customer and calculate recency and frequency directly in Python without pandas.
• C.Create a Python UDF using Pandas to calculate 'Recency' and 'Frequency'. Apply this UDF to the 'CUSTOMER DATA' table through Snowpark, processing the data row by row.
• D.Fetch the entire 'CUSTOMER DATA table into a Pandas DataFrame using , then use Pandas' time-series functions to calculate 'Recency' and 'Frequency'. After feature engineering, load the Pandas DataFrame back into Snowflake.
• E.Use Snowpark DataFrame API to perform window functions within Snowflake to calculate 'Recency' and 'Frequency' directly, leveraging Snowflake's processing power without transferring data to the client.

You have developed a customer churn prediction model using Python and deployed it as a Snowflake UDE You are monitoring its performance and notice a significant drop in accuracy over time. To address this, you need to implement automated model retraining with regular validation. Which of the following steps and validation techniques are MOST critical for ensuring the retrained model is effective and avoids overfitting to recent data? (Select THREE)

• A.Update the UDF in place using 'CREATE OR REPLACE FUNCTION' immediately after retraining completes, regardless of the validation results.
• B.Monitor the model's performance on a live dataset and trigger retraining only when the performance drops below a predefined threshold, using metrics like accuracy, precision, or recall. Save Model Performance to 'MODEL_PERFORMANCE.
• C.Retrain the model using the entire available dataset, as this will maximize the amount of data the model learns from.
• D.Implement a data drift detection mechanism. Monitor the distribution of input features over time and trigger retraining if significant drift is detected using tools such as Snowflake's Anomaly Detection features or custom drift metrics calculated in SQL.
• E.Use cross-validation techniques (e.g., k-fold cross-validation) during the retraining process to estimate the model's performance on unseen data and prevent overfitting. Evaluate on a held-out validation set.

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.Implement a custom microservice that reads data from Snowflake, performs inference using the scikit-learn model, and writes the predictions back to Snowflake.
• B.Migrate your entire Snowflake data warehouse to a different platform which better supports real-time ML inference.
• C.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.
• D.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.
• E.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.