最も有効な DSA-C03 試験参考書

You are building a machine learning pipeline in Snowflake using Snowpark Python. You have completed the data preparation and feature engineering steps and now need to train a model. You want to track the performance of different model versions and hyperparameters using MLflow. You are considering these deployment strategies. Which of the deployment strategies allows automatic logging of metrics, parameters, and model artifacts to MLflow for each training run without requiring explicit MLflow logging code?

• A. Train the model within a Snowpark Python stored procedure. Use a Snowflake stage to store MLflow artifacts.
• B. Train the model locally on your development machine and manually log metrics and artifacts to MLflow using the MLflow API. Then, deploy the trained model to Snowflake as a UDF or stored procedure.
• C. Train the model within a Snowpark Python UDF. Use a Snowflake stage to store MLflow artifacts.
• D. Train the model using Snowpark's DataFrame API directly in a Snowflake worksheet. Manually create a log file with metrics and model parameters and upload it to a Snowflake stage.
• E. Use the Snowpark MLAPI and its integration with MLflow's autologging feature. Enable autologging before starting the training run. Deploy the model to Snowflake as a UDF.

解説: (ShikenPASS メンバーにのみ表示されます)

You're working with a large dataset containing customer purchase history. You want to identify customers whose purchase frequency deviates significantly from the average purchase frequency of all customers. The dataset is in a table named 'purchase history' with columns 'customer id' and 'purchase date'. What combination of Snowflake functionalities will allow you to achieve this task efficiently?
Choose all that apply.

• A. Create a UDF that computes the purchase frequency for a single user and apply it to all customers.
• B. Calculate the average purchase frequency across all customers using and group by 'customer_id'.
• C. Use the window function to divide customers into quantiles based on their total purchase count.
• D. Employ the 'QUALIFY clause along with window functions to filter customers based on a condition related to their purchase frequency compared to the average.
• E. Calculate the Z-score of each customer's purchase frequency using 'AVG(Y, 'STDDEV()' , and window functions, and then filter based on a Z-score threshold.

解説: (ShikenPASS メンバーにのみ表示されます)

A marketing team is using Snowflake to store customer data including demographics, purchase history, and website activity. They want to perform customer segmentation using hierarchical clustering. Considering performance and scalability with very large datasets, which of the following strategies is the MOST suitable approach?

• A. Utilize a SQL-based affinity propagation method directly within Snowflake. This removes the need for feature scaling and specialized hardware.
• B. Employ BIRCH clustering with Snowflake Python UDF. Configure Snowflake resources accordingly. Optimize the clustering process. And tune parameters.
• C. Directly apply an agglomerative hierarchical clustering algorithm with complete linkage to the entire dataset within Snowflake, using SQL. This is computationally feasible due to SQL's efficiency.
• D. Perform mini-batch K-means clustering using Snowflake's compute resources through a Snowpark DataFrame. Take a large sample of each mini-batch and perform hierarchical clustering on each mini-batch and then create clusters of clusters.
• E. Randomly sample a small subset of the customer data and perform hierarchical clustering on this subset using an external tool like R or Python with scikit-learn. Assume that results generalize well to the entire dataset. Avoid using Snowflake for this purpose.

解説: (ShikenPASS メンバーにのみ表示されます)

You are building a data science pipeline in Snowflake to predict customer churn. The pipeline involves extracting data, transforming it using Dynamic Tables, training a model using Snowpark ML, and deploying the model for inference. The raw data arrives in a Snowflake stage daily as Parquet files. You want to optimize the pipeline for cost and performance. Which of the following strategies are MOST effective, considering resource utilization and potential data staleness?

• A. Use a single, large Dynamic Table to perform all transformations in one step, relying on Snowflake's optimization to handle dependencies and incremental updates.
• B. Load all data into traditional Snowflake tables and use scheduled tasks with stored procedures written in Python to perform the transformations and model training.
• C. Schedule all data transformations and model training as a single large Snowpark Python script executed by a Snowflake task, ignoring data freshness requirements.
• D. Implement a series of smaller Dynamic Tables, each responsible for a specific transformation step, with well-defined refresh intervals tailored to the data's volatility and the downstream model's requirements.
• E. Use a combination of Dynamic Tables for feature engineering and Snowpark ML for model training and deployment, ensuring proper dependency management and refresh intervals for each Dynamic Table based on data freshness requirements.

解説: (ShikenPASS メンバーにのみ表示されます)

You are developing a Spark application that needs to read data from a Snowflake table and write the processed data back to a different Snowflake table. Which of the following configurations and code snippets, used in conjunction with the Spark Snowflake Connector, would ensure secure and efficient data transfer, taking into account potential network latency and authentication best practices? Select all that apply.

• A. Set 'sfURL', 'sfUser', 'sfPassword', 'sfDatabase', and 'sfSchema' properties in the Spark configuration. Use to read data and to write data. Rely on Snowflake's default JDBC driver settings for network optimization.
• B.
  
• C. Use Snowflake's Key Pair Authentication. Store the private key securely, and configure 'sflJRL', 'sflJser', 'private_key', 'sfDatabase', and 'sfSchema' properties in the Spark configuration. Ensure the user has appropriate Snowflake privileges. Configure 'numPartitions' parameter based on the scale of data to parallelize read and write operations.
• D. Employ Snowflake's OAuth authentication. Obtain an OAuth token and pass it as a parameter to the Spark Snowflake Connector. You need to also provide 'sfDatabase' and 'sfSchemas properties. Don't set the number of partitions, leaving it to Spark's default behavior.
• E. Configure network timeout parameters in the Spark Snowflake Connector options to handle potential network latency, specifically 'networkTimeoutlnMilliSeconds'. Use 'PREACTIONS' and 'POSTACTIONS' to prepare and finalize data loading. Implement robust error handling to retry failed operations.

解説: (ShikenPASS メンバーにのみ表示されます)