Download Snowflake : DSA-C03 Fragen & Antworten als PDF & Test Software

You have implemented a Python UDTF in Snowflake to train a machine learning model incrementally using incoming data'. The UDTF performs well initially, but as the volume of data processed increases significantly, you observe a noticeable degradation in performance and an increase in query execution time. You suspect that the bottleneck is related to the way the model is being updated and persisted within the UDTF. Which of the following optimization strategies, or combination of strategies, would be MOST effective in addressing this performance issue?

• A. Leverage Snowflake's external functions and a cloud-based ML platform (e.g., SageMaker, Vertex A1) to offload the model training process. The UDTF would then only be responsible for data preparation and calling the external function.
• B. Persist the trained model to a Snowflake stage after each batch update. Use a separate UDF (User-Defined Function) to load the model from the stage before processing new data. This decouples model training from inference.
• C. Use the 'cachetools' library within the UDTF to cache intermediate results and reduce redundant calculations during each function call. Configure the cache with a maximum size and eviction policy appropriate for the data volume.
• D. Rewrite the UDTF in Java or Scala, as these languages generally offer better performance compared to Python for computationally intensive tasks. Use the same machine learning libraries that you used with Python.
• E. Instead of updating the model incrementally within the UDTF for each row, batch the incoming data into larger chunks and perform model updates only on these batches. Use Snowflake's VARIANT data type to store these batches temporarily.

A data scientist is analyzing website traffic data stored in Snowflake. The data includes daily page views for different pages. The data scientist suspects that the variance of page views for a particular page, 'home', has significantly increased recently. Which of the following steps and Snowflake SQL queries could be used to identify a potential change in the variance of 'home' page views over time (e.g., comparing variance before and after a specific date)? Select all that apply.

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

You are developing a model to predict house prices based on structured data including size, number of bedrooms, location, and age. You have built a linear regression model within Snowflake. During the evaluation, you observe that the residuals exhibit heteroscedasticity. Which of the following actions is the LEAST appropriate to address heteroscedasticity in this scenario, considering you want to implement the solution primarily using Snowflake's built-in features and capabilities?

• A. Use robust standard errors in the linear regression analysis, even though Snowflake doesn't directly support calculating them. You decide to export model coefficients to an external statistics package (e.g., Python with Statsmodels) to compute robust standard errors and then bring insights back to Snowflake.
• B. Include interaction terms between the independent variables in your linear regression model.
• C. Apply a logarithmic transformation to the target variable ('SALES_PRICE) using the 'LOG' function within Snowflake before training the linear regression model.
• D. Implement Weighted Least Squares (WLS) regression by calculating weights inversely proportional to the variance of the residuals for each data point. This involves creating a UDF to calculate weights and modifying the linear regression model fitting process. (Assume direct modification of the fitting process is possible within Snowflake).
• E. Transform independent variables using Box-Cox transformation and include in Snowflake Linear Regression Model Training

You are tasked with building a predictive model in Snowflake to identify high-value customers based on their transaction history. The 'CUSTOMER_TRANSACTIONS table contains a 'TRANSACTION_AMOUNT column. You need to binarize this column, categorizing transactions as 'High Value' if the amount is above a dynamically calculated threshold (the 90th percentile of transaction amounts) and 'Low Value' otherwise. Which of the following Snowflake SQL queries correctly achieves this binarization, leveraging window functions for threshold calculation and resulting in a 'CUSTOMER SEGMENT column?

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

You have a binary classification model deployed in Snowflake to predict customer churn. The model outputs a probability score between 0 and 1. You've calculated the following confusion matrix on a holdout set: I I Predicted Positive I Predicted Negative I --1 1 Actual Positive | 80 | 20 | I Actual Negative | 10 | 90 | What are the Precision, Recall, and Accuracy for this model, and what do these metrics tell you about the model's performance? SELECT statement given for true and false condition (True Positive, True Negative, False Positive, False Negative)

• A. Precision = 0.89, Recall = 0.80, Accuracy = 0.85. The model is slightly better at avoiding false positives than identifying true positives.
  
• B. Precision = 0.80, Recall = 0.90, Accuracy = 0.90. The model is performing poorly, with a high rate of both false positives and false negatives.
• C. Precision = 0.80, Recall = 0.89, Accuracy = 0.85. The model is slightly better at identifying true positives than avoiding false positives.
• D. Precision = 0.89, Recall = 0.80, Accuracy = 0.85. The model has good overall performance with balanced precision and recall.
• E. Precision = 0.90, Recall = 0.80, Accuracy = 0.80. The model has good overall performance but needs to be adjusted to improve the false negative rate.