You are exploring a large dataset of website user behavior in Snowflake to identify patterns and potential features for a machine learning model predicting user engagement. You want to create a visualization showing the distribution of 'session_duration' for different 'user_segments'. The 'user_segmentS column contains categorical values like 'New', 'Returning', and 'Power User'. Which Snowflake SQL query and subsequent data visualization technique would be most effective for this task?
Correct Answer: B
Using the Median (option B) provides a better central tendency measure than the average (option A) when the data may have outliers. The box plot effectively visualizes the distribution, including quartiles and outliers. Option C involves generating separate queries and histograms, which is less efficient. Calculating quantiles using 'APPROX_PERCENTILE' (Option D) is good for large datasets, but the resulting scatter plot isn't the best way to show distribution. Pie chart does not show distrubution but proportions.
Question 2
You have deployed a fraud detection model in Snowflake using Snowpark and are monitoring its performance. You observe a significant drift in the transaction data distribution compared to the data used during training. To address this, you want to implement a retraining strategy. Which of the following steps are MOST critical to automate the retraining process using Snowflake's features?
Correct Answer: A,B,C
Options A, B, and C are the most critical for automating the retraining process. A Stream captures changes in the data. A UDF with drift metrics and a Task automate the drift detection and retraining trigger. Updating the model artifact deploys the updated model. While data lineage (D) is important for reproducibility, and using a new docker image for each retraining (E) is viable, they are not strictly most critical for the automation of the core retraining process.
Question 3
You're developing a model to predict customer churn using Snowflake. Your dataset is large and continuously growing. You need to implement partitioning strategies to optimize model training and inference performance. You consider the following partitioning strategies: 1. Partitioning by 'customer segment (e.g., 'High-Value', 'Medium-Value', 'Low-Value'). 2. Partitioning by 'signup_date' (e.g., monthly partitions). 3. Partitioning by 'region' (e.g., 'North America', 'Europe', 'Asia'). Which of the following statements accurately describe the potential benefits and drawbacks of these partitioning strategies within a Snowflake environment, specifically in the context of model training and inference?
Correct Answer: A,B,C,E
Options A, B, C and E are correct because: A: Correctly identifies the benefits (segment-specific models) and drawbacks (overfitting on small segments) of partitioning by 'customer_segment. B: Accurately describes the advantages (temporal patterns, walk-forward validation) and limitations (independence from signup date) of partitioning by 'signup_date' . C: Properly explains the use case (geographic influence), performance benefits (filtering), and potential drawbacks (data silos) of partitioning by 'region'. E: Correctly highlights the implementation overhead and potential skew issues associated with partitioning. Option D is incorrect because Clustering on top of paritioning is not always guranteed performance improvements without assessing underlying query patterns. Snowflake automatically partitions data into micro-partitions, so additional clustering might not always result in significant performance improvements.
Question 4
You are responsible for deploying a fraud detection model in Snowflake. The model needs to be validated rigorously before being put into production. Which of the following actions represent the MOST comprehensive approach to model validation within the Snowflake environment, focusing on both statistical performance and operational readiness, and using Snowflake features for validation?
Correct Answer: B,C
Options B and C represent the most comprehensive approaches. Option B utilizes K-fold cross-validation within Snowflake for robust performance evaluation across data segments and automates validation on new data using streams and tasks. Option C emphasizes backtesting with historical data using Snowflake's time travel feature and monitors performance with alerts, ensuring real-world relevance and timely detection of performance degradation. Option A is insufficient as it relies on a single train/test split. Option D is inadequate and risky due to lack of validation. Option E is also insufficient since calculating only AUC on the entire dataset results in overfitting.
Question 5
You are building a fraud detection model for an e-commerce platform. One of the features is 'purchase_amount', which ranges from $1 to $10,000. The data has a skewed distribution with many small purchases and a few very large ones. You need to normalize this feature for your model, which uses gradient descent. Which normalization technique(s) would be most suitable in Snowflake, considering the data characteristics and the need to handle potential future outliers?
Correct Answer: C,D
Options C and D are the most suitable. Robust scaling (C) is effective because it uses the IQR, making it less sensitive to outliers compared to Min-Max scaling (A) or Z-score standardization (B). The Snowflake UDF handles potential outliers by not being dramatically influenced by them. Power Transformer (D) addresses the skewness of the data, also mitigating the impact of outliers. Min-Max scaling (A) is highly sensitive to outliers, making it a poor choice. Z-score standardization (B) can be affected by extreme values in skewed distributions. Unit Vector normalization (E) changes the meaning of the purchase amounts by making the total magnitude 1 , which isn't desirable here.
