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You are developing a model to predict customer churn using Snowflake ML. After training a Gradient Boosting model, you want to understand the relationship between 'number_of_products' and the churn probability. You generate a partial dependence plot (PDP) for 'number_of_products'. The PDP shows a steep increase in churn probability as 'number_of_products' increases from 1 to 3, followed by a plateau. Which of the following statements are the MOST accurate interpretations of this PDP? Assume the dataset is balanced and has undergone proper preprocessing.
A. The PDP indicates a high degree of interaction between 'number_of_products' and other features in the model, making the interpretation unreliable.
B. Increasing the number of products purchased by all customers will definitively reduce overall churn.
C. The model is perfectly calibrated, and the PDP accurately represents the true causal effect of 'number_of_products' on churn.
D. Customers who purchase more than 3 products are less likely to churn, suggesting higher engagement or satisfaction.
E. There might be a confounding variable correlated with both 'number_of_products' and churn, leading to a spurious relationship in the PDP.
Answer: D,E
Explanation:
The correct answers are A and C. A: The plateau after 3 products indicates that increasing purchases beyond this point doesn't significantly reduce churn. C: PDPs show correlation, not causation. A confounding variable could be driving both 'number_of_products' and churn. Option B is incorrect because no model is perfectly calibrated and PDPs don't represent causal effects without further analysis. Option D is plausible but requires more information about the specific model and feature interactions. Option E is incorrect as PDPs indicate correlation and not necessarily causation, thus, it would be unsafe to assume increasing the number of products would definitively reduce churn.
NEW QUESTION # 277
You are using Snowflake Cortex to analyze customer reviews. You have created a vector embedding for each review using a UDF that calls a remote LLM inference endpoint. Now you need to perform a similarity search to identify reviews that are similar to a given query review. Which of the following SQL queries leveraging vector functions in Snowflake is the MOST efficient and appropriate way to achieve this, assuming the 'REVIEW EMBEDDINGS' table has columns 'review_id' and 'embedding' (a VECTOR column) and query_embedding' is a pre-computed vector embedding?
A. Option C
B. Option D
C. Option E
D. Option B
E. Option A
Answer: C
Explanation:
The most efficient and accurate way to perform a similarity search with vector embeddings is using ordered in descending order because inner product is the fastest of the vector functions and still gets the vector similarity score. The operator performs an exact match which doesn't consider vector similarity (A). is for array data, not vectors (B). 'QUALIFY' and 'VECTOR COSINE SIMILARITY works but isn't optimal (C), and L2 distance require some value/threshold to compare. 'ORDER BY ... LIMIT is efficient with the inner product, it's very fast (E).
NEW QUESTION # 278
You are managing a machine learning model lifecycle in Snowflake using the Model Registry. Which of the following statements are true regarding model lineage and governance when utilizing the Model Registry for model versioning and deployment?
A. The Model Registry provides a central repository to register, version, and manage models, enabling better collaboration and governance across data science teams.
B. Custom tags and metadata can be associated with each model version, enabling detailed documentation and traceability of model development and deployment.
C. Integration with Snowflake's RBAC (Role-Based Access Control) allows for granular control over who can register, update, and deploy model versions.
D. The Model Registry automatically tracks the exact SQL queries used to train the model, allowing for full reproducibility of the training process.
E. Model Registry automatically retrains models based on scheduled data updates, ensuring models are always up-to-date without manual intervention.
Answer: A,B,C
Explanation:
Options B, C, and D are correct. The Model Registry offers a centralized repository for model management (B), supports custom tags for documentation and traceability (C), and integrates with Snowflake's RBAC for access control (D). Option A is incorrect because the Model Registry does not automatically track SQL queries used for training. While lineage is a part of model governance, Model Registry's lineage capabilities are not focused on capturing training queries but rather on tracking model versions, metrics, and associated metadata. Option E is incorrect; automated retraining is not a feature of the Model Registry itself but can be orchestrated using Snowflake Tasks or other scheduling tools in conjunction with the Model Registry.
NEW QUESTION # 279
A retail company is using Snowflake to store transaction data'. They want to create a derived feature called 'customer _ recency' to represent the number of days since a customer's last purchase. The transactions table 'TRANSACTIONS has columns 'customer_id' (INT) and 'transaction_date' (DATE). Which of the following SQL queries is the MOST efficient and scalable way to derive this feature as a materialized view in Snowflake?
A. Option D
B. Option E
C. Option C
D. Option B
E. Option A
Answer: C
Explanation:
Option C is the most efficient because it correctly calculates the number of days since the last transaction using and 'DATEDIFF. The 'OR REPLACE clause ensures that the materialized view can be updated if it already exists. Options A and B are syntactically identical but A is slightly more correct since it considers the MAX. Option D calculates recency from the first transaction, which is incorrect. Option E is similar to option C but less performant since we want datediff on max(transaction_date) and not calculate and take the max over it.
NEW QUESTION # 280
You are using Snowflake ML to train a binary classification model. After training, you need to evaluate the model's performance. Which of the following metrics are most appropriate to evaluate your trained model, and how do they differ in their interpretation, especially when dealing with imbalanced datasets?
A. AUC-ROC: Measures the ability of the model to distinguish between classes. It is less sensitive to class imbalance than accuracy. Log Loss: Measures the performance of a classification model where the prediction input is a probability value between 0 and 1.
B. Confusion Matrix: A table that describes the performance of a classification model by showing the counts of true positive, true negative, false positive, and false negative predictions. This isnt a metric but representation of the metrics.
C. Precision, Recall, F I-score, AUC-ROC, and Log Loss: Precision focuses on the accuracy of positive predictions; Recall focuses on the completeness of positive predictions; Fl-score balances Precision and Recall; AUC-ROC evaluates the separability of classes and Log Loss quantifies the accuracy of probabilities, especially valuable for imbalanced datasets because they provide a more nuanced view of performance than accuracy alone.
D. Mean Squared Error (MSE): The average squared difference between the predicted and actual values. R-squared: Represents the proportion of variance in the dependent variable that is predictable from the independent variables. These are great for regression tasks.
E. Accuracy: It measures the overall correctness of the model. Precision: It measures the proportion of positive identifications that were actually correct. Recall: It measures the proportion of actual positives that were identified correctly. Fl-score: It is the harmonic mean of precision and recall.
Answer: C
Explanation:
Option E correctly identifies the most appropriate metrics (Precision, Recall, Fl-score, AUC-ROC, and Log Loss) for evaluating a binary classification model, especially in the context of imbalanced datasets. It also correctly describes the focus of each metric. Accuracy can be misleading with imbalanced datasets. MSE and R-squared are for regression problems (Option B). Confusion Matrix is a table, and Options D, contains incorrect statement.
NEW QUESTION # 281
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