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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q203-Q208):

NEW QUESTION # 203
You have trained a complex Random Forest model in Snowflake to predict loan default risk. You wish to understand the individual and combined effects of 'credit_score' and 'debt_to_income_ratio' on the predicted probability of default. Which approach is MOST suitable for visualizing and interpreting these relationships?

A. Fit a simpler linear model (e.g., Logistic Regression) to the data and interpret its coefficients.
B. Generate individual Partial Dependence Plots (PDPs) for 'credit_score' and 'debt_to_income_ratio'.
C. Examine the model's overall accuracy (e.g., AUC) and assume the relationships are well-represented.
D. Create a two-way Partial Dependence Plot (PDP) showing the interaction between 'credit_score' and 'debt_to_income_ratio'.
E. Calculate feature importance using SNOWFLAKE.ML.FEATURE IMPORTANCE and focus on the features with the highest scores.

Answer: D

Explanation:
The correct answer is C. While individual PDPs (option B) provide insights into the individual effects of each feature, a two-way PDP specifically visualizes and helps interpret the interaction between 'credit_score' and 'debt_to_income_ratio'. This is crucial for understanding how the combined effect of these features influences the predicted probability of default. Feature importance (option A) indicates feature relevance but doesn't show the nature of the relationship. Simplifying the model (option D) sacrifices the complexity captured by the Random Forest. Overall accuracy (option E) doesn't provide specific insights into feature relationships.

NEW QUESTION # 204
A financial institution wants to predict fraudulent transactions on credit card data stored in Snowflake. The dataset includes features like transaction amount, merchant ID, location, time of day, and user profile information. The target variable is 'is_fraudulent' (0 or 1). You have trained several binary classification models (Logistic Regression, Random Forest, and Gradient Boosting) using scikit-learn and persisted them using a Snowflake external function for inference. To optimize for both performance (inference speed) and accuracy, which of the following steps should you consider before deploying your model for real-time scoring using the external function? SELECT ALL THAT APPLY.

A. Increase the batch size of requests sent to the external function to amortize the overhead of invoking the external function itself, even if it increases latency for individual transactions.
B. Normalize or standardize the input features in Snowflake using SQL before passing them to the external function to ensure consistent scaling and potentially improve model performance.
C. Implement feature selection techniques (e.g., using feature importance scores from Random Forest or Gradient Boosting) to reduce the number of features passed to the external function, improving inference speed.
D. Replace the trained models with a simple rule-based system based solely on transaction amount. If the amount is greater than a threshold, flag it as fraudulent, as this will be faster than calling the external function.
E. Evaluate the models on a representative held-out dataset within Snowflake using SQL queries (e.g., calculating AUC, precision, recall) to choose the model with the best balance of performance and accuracy before deploying it.

Answer: B,C,E

Explanation:
A, C, and D are the correct answers. A addresses optimizing inference speed by reducing input complexity. C addresses ensuring data quality and model performance consistency. D covers rigorous model validation and selection. B could potentially improve throughput, but could also hurt latency, so it is not always an optimal choice without careful consideration of requirements. E is incorrect as its a huge oversimplification and will likely have low accuracy.

NEW QUESTION # 205
You are tasked with forecasting the daily sales of a specific product for the next 30 days using Snowflake. You have historical sales data for the past 3 years, stored in a Snowflake table named 'SALES DATA', with columns 'SALE DATE (DATE type) and 'SALES AMOUNT' (NUMBER type). You want to use the Prophet library within a Snowflake User-Defined Function (UDF) for forecasting. The Prophet model requires the input data to have columns named 'ds' (for dates) and 'y' (for values). Which of the following code snippets demonstrates the CORRECT way to prepare and pass your data to the Prophet UDF in Snowflake, assuming you've already created the Python UDF 'prophet_forecast'?

Answer: D

Explanation:
The correct approach is to construct JSON objects with 'ds' and 'y' as keys and the corresponding 'SALE_DATE and 'SALES_AMOUNT as values. Then, these JSON objects are aggregated into an array using ARRAY AGG(). This array is then passed to the prophet_forecast' UDF. Options A, B, and D are incorrect because they either pass individual dates and sales amounts as separate arrays or pass the JSON object one by one which is not the desired approach for Prophet UDE.

NEW QUESTION # 206
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?

A. Robust scaling using interquartile range (IQR) in a stored procedure with Python:
B. Min-Max scaling using the following SQL:
C. Power Transformer (e.g., Yeo-Johnson) implemented with Snowpark Python:
D. Z-score standardization using the following SQL:
E. Unit Vector normalization (L2 Normalization) using SQL:

Answer: A,C

Explanation:
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.

NEW QUESTION # 207
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?

A. Partitioning by 'signup_date' is ideal for capturing temporal dependencies in churn behavior and allows for easy retraining of models with the latest data. It also naturally aligns with a walk-forward validation approach. However, it might not be effective if churn drivers are independent of signup date.
B. Partitioning by 'region' is useful if churn is heavily influenced by geographic factors (e.g., local market conditions). It can improve query performance during both training and inference when filtering by region. However, it can create data silos, making it difficult to build a global churn model that considers interactions across regions. Furthermore, the 'region' column must have low cardinality.
C. Partitioning by 'customer_segment' is beneficial if churn patterns are significantly different across segments, allowing for training separate models for each segment. However, if any segment has very few churned customers, it may lead to overfitting or unreliable models for that segment.
D. Implementing partitioning requires modifying existing data loading pipelines and may introduce additional overhead in data management. If the cost of partitioning outweighs the performance gains, it's better to rely on Snowflake's built-in micro-partitioning alone. Also, data skew in partition keys is a major concern.
E. Using clustering in Snowflake on top of partitioning will always improve query performance significantly and reduce compute costs irrespective of query patterns.

Answer: A,B,C,D

Explanation:
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.

NEW QUESTION # 208
......

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