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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q133-Q138):

NEW QUESTION # 133
Your team has deployed a machine learning model to Snowflake for predicting customer churn. You need to implement a robust metadata tagging strategy to track model lineage, performance metrics, and usage. Which of the following approaches are the MOST effective for achieving this within Snowflake, ensuring seamless integration with model deployment pipelines and facilitating automated retraining triggers based on data drift?

A. Using Snowflake's built-in tag functionality to tag tables, views, and stored procedures related to the model. Implementing custom Python scripts using Snowflake's Python API (Snowpark) to automatically apply tags during model deployment and retraining based on predefined rules and data quality checks.
B. Relying solely on manual documentation and spreadsheets to track model metadata, as automated solutions introduce unnecessary complexity and potential errors.
C. Leveraging a third-party metadata management tool that integrates with Snowflake and provides a centralized repository for model metadata, lineage tracking, and data governance. This tool should support automated tag propagation and data drift monitoring. Use Snowflake external functions to trigger alerts based on metadata changes.
D. Storing model metadata in a separate relational database (e.g., PostgreSQL) and using Snowflake external tables to access the metadata information. Implement custom stored procedures to synchronize metadata between Snowflake and the external database.
E. Utilizing Snowflake's INFORMATION SCHEMA views to extract metadata about tables, views, and stored procedures, and then writing custom SQL scripts to generate reports and track model lineage. Combine this with Snowflake's data masking policies to control access to sensitive metadata.

Answer: A,C

Explanation:
Options A and C are the most effective. Option A leverages Snowflake's native tagging capabilities combined with Snowpark for automation, allowing dynamic tagging during model deployment and retraining. Option C provides a centralized and robust metadata management approach via a third-party tool, crucial for complex model deployments requiring lineage tracking, data governance, and automated data drift monitoring. Options B and D are less efficient. Option B introduces manual and error-prone processes. Option D adds unnecessary complexity by requiring synchronization between Snowflake and an external database. While option E can be useful for generating reports, it's not a comprehensive solution for metadata tagging and model lineage tracking.

NEW QUESTION # 134
You're developing a model to predict equipment failure using sensor data stored in Snowflake. The dataset is highly imbalanced, with failure events (positive class) being rare compared to normal operation (negative class). To improve model performance, you're considering both up-sampling the minority class and down-sampling the majority class. Which of the following statements regarding the potential benefits and drawbacks of combining up-sampling and down-sampling techniques in this scenario are TRUE? (Select TWO)

A. Combining up-sampling and down-sampling can lead to a more balanced dataset, potentially improving the model's ability to learn patterns from both classes without introducing excessive bias from solely up-sampling.
B. Over-sampling, combined with downsampling, makes the model more prone to overfitting since this causes the model to train on a large dataset.
C. Down-sampling, when combined with up-sampling, can exacerbate the risk of losing important information from the majority class, leading to underfitting, especially if the majority class is already relatively small.
D. The optimal sampling ratio for both up-sampling and down-sampling must always be 1:1, regardless of the initial class distribution.
E. Using both up-sampling and down-sampling always guarantees improved model performance compared to using only one of these techniques, regardless of the dataset characteristics.

Answer: A,C

Explanation:
Option A is correct: Combining both techniques can lead to a more balanced dataset, potentially improving the model's ability to learn patterns from both classes, if done correctly. Option C is correct: Down-sampling can exacerbate the risk of losing important information from the majority class, potentially leading to underfitting, especially if the majority class is already relatively small. Option B is incorrect because the effect depends on the data. Option D is incorrect because oversampling helps the model, even combined with downsampling, not to be prone to overfitting. Option E is incorrect because the right up/down-sampling ratio is very specific to the dataset.

NEW QUESTION # 135
You are building a machine learning model to predict loan defaults. You have a dataset in Snowflake with the following features: 'income' (annual income in USD), 'loan_amount' (loan amount in USD), and 'credit_score' (FICO score). You need to normalize these features before training your model. The data has outliers in both 'income' and 'loan_amount', and 'credit_score' has a roughly normal distribution but you still want to standardize it to have a mean of 0 and standard deviation of 1. You want to perform these normalizations using only SQL in Snowflake (no UDFs). Which of the following SQL transformations are most suitable?

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

Answer: A

Explanation:
Option C is the most suitable. Robust Scaling is appropriate for 'income' and 'loan_amount' due to the presence of outliers. Robust scaling, using IQR is less sensitive to extreme values than Min-Max or Z-score. Z-score standardization is suitable for 'credit_score' as it has a roughly normal distribution, and standardization is desired. Option A is incorrect since Min-Max scaling is highly sensitive to outliers. Option B is incorrect because Z-score is not outlier resilient and it doesn't take into account the data properties given for credit score. Log transformation and arcsinh transform can handle outliers, they're not as resilient as robust scaling. The arcsinh transformation is also useful for features that may have negative values, but we don't have that information here.

NEW QUESTION # 136
You are preparing a dataset in Snowflake for a K-means clustering algorithm. The dataset includes features like 'age', 'income' (in USD), and 'number of_transactions'. 'Income' has significantly larger values than 'age' and 'number of_transactions'. To ensure that all features contribute equally to the distance calculations in K-means, which of the following scaling approaches should you consider, and why? Select all that apply:

A. Apply RobustScaler to handle outliers and then StandardScaler or MinMaxScaler to further scale the features.
B. Apply PowerTransformer to transform income and StandardScaler to other features to handle skewness.
C. Do not scale the data, as K-means is robust to differences in feature scales.
D. Apply MinMaxScaler to all three features to scale them to a range between O and 1 .
E. Apply StandardScaler to all three features ('age', 'income', 'number_of_transactions') to center the data around zero and scale it to unit variance.

Answer: A,D,E

Explanation:
K-means clustering is sensitive to the scale of the features because it relies on distance calculations. Features with larger values will have a disproportionate influence on the clustering results. StandardScaler centers the data around zero and scales it to unit variance, which ensures that all features have a similar range and variance. MinMaxScaler scales the features to a range between O and 1, which also addresses the issue of different scales. RobustScaler handles outliers which will then use the other two scaling techniques. Therefore A, B and D are the appropriate scaling techniques. C is not correct as K-means relies on distance calculations and not scaling the data could give some feature a larger weight which isn't the desired outcome. Option E: Using PowerTransformer on 'income' to reduce skewness and StandardScaler on the other features can be a valid approach, but it depends on the distribution of 'income' and the presence of outliers. If 'income' is highly skewed and/or contains outliers, this combination might be more effective than using StandardScaler or MinMaxScaler alone.

NEW QUESTION # 137
You are building a multi-class classification model in Snowflake to predict the category of customer support tickets (e.g., 'Billing', 'Technical Support', 'Sales Inquiry', 'Account Management', 'Feature Request') based on the ticket's text content. The initial model evaluation shows an overall accuracy of 75%, but the 'Feature Request' category has a significantly lower precision and recall compared to other categories. Which of the following strategies would be MOST effective in addressing this issue, considering the limitations and advantages of Snowflake's data processing capabilities and typical machine learning practices?

A. Oversample the 'Feature Request' category in the training dataset before training the model. This involves creating synthetic data points or duplicating existing data to balance the class distribution. This can be done using SQL and Snowflake's internal stage for storing temporary data before training.
B. Engineer new features specifically designed to improve the model's ability to distinguish 'Feature Request' tickets from other categories. This could involve creating sentiment scores for 'innovation' or using topic modeling to identify key themes related to feature requests.
C. All of the above.
D. Apply a cost-sensitive learning approach during model training, assigning a higher misclassification cost to errors involving the 'Feature Request' category. This encourages the model to prioritize correctly classifying feature requests.
E. Increase the threshold for classifying a ticket as 'Feature Request' to improve precision, even if it further reduces recall. This prioritizes accurate identification of feature requests over capturing all of them.

Answer: C

Explanation:
All options are potentially beneficial. Increasing the threshold (A) improves precision. Oversampling (B) addresses class imbalance. Cost-sensitive learning (C) penalizes misclassification. Feature engineering (D) improves discrimination. Therefore, the optimal solution may involve combining these strategies. Oversampling can be implemented using SQL and INSERT INTO statements in Snowflake, storing the oversampled data in a temporary table. Cost-sensitive learning might involve adjusting model weights or using a custom loss function (depending on the chosen model framework, potentially requiring integration with external ML tools).

NEW QUESTION # 138
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