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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q144-Q149):

NEW QUESTION # 144
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. There might be a confounding variable correlated with both 'number_of_products' and churn, leading to a spurious relationship in the PDP.
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. The PDP indicates a high degree of interaction between 'number_of_products' and other features in the model, making the interpretation unreliable.
E. Customers who purchase more than 3 products are less likely to churn, suggesting higher engagement or satisfaction.

Answer: A,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 # 145
You are working with a Snowflake table 'CUSTOMER DATA containing customer information for a marketing campaign. The table includes columns like 'CUSTOMER ID', 'FIRST NAME', 'LAST NAME, 'EMAIL', 'PHONE NUMBER, 'ADDRESS, 'CITY, 'STATE, ZIP CODE, 'COUNTRY, 'PURCHASE HISTORY, 'CLICKSTREAM DATA, and 'OBSOLETE COLUMN'. You need to prepare this data for a machine learning model focused on predicting customer churn. Which of the following strategies and Snowpark Python code snippets would be MOST efficient and appropriate for removing irrelevant fields and handling potentially sensitive personal information while adhering to data governance policies? Assume data governance requires removing personally identifiable information (PII) that isn't strictly necessary for the churn model.

A. Keeping all columns as is and providing access to Data Scientists without any changes, relying on role based security access controls only.
B. Dropping 'FIRST NAME, UST NAME, 'EMAIL', 'PHONE NUMBER, 'ADDRESS', 'CITY, 'STATE', ZIP CODE, 'COUNTRY and 'OBSOLETE_COLUMN' columns directly using 'LAST_NAME', 'EMAIL', 'PHONE_NUMBER', 'ADDRESS', 'CITY', 'STATE', 'ZIP_CODE', 'COUNTRY', without any further consideration.
C. Drop 'OBSOLETE_COLUMN'. For columns like and 'LAST_NAME' , consider aggregating into a single 'FULL_NAME feature if needed for some downstream task. Apply hashing or tokenization techniques to sensitive PII columns like and 'PHONE NUMBER using Snowpark UDFs, depending on the model's requirements. Drop columns like 'ADDRESS, 'CITY, 'STATE, ZIP_CODE, 'COUNTRY as they likely do not contribute to churn prediction. Example hashing function:
D. Dropping columns 'OBSOLETE_COLUMN' directly. Then, for PII columns ('FIRST_NAME, 'LAST_NAME, 'EMAIL', 'PHONE_NUMBER, 'ADDRESS', 'CITY', 'STATE' , , 'COUNTRY), create a separate table with anonymized or aggregated data for analysis unrelated to the churn model. Use Keep all PII columns but encrypt them using Snowflake's built-in encryption features to comply with data governance before building the model. Drop 'OBSOLETE COLUMN'.

Answer: A

Explanation:
Option D is the most comprehensive and adheres to best practices. It identifies and removes truly irrelevant columns ('OBSOLETE_COLUMN', and location details), handles PII appropriately using hashing and tokenization (or aggregation), and leverages Snowpark UDFs for custom data transformations. Options A is too simplistic and doesn't consider data governance. Option B is better than A, but more complex than needed if the data is not needed elsewhere. Option C doesn't address the principle of minimizing data exposure. Option E is unacceptable from a data governance and security perspective. The example code demonstrates how to register a UDF for hashing email addresses.

NEW QUESTION # 146
You've built a customer churn prediction model in Snowflake, and are using the AUC as your primary performance metric. You notice that your model consistently performs well (AUC > 0.85) on your validation set but significantly worse (AUC < 0.7) in production. What are the possible reasons for this discrepancy? (Select all that apply)

A. Your training and validation sets are not representative of the real-world production data due to sampling bias.
B. Your model is overfitting to the validation data. This causes to give high performance on validation set but less accurate in the real world.
C. There's a temporal bias: the customer behavior patterns have changed since the training data was collected.
D. The AUC metric is inherently unreliable and should not be used for model evaluation.
E. The production environment has significantly more missing data compared to the training and validation environments.

Answer: A,B,C,E

Explanation:
A, B, C, and D are all valid reasons for performance degradation in production. Sampling bias (A) means the training/validation data doesn't accurately reflect the production data. Temporal bias (B) arises when customer behavior changes over time. Overfitting (C) leads to good performance on the training/validation set but poor generalization to new data. Missing data (D) can negatively impact the model's ability to make accurate predictions. AUC is a reliable metric, especially when combined with other metrics, so E is incorrect.

NEW QUESTION # 147
You've trained a sentiment analysis model in Snowflake using Snowpark Python and deployed it as a UDF. After several weeks, you notice the model's performance has degraded significantly. You suspect concept drift. Which of the following actions represent the MOST effective and comprehensive approach to address this situation, considering the entire Machine Learning Lifecycle, including monitoring, retraining, and model versioning? Assume you have monitoring in place that alerted you to the drift.

A. Immediately replace the current UDF with a newly trained model using the latest data, ignoring model versioning and assuming the latest data will solve the drift issue.
B. Disable the model and revert to a rule-based system, abandoning the machine learning approach altogether.
C. Adjust the existing model's parameters manually to compensate for the observed performance degradation without retraining or versioning.
D. Analyze the recent data to understand the nature of the concept drift, retrain the model with a combination of historical and recent data, version the new model, and perform AIB testing against the existing model before fully deploying the new version. Log both model version predictions during AIB testing.
E. Retrain the model on a sample of the most recent data, overwriting the original model files in your Snowflake stage and updating the UDF definition. Keep no record of the old model.

Answer: D

Explanation:
Addressing concept drift requires careful analysis, retraining with appropriate data (historical + recent), and controlled deployment using A/B testing. Model versioning ensures that you can rollback if the new model performs poorly. Logging the predictions of both models assists in further performance analysis. Directly replacing (option A) or manually adjusting (option C) are risky without proper evaluation. Abandoning the ML approach (option D) is a last resort. Option E lacks model versioning and thus risks complete loss of the older model which is a common practice violation in ML engineering.

NEW QUESTION # 148
A Snowflake table named 'SALES DATA contains a 'TRANSACTION DATE column stored as VARCHAR. The data in this column is inconsistent; some rows have dates in 'YYYY-MM-DD' format, others in 'MM/DD/YYYY' format, and some contain invalid date strings like 'N/A'. You need to standardize all dates to 'YYYY-MM-DD' format and store them in a new column called FORMATTED DATE in a new table 'STANDARDIZED_SALES DATA. Which of the following approaches, using Snowpark Python and SQL, most effectively handles these inconsistencies and minimizes errors during data transformation? Select all that apply:

A. Employing Snowpark's error handling mechanism (e.g., 'try...except' blocks) within a loop to iteratively convert each date string, catching and logging errors, and storing valid dates in a new column.
B. Using a Snowpark Python UDF to parse each date string individually, handling different formats with conditional logic, and returning a formatted date string. This provides flexibility in handling diverse date formats.
C. Using a series of DATE" and 'TO_VARCHAR SQL functions in Snowpark to attempt converting the date in different formats and then formatting the result to 'YYYY-MM-DD'. Any conversion failing returns NULL.
D. Creating a view on top of 'SALES_DATA' that implements the conversion logic. This avoids creating a new physical table immediately and allows for experimentation with different conversion strategies before materializing the data.
E. Using a single 'TO_DATE function with format parameter set to 'AUTO' combined with 'TO_VARCHAR to format the date to 'YYYY-MM-DD'.

Answer: C,D

Explanation:
Options B and D are the most effective. Option B uses with different formats to handle inconsistencies. If a format fails, it returns NULL, providing a clean way to handle invalid dates. Combining this with VARCHAR formats the valid dates to 'YYYY-MM-DD'. Option D suggests creating a view. Views are useful for testing transformation logic without immediately impacting the base table, allowing experimentation before committing to a data transformation pipeline. Materializing the data into a table would be a subsequent step, after verifying the transformation's correctness. Option A, while flexible, is less performant because UDFs (User-Defined Functions) generally add overhead compared to built-in SQL functions. Option C is inefficient and not a recommended practice in Snowpark for vectorized operations. Option E will not work in most of the cases, as the AUTO parameter cannot reliably differentiate all provided formats. Furthermore, it does not account for data quality issues where there is no date format.

NEW QUESTION # 149
......

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