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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q204-Q209):

NEW QUESTION # 204
You are a data scientist working with a Snowflake table named 'CUSTOMER TRANSACTIONS' that contains sensitive PII data, including customer names and email addresses. You need to create a representative sample of 1% of the data for model development, ensuring that the sample is anonymized and protects customer privacy. The sample must be reproducible for future model iterations.
Which of the following steps are most appropriate using Snowpark for Python and SQL?

A. Use Snowpark DataFrame's 'sample' function with a fraction of 0.01 and a fixed random seed. Before sampling, create a view that masks 'customer_name' and 'email_address' columns, and then sample from the view.
B. Employ stratified sampling based on a customer segment column, then anonymize data. Use the TABLESAMPLE BERNOULLI function in SQL with a 1 percent sample rate. Apply SHA256 hashing to the 'customer_name' and 'email_addresS columns using SQL functions.
C. Create a new table using 'CREATE TABLE AS SELECT statement combined with 'SAMPLE clause and SHA256 hashing functions in SQL to create the sample and anonymize data. Manually seed the random number generator in Python before executing the SQL statement via Snowpark.
D. Use the 'QUALIFY OVER (ORDER BY RANDOM()) (SELECT COUNT( ) 0.01 FROM CUSTOMER_TRANSACTIONS)' clause with SHA256 on sensitive columns directly within a CREATE TABLE AS statement to generate an anonymized sample. The function should return only 1 percentage of row.
E. Use the 'SAMPLE clause in a SQL query to extract 1% of the rows, then apply SHA256 hashing to the 'customer_name' and 'email_addresS columns within Snowpark using a UDF. Seed the sampling for reproducibility.

Answer: B,E

Explanation:
Options A and D are correct because they address both sampling and anonymization requirements while leveraging Snowflake's capabilities. Option A utilizes SAMPLE clause within a SQL query in Snowflake and then leverages UDF for SHA256 hashing of sensitive information. This is a practical and common data sampling/anonymization pattern. Option D employs stratified sampling based on a customer segment, TABLESAMPLE BERNOULLI and SHA256 hashing in SQL, which provides a solid anonymization and sampling strategy.Option B: Creating a view is a good practice, but it doesn't automatically anonymize the data, and directly sampling from the view without anonymization doesn't meet the security requirements. Option C: Manually seeding in Python doesn't guarantee reproducibility when SQL is executed separately, as Snowflake has its own random number generator. Option E does not guarantee reproduciblity, and the query complexity might introduce performance issues and is less readable compared to the other options.

NEW QUESTION # 205
A financial institution aims to detect fraudulent transactions using a Supervised Learning model deployed in Snowflake. They have a dataset with transaction details, including amount, timestamp, merchant category, and customer ID. The target variable is 'is_fraudulent' (0 or 1). They are considering different Supervised Learning algorithms. Which of the following algorithms would be MOST suitable for this fraud detection task, considering the need for interpretability, scalability, and the potential for imbalanced classes, and what specific strategies can be employed within Snowflake to handle the class imbalance?

A. Decision Tree or Random Forest, combined with techniques like oversampling the minority class (fraudulent transactions) within Snowflake using SQL or UDFs to balance the dataset before training. These models provide reasonable interpretability and can handle non-linear relationships effectively.
B. K-Nearest Neighbors (KNN), because it is simple to implement and doesn't require extensive training.
C. Naive Bayes, because it requires no hyperparameter tuning and works well on numerical data.
D. Linear Regression, because it's computationally efficient and easy to understand, even though fraud detection is a classification problem.
E. Support Vector Machine (SVM) with a radial basis function (RBF) kernel, as it can capture complex non-linear relationships without concern for interpretability.

Answer: A

Explanation:
Decision Trees and Random Forests are well-suited for fraud detection due to their ability to handle non-linear relationships and provide interpretability. The class imbalance problem (where fraudulent transactions are much rarer than legitimate ones) is a common challenge in fraud detection. Oversampling the minority class or using techniques like SMOTE within Snowflake before training can significantly improve the model's performance. KNN is not well-suited for high-dimensional data or imbalanced datasets. SVM can be computationally expensive and lacks interpretability. Linear Regression is inappropriate for a classification problem. Naive Bayes makes strong independence assumptions that may not hold in fraud detection scenarios.

NEW QUESTION # 206
You're building a fraud detection model and want to determine if the average transaction amount for fraudulent transactions is significantly higher than the average transaction amount for legitimate transactions. You have two tables in Snowflake:
'FRAUDULENT TRANSACTIONS and 'LEGITIMATE TRANSACTIONS, both with a 'TRANSACTION AMOUNT column. You believe that FRAUDULENT TRANSACTIONS contains fewer than 30 transactions. You don't know the population standard deviations. What are the proper steps to conduct the hypothesis test, and what is the correct hypothesis statement?

A. Perform a t-test. Null Hypothesis: The average transaction amount for fraudulent transactions is less than or equal to the average transaction amount for legitimate transactions. Alternative Hypothesis: The average transaction amount for fraudulent transactions is greater than the average transaction amount for legitimate transactions.
B. Perform a chi-squared test. Null Hypothesis: There is no relationship between transaction amount and whether a transaction is fraudulent. Alternative Hypothesis: There is a relationship between transaction amount and whether a transaction is fraudulent.
C. Perform a Z-test. Null Hypothesis: The average transaction amount for fraudulent transactions is equal to the average transaction amount for legitimate transactions. Alternative Hypothesis: The average transaction amount for fraudulent transactions is not equal to the average transaction amount for legitimate transactions.
D. Perform a Z-test. Null Hypothesis: The average transaction amount for fraudulent transactions is less than or equal to the average transaction amount for legitimate transactions. Alternative Hypothesis: The average transaction amount for fraudulent transactions is greater than the average transaction amount for legitimate transactions.
E. Perform a t-test. Null Hypothesis: The average transaction amount for fraudulent transactions is equal to the average transaction amount for legitimate transactions. Alternative Hypothesis: The average transaction amount for fraudulent transactions is not equal to the average transaction amount for legitimate transactions.

Answer: A

Explanation:
The correct answer is C. Since the sample size for fraudulent transactions is less than 30, and the population standard deviations are unknown, a t-test is more appropriate than a Z-test. The null hypothesis should state the assumption that we are trying to disprove (i.e., fraudulent transactions are not, on average, higher). The alternative hypothesis is the claim we are trying to support (i.e., fraudulent transactions ARE, on average, higher). The chi-squared test is used for categorical data, not continuous data like transaction amount. We are interested in knowing if one set of transaction amounts is greater, so its a one tailed t test.

NEW QUESTION # 207
You are tasked with deploying a fraud detection model in Snowflake using the Model Registry. The model is trained on a dataset that is updated daily. You need to ensure that your deployed model uses the latest approved version and that you can easily roll back to a previous version if any issues arise. Which of the following approaches would provide the most robust and maintainable solution for model versioning and deployment, considering minimal downtime during updates and rollback?

A. Use Snowflake Tasks to periodically refresh a table containing the latest model weights. The UDF directly queries this table for predictions.
B. Store all model versions within a single model registry entry without versioning, overwriting the existing file with each new training run.
C. Create multiple Snowflake UDFs, each corresponding to a different model version. Manually switch the active UDF by updating application code when a new model is deployed.
D. Deploy a new Snowflake UDF referencing the model file directly in cloud storage every time the model is retrained. Rely on cloud storage versioning for rollback.
E. Register each new model version in the Snowflake Model Registry and promote the desired version to 'PRODUCTION' stage. Update a single UDF that dynamically fetches the model based on the 'PRODUCTION' stage metadata.

Answer: E

Explanation:
Option B provides the most robust and maintainable solution. Registering each model version in the Snowflake Model Registry allows for easy tracking and rollback. Promoting the desired version to 'PRODUCTION' and dynamically fetching the model in a UDF based on this metadata ensures minimal downtime during updates and rollbacks. Option A relies on cloud storage versioning, which is less integrated with Snowflake's metadata management. Option C requires manual UDF switching, which is error-prone. Option D doesn't utilize the Model Registry effectively. Option E eliminates the benefits of version control.

NEW QUESTION # 208
You are tasked with validating a regression model predicting customer lifetime value (CLTV). The model uses various customer attributes, including purchase history, demographics, and website activity, stored in a Snowflake table called 'CUSTOMER DATA. You want to assess the model's calibration specifically, whether the predicted CLTV values align with the actual observed CLTV values over time. Which of the following evaluation techniques would be MOST suitable for assessing the calibration of your CLTV regression model in Snowflake?

A. Calculate the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) on a hold-out test set to quantify the overall prediction accuracy.
B. Calculate the R-squared score on a hold-out test set to assess the proportion of variance in the actual CLTV explained by the model.
C. Conduct a Kolmogorov-Smirnov test to check the distribution of predicted and actual value.
D. Evaluate the model's residuals by plotting them against the predicted values and checking for patterns or heteroscedasticity.
E. Create a calibration curve (also known as a reliability diagram) by binning the predicted CLTV values, calculating the average predicted CLTV and the average actual CLTV within each bin, and plotting these averages against each other.

Answer: E

Explanation:
Option B is the most suitable technique for assessing calibration. A calibration curve directly visualizes the relationship between predicted and actual values, allowing you to see if the model is systematically over- or under-predicting CLTV for different ranges of predicted values. Options A, C, and D are useful for assessing overall accuracy and model fit but do not directly address calibration. MAE and RMSE (A) measure overall error magnitude. Residual analysis (C) can reveal problems with model assumptions. R-squared (D) measures the explained variance, not calibration. Option E measures whether two samples follow the same distribution, however, it would not be most suitable for assessing calibration of your CLTV regression model.

NEW QUESTION # 209
......

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DSA-C03 Reliable Exam Tutorial - New DSA-C03 Braindumps Questions

New DSA-C03 Braindumps Questions - Pdf DSA-C03 Format

Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q204-Q209):

SnowPro Advanced: Data Scientist Certification Exam Latest Pdf Material & DSA-C03 Valid Practice Files & SnowPro Advanced: Data Scientist Certification Exam Updated Study Guide

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