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Snowflake SnowPro Advanced: Data Scientist Certification Exam Sample Questions (Q25-Q30):

NEW QUESTION # 25
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 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.
B. 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.
C. 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.
D. 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.
E. 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.

Answer: C

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 # 26
A data scientist is developing a model within a Snowpark Python environment to predict customer churn. They have established a Snowflake session and loaded data into a Snowpark DataFrame named 'customer data'. The feature engineering pipeline requires a custom Python function, 'calculate engagement_score', to be applied to each row. This function takes several columns as input and returns a single score representing customer engagement. The data scientist wants to apply this function in parallel across the entire DataFrame using Snowpark's UDF capabilities. The following code snippet is used to define and register the UDF:

When the UDF is called the above error is observed. What change needs to be applied to make the UDF work as expected?

A. Add '@F.sproc' decorator before the function definition.
B. Change the function call to use the Snowpark DataFrame's 'select' function with column objects: 'customer_data.select(engagement_score_udf(F.col('num_transactions'), F.col('avg_transaction_value'),
C. Redefine the function to accept string arguments and cast them to the correct data types within the function.
D. Remove argument from 'session.udf.register' call. Snowpark can infer the input types automatically.
E. Wrap the Python function inside a stored procedure using @F.sproc' and call that stored procedure instead of the plain python function.

Answer: B

Explanation:
The error message 'UDFArgumentException: Invalid argument types for function 'calculate_engagement_score_udf. Expected arguments: ONT, FLOAT, INT], actual arguments: [COLUMN_NAME, COLUMN_NAME, COLUMN_NAME]' indicates that the UDF is receiving column objects instead of the actual data values. This is because when calling the UDF on a Snowpark DataFrame, you need to explicitly reference the columns using The correct way to apply the UDF to the DataFrame is to use the 'select' function with ' F.col()' to pass the column objects as arguments to the UDF.

NEW QUESTION # 27
You are building a fraud detection model using transaction data stored in Snowflake. The dataset includes features like transaction amount, merchant category, location, and time. Due to regulatory requirements, you need to ensure personally identifiable information (PII) is handled securely and compliantly during the data collection and preprocessing phases. Which of the following combinations of Snowflake features and techniques would be MOST suitable for achieving this goal?

A. Encrypt the entire database containing the transaction data to protect PII from unauthorized access.
B. Use Snowflake's masking policies to redact PII columns before any data is accessed for model training. Ensure role-based access control is configured so that only authorized personnel can access the unmasked data for specific purposes.
C. Create a view that selects only the non-PII columns for model training. Grant access to this view to the data science team.
D. Apply differential privacy techniques on aggregated data derived from the transaction data, before using it for model training. Combine this with Snowflake's row access policies to restrict access to sensitive transaction records based on user roles and data attributes.
E. Use Snowflake's data sharing capabilities to share the transaction data with a third-party machine learning platform for model development, without any PII masking or redaction.

Answer: B,D

Explanation:
Options A and E are the MOST suitable. Option A directly addresses PII protection by leveraging Snowflake's masking policies to redact sensitive data before it is used for model training. Role-based access control provides an additional layer of security by limiting access to the unmasked data. Option E applies differential privacy to protect individual transaction data while still enabling useful model training and combines it with Row Access policies to restrict access to sensitive transaction records. Option B is partially correct but insufficient, as it only addresses which columns are seen, not protection within those columns. Option C protects the entire database but doesn't address PII handling during model training. Option D is highly risky and non-compliant, as it exposes PII to a third party without adequate protection.

NEW QUESTION # 28
You are validating a time series forecasting model for daily sales using Snowflake and Snowpark. The residuals plot shows a clear sinusoidal pattern. Which of the following actions should you consider to improve your model? (Select all that apply)

A. Remove outlier data points to improve overall model performance.
B. Change the algorithm to a linear regression model, since it is more likely to capture sinusoidal patterns
C. Apply a Box-Cox transformation to the target variable (sales) to stabilize the variance.
D. Increase the regularization strength in your model.
E. Incorporate lagged features representing previous sales values (e.g., sales from the previous day, week, or month).

Answer: C,E

Explanation:
A sinusoidal pattern in the residuals indicates that the model is not adequately capturing the seasonal patterns in the data. Incorporating lagged features (Option B) allows the model to learn from past sales trends. A Box-Cox transformation (Option C) can help stabilize the variance and improve the model's fit. Increasing regularization (Option A) or removing outliers (Option D) might help in some cases, but they are not the primary solutions for a sinusoidal pattern. Linear regression models are unlikely to capture sinusoidal patterns, so Option E is wrong.

NEW QUESTION # 29
You are tasked with building a Python stored procedure in Snowflake to train a Gradient Boosting Machine (GBM) model using XGBoost.
The procedure takes a sample of data from a large table, trains the model, and stores the model in a Snowflake stage. During testing, you notice that the procedure sometimes exceeds the memory limits imposed by Snowflake, causing it to fail. Which of the following techniques can you implement within the Python stored procedure to minimize memory consumption during model training?

A. Write the training data to a temporary table in Snowflake, then use Snowflake's external functions to train the XGBoost model on a separate compute cluster outside of Snowflake. Then upload the model to snowflake stage.
B. Reduce the sample size of the training data and increase the number of boosting rounds to compensate for the smaller sample. Use the 'predict_proba' method to avoid storing probabilities for all classes.
C. Convert the Pandas DataFrame used for training to a Dask DataFrame and utilize Dask's distributed processing capabilities to train the XGBoost model in parallel across multiple Snowflake virtual warehouses.
D. Use the 'hist' tree method in XGBoost, enable gradient-based sampling ('gosS), and carefully tune the 'max_depth' and parameters to reduce memory usage during tree construction. Convert all features to numerical if possible.
E. Implement XGBoost's 'early stopping' functionality with a validation set to prevent overfitting. If the stored procedure exceeds the memory limits, the model cannot be saved. Always use larger virtual warehouse.

Answer: D

Explanation:
Option B is the MOST effective way to minimize memory consumption within the Python stored procedure. The 'hist' tree method in XGBoost uses a histogram-based approach for finding the best split points, which is more memory-efficient than the exact tree method. Gradient- based sampling ('goss') reduces the number of data points used for calculating the gradients, further reducing memory usage. Tuning 'max_depth' and helps to control the complexity of the trees, preventing them from growing too large and consuming excessive memory. Converting categorical features to numerical is crucial as categorical features when One Hot Encoded, can explode feature space and significantly increase memory footprint. Option A will not work directly within Snowflake as Dask is not supported on warehouse compute. Option C may reduce the accuracy of the model. Option D requires additional infrastructure and complexity. Option E doesn't directly address the memory issue during the training phase, although early stopping is a good practice, the underlying memory pressure will remain.

NEW QUESTION # 30
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