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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A Data Scientist is designing a machine learning model to predict customer churn for a telecommunications company. They have access to various data sources, including call logs, billing information, customer demographics, and support tickets, all residing in separate Snowflake tables. The data scientist aims to minimize bias and ensure data quality during the data collection phase. Which of the following strategies would be MOST effective for collecting and preparing the data for model training?

A) Use Snowflake's Data Marketplace to supplement the existing data with external datasets, regardless of their relevance to the churn prediction problem.
B) Directly use all available columns from each table without any preprocessing to avoid introducing bias.
C) Perform exploratory data analysis (EDA) on each table to identify relevant features and potential biases. Use feature selection techniques to reduce dimensionality. Implement robust data validation checks to ensure data quality and consistency before joining the tables. Handle missing values strategically based on the specific column and its potential impact on the model.
D) Randomly select a subset of data from each table to reduce computational complexity and speed up model training.
E) Create a single, wide table by performing a series of INNER JOINs on all tables using customer ID as the primary key. Handle missing values by imputing with the mean for numerical columns and 'Unknown' for categorical columns.

2. You are tasked with deploying a pre-trained sentiment analysis model hosted externally using AWS SageMaker. The model endpoint requires an API key for authentication, and you want to score customer reviews stored in a Snowflake table named 'CUSTOMER REVIEWS. Which of the following steps are necessary to securely and efficiently integrate this external model with Snowflake, assuming you have already created a Snowflake stage to store secrets?

A) Create a secret object in Snowflake to store the API key. Grant appropriate privileges on the secret to the role that will execute the external function. Modify external function that references secure external stage.
B) Store the API key in an environment variable within the AWS Lambda function (if using API Gateway) that serves as an intermediary between Snowflake and SageMaker. Snowflake calls the API Gateway endpoint which relays the request to the SageMaker endpoint, and no specific configuration is needed on snowflake.
C) Use Snowflake's external functions to directly call the SageMaker endpoint from a SQL query, passing the customer review text as input. No separate secure external stage configuration is needed as long as Snowflake has internet access.
D) Create an external function in Snowflake that invokes the SageMaker endpoint, hardcoding the API key directly into the function definition for simplicity.
E) Create an external function in Snowflake that retrieves the API key from a secure Snowflake secret object. Grant USAGE privilege on the secret to the service account associated with the external function.

3. You are analyzing customer transaction data in Snowflake to identify fraudulent activities. The 'TRANSACTION AMOUNT' column exhibits a right-skewed distribution. Which of the following Snowflake queries is MOST effective in identifying outliers based on the Interquartile Range (IQR) method, specifically targeting unusually large transaction amounts? Assume IQR is already calculated as variable and QI as and Q3 as in snowflake session.

A) SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION_AMOUNT > (SELECT MEDIAN(TRANSACTION AMOUNT) FROM TRANSACTIONS);
B) SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION_AMOUNT < qi - (1.5 iqr);
C) SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION AMOUNT > q3 + (1.5 iqr);
D) SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION_AMOUNT > (SELECT + 3 FROM TRANSACTIONS);
E) SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION_AMOUNT > (SELECT WITHIN GROUP (ORDER BY TRANSACTION_AMOUNT) FROM TRANSACTIONS);

4. You have successfully deployed a machine learning model in Snowflake using Snowpark and are generating predictions. You need to implement a robust error handling mechanism to ensure that if the model encounters an issue during prediction (e.g., missing feature, invalid data type), the process doesn't halt and the errors are logged appropriately. You are using a User-Defined Function (UDF) to call the model. Which of the following strategies, when used IN COMBINATION, provides the BEST error handling and monitoring capabilities in this scenario?

A) Use Snowflake's event tables to capture errors and audit logs related to the UDF execution.
B) Use a 'TRY...CATCH' block within the UDF to catch exceptions, log the errors to a separate Snowflake table, and return a default prediction value (e.g., NULL) for the affected row.
C) Rely solely on Snowflake's query history to identify failed predictions and debug the model, without any explicit error handling within the UDE
D) Implement a custom logging solution by writing error messages to an external file storage (e.g., AWS S3) using an external function called from within the UDE
E) Wrap the prediction call in a 'SYSTEM$QUERY_PROFILE function to get detailed query execution statistics and identify potential performance bottlenecks.

5. A pharmaceutical company is testing a new drug to lower blood pressure. They conduct a clinical trial with 200 patients. After treatment, the sample mean reduction in systolic blood pressure is 10 mmHg, with a sample standard deviation of 15 mmHg. You want to construct a 99% confidence interval for the true mean reduction in systolic blood pressure. Which of the following statements is most accurate concerning the appropriate distribution and critical value to use?

A) Use a z-distribution because the sample size is large (n > 30), and the critical value is approximately 2.576.
B) Use a z-distribution because we are estimating mean, and use a critical value of 1.96.
C) Use a t-distribution with 200 degrees of freedom, and the critical value is close to 2.576.
D) Use a chi-squared distribution with 199 degrees of freedom.
E) Use a t-distribution with 199 degrees of freedom, and the critical value is slightly larger than 2.576.

Solutions: