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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A data scientist is tasked with building a predictive maintenance model for industrial equipment. The data is collected from IoT sensors and stored in Snowflake. The raw sensor data is voluminous and contains noise, outliers, and missing values. Which of the following code snippets, executed within a Snowflake environment, demonstrates the MOST efficient and robust approach to cleaning and transforming this sensor data during the data collection phase, specifically addressing outlier removal and missing value imputation using robust statistics? Assume necessary libraries like numpy and pandas are available via Snowpark.

A)

B)

C)

D)

E)

2. Which of the following statements are TRUE regarding the 'Data Understanding' and 'Data Preparation' steps within the Machine Learning lifecycle, specifically concerning handling data directly within Snowflake for a large, complex dataset?

A) Data Understanding primarily involves identifying potential data quality issues like missing values, outliers, and inconsistencies, and Snowflake features like 'QUALIFY and 'APPROX TOP can aid in this process.
B) Data Preparation should always be performed outside of Snowflake using external tools to avoid impacting Snowflake performance.
C) The 'Data Understanding' step is unnecessary when working with data stored in Snowflake because Snowflake automatically validates and cleans the data during ingestion.
D) During Data Preparation, you should always prioritize creating a single, wide table containing all possible features to simplify the modeling process.
E) Data Preparation in Snowflake can involve feature engineering using SQL functions, creating aggregated features with window functions, and handling missing values using 'NVL' or 'COALESCE. Furthermore, Snowpark Python provides richer data manipulation using DataFrame APIs directly on Snowflake data.

3. 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) Rely solely on Snowflake's query history to identify failed predictions and debug the model, without any explicit error handling within the UDE
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) Use Snowflake's event tables to capture errors and audit logs related to the UDF execution.
D) Wrap the prediction call in a 'SYSTEM$QUERY_PROFILE function to get detailed query execution statistics and identify potential performance bottlenecks.
E) 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

4. You are working with a dataset containing timestamps representing website user activity. The timestamps are stored as strings in the format 'YYYY-MM-DD HH:MI:SS.SSSSSS' in a Snowflake table named 'website_activity'. You need to extract the hour of the day from these timestamps and encode it as a cyclical feature using sine and cosine transformations. This is to capture the cyclical nature of user activity throughout the day (e.g., 23:00 and 00:00 are close in time). Which of the following Snowflake SQL code snippets correctly implements this cyclical encoding and creates the 'hour_sin' and 'hour_cos' columns?

A)

B)

C)

D)

E)

5. You are tasked with analyzing the 'transaction amounts' column in the 'sales data' table to understand its variability across different geographical regions. You need to calculate the variance of transaction amounts for each region. However, some regions have very few transactions, which can skew the variance calculation. Which of the following SQL statements correctly calculates the variance for each region, excluding regions with fewer than 10 transactions, using Snowflake's native statistical functions?