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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A data scientist uses bootstrapping to estimate the sampling distribution of a statistic calculated from a dataset stored in Snowflake. They observe that the bootstrap distribution is significantly different from the original data distribution. Which of the following statements best describes the possible reasons for this difference, considering both the theoretical underpinnings of bootstrapping and potential limitations?

A) The statistic being estimated is inherently unstable and has a high variance, causing the bootstrap distribution to be wider and potentially different in shape compared to the original data distribution. This is a normal outcome when dealing with such statistics.
B) Bootstrapping always provides accurate estimates of sampling distributions, any significant difference indicates an error in the code implementation.
C) The original sample may not be representative of the population, and the bootstrap procedure is simply amplifying the biases present in the original sample. Additionally, the statistic itself may be highly sensitive to outliers or specific data points, leading to a distorted bootstrap distribution.
D) The difference is unexpected; the bootstrap distribution should always closely resemble the original data distribution, regardless of the statistic being estimated.
E) Bootstrapping is only appropriate for normally distributed data; if the original data is not normal, the bootstrap distribution will inevitably differ significantly.

2. You are a data scientist working with a Snowflake table named 'CUSTOMER DATA' that contains a 'PHONE NUMBER' column stored as VARCHAR. The 'PHONE NUMBER' column sometimes contains non-numeric characters like hyphens and parentheses, and in some rows the data is missing. You need to create a new table 'CLEANED CUSTOMER DATA' with a column named 'CLEANED PHONE NUMBER that contains only the numeric part of the phone number (as VARCHAR) and replaces missing or invalid phone numbers with NULL. Which of the following Snowpark Python code snippets achieves this most efficiently, ensuring no errors occur during the data transformation, and considers Snowflake's performance best practices?

A) Option B
B) Option D
C) Option C
D) Option A
E) Option E

3. You're building a regression model using Snowpark Python to predict house prices. After initial training, you observe that the model consistently overestimates the prices of high-value houses and underestimates the prices of low-value houses. Given the options below, which optimization metric, along with code snippet to calculate it using Snowpark, would be most effective in addressing this specific issue?

A) Root Mean Squared Error (RMSE) - as it gives more weight to larger errors, making it suitable for addressing the underestimation/overestimation problem.

B) Adjusted R-squared - as it penalizes the addition of irrelevant features, improving the model's generalization ability.

C) R-squared - as it measures the proportion of variance explained, directly addressing how well the model fits the data across all price ranges.

D) Mean Absolute Error MAE - as it is sensitive to outliers and will penalize large errors more heavily.

E) Mean Squared Error (MSE) - as it is less sensitive to outliers than RMSE.

4. You are investigating website session durations stored in a Snowflake table named 'WEB SESSIONS. You suspect that bot traffic is artificially inflating the average session duration. You have the following session durations (in seconds) in the 'SESSION DURATION' column: [10, 12, 15, 18, 20, 22, 25, 28, 30, 1000]. Given this data and the context of bot traffic, which measure of central tendency is MOST robust to the influence of the outlier (1000) in this dataset? Assuming you already have table and dataframe created for this analysis. (Choose ONE)

A) Mode
B) Mean
C) Median
D) Trimmed mean (e.g. 10% trimmed)
E) Geometric Mean

5. You have built an external function to train a PyTorch model using SageMaker. The model training process requires a significant amount of CPU and memory. The training data is passed from Snowflake to the external function in batches. The external function code in AWS Lambda is as follows:

The Snowflake external function is defined as follows:

During testing, you encounter '500 Internal Server Error' from the external function consistently. Upon inspection of the Lambda logs, you find messages indicating 'PayloadTooLargeError'. What is the most likely cause and how do you mitigate it within the context of Snowflake and AWS Lambda?

A) The Snowflake external function definition is incorrect. Change the 'RETURNS VARIANT clause to 'RETURNS VARCHAR as the Lambda function returns a JSON string.
B) The IAM role associated with the Lambda function lacks the necessary permissions to invoke the SageMaker training job. Grant the Lambda function's IAM role the appropriate SageMaker permissions.
C) The Lambda function is timing out before the model training can complete. Increase the Lambda function's timeout setting to allow sufficient time for the training process.
D) The size of the data being sent from Snowflake to the Lambda function exceeds the maximum payload size allowed by AWSAPI Gateway. Increase the maximum payload size limit in the API Gateway settings.
E) The size of the data being sent from Snowflake to the Lambda function exceeds the maximum payload size allowed by AWS API Gateway. Implement data partitioning in Snowflake and send smaller batches of data to the Lambda function, aggregating the results in a separate table.

Solutions: