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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are tasked with predicting the sales price of houses based on their size (square footage) using linear regression in Snowflake. You have a table named 'HOUSE PRICES' with columns 'SQUARE FOOTAGE' and 'SALES PRICE'. You want to calculate the slope and intercept using Snowflake SQL. Which of the following queries, considering potential NULL values in the data, is the MOST robust and statistically sound for calculating the slope and intercept for a simple linear regression model?

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

2. You are deploying a fraud detection model hosted on a third-party ML platform and accessing it via an external function in Snowflake. The model API has a strict rate limit of 10 requests per second. To prevent exceeding this limit and ensure smooth operation, what strategies could you implement within Snowflake, considering performance and cost implications? Select all that apply.

A) Implement a retry mechanism within the external function definition to handle API rate limit errors (e.g., HTTP 429 errors) using exponential backoff.
B) Utilize Snowflake's built-in caching mechanism for the external function results. This reduces the number of calls to the external API for repeated input data.
C) Implement a UDF (User-Defined Function) that sleeps for 0.1 seconds before each call to the external function. This guarantees a maximum rate of 10 requests per second.
D) Scale up the Snowflake virtual warehouse to the largest size possible. This will allow for more concurrent requests without exceeding the rate limit.
E) Implement a custom queueing system within Snowflake using temporary tables and stored procedures to batch requests and send them to the external function at a controlled rate.

3. A data scientist is tasked with predicting customer churn for a telecommunications company using Snowflake. The dataset contains call detail records (CDRs), customer demographic information, and service usage data'. Initial analysis reveals a high degree of multicollinearity between several features, specifically 'total_day_minutes', 'total_eve_minutes', and 'total_night_minutes'. Additionally, the 'state' feature has a large number of distinct values. Which of the following feature engineering techniques would be MOST effective in addressing these issues to improve model performance, considering efficient execution within Snowflake?

A) Apply Principal Component Analysis (PCA) to reduce the dimensionality of the CDR features ('total_day_minutes', 'total_eve_minutes', 'total_night_minutes') and use one-hot encoding for the 'state' feature.
B) Use a variance threshold to remove highly correlated CDR features and create a feature representing the geographical region (e.g., 'Northeast', 'Southwest') based on the 'state' feature using a custom UDF.
C) Calculate the Variance Inflation Factor (VIF) for each CDR feature and drop the feature with the highest VIE Apply frequency encoding to the 'state' feature.
D) Create interaction features by multiplying 'total_day_minutes' with 'customer_service_calls' and applying a target encoding to the 'state' feature.
E) Apply min-max scaling to the CDR features to normalize them and use label encoding for the 'state' feature. Train a decision tree model, as it is robust to multicollinearity.

4. You are using a Snowflake Notebook to analyze customer churn for a telecommunications company. You have a dataset with millions of rows and want to perform feature engineering using a combination of SQL transformations and Python code. Your goal is to create a new feature called 'average_monthly call_duration' which calculates the average call duration for each customer over the last 3 months. You are using the Snowpark DataFrame API within your notebook. Given the following code snippet to start with:

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

5. You are a data scientist working with a large dataset of customer transactions stored in Snowflake. You need to identify potential fraud using statistical summaries. Which of the following approaches would be MOST effective in identifying unusual spending patterns, considering the need for scalability and performance within Snowflake?

A) Implement a custom UDF (User-Defined Function) in Java to calculate the interquartile range (IQR) for each customer's transaction amounts and flag transactions as outliers if they are below QI - 1.5 IQR or above Q3 + 1.5 IQR.
B) Calculate the average transaction amount and standard deviation for each customer using window functions in SQL. Flag transactions that fall outside of 3 standard deviations from the customer's mean.
C) Sample a subset of the data, calculate descriptive statistics using Snowpark Python and the 'describe()' function, and extrapolate these statistics to the entire dataset.
D) Use Snowflake's native anomaly detection functions (if available, and configured for streaming) to detect anomalies based on transaction amount and frequency, grouped by customer ID.
E) Export the entire dataset to a Python environment, use Pandas to calculate the average transaction amount and standard deviation for each customer, and then identify outliers based on a fixed threshold.

Solutions: