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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are tasked with building a predictive model in Snowflake to identify high-value customers based on their transaction history. The 'CUSTOMER_TRANSACTIONS table contains a 'TRANSACTION_AMOUNT column. You need to binarize this column, categorizing transactions as 'High Value' if the amount is above a dynamically calculated threshold (the 90th percentile of transaction amounts) and 'Low Value' otherwise. Which of the following Snowflake SQL queries correctly achieves this binarization, leveraging window functions for threshold calculation and resulting in a 'CUSTOMER SEGMENT column?

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

2. You are working with a large dataset of transaction data in Snowflake to identify fraudulent transactions. The dataset contains millions of rows and includes features like transaction amount, location, time, and user ID. You want to use Snowpark and SQL to identify potential outliers in the 'transaction amount' feature. Given the potential for skewed data and varying transaction volumes across different locations, which of the following data profiling and feature engineering techniques would be the MOST effective at identifying outlier transaction amounts while considering the data distribution and location-specific variations?

A) Use Snowflake's APPROX_PERCENTILE function with Snowpark to calculate percentiles of the 'transaction amount' feature. Transactions with amounts in the top and bottom 1% are flagged as outliers.
B) Apply a clustering algorithm (e.g., DBSCAN) using Snowpark ML to the transaction data, using transaction amount, location and time as features. Treat data points in small, sparse clusters as outliers. This approach does not need to be performed for each location, just the entire dataset.
C) Partition the data by location using Snowpark. For each location, calculate the median and median absolute deviation (MAD) of the 'transaction amount' feature. Identify outliers as transactions with amounts that fall outside of the median +/- 3 MAD for that location.
D) Use Snowpark to calculate the interquartile range (IQR) of the 'transaction amount' feature for the entire dataset. Identify outliers as transactions with amounts that fall below QI - 1.5 IQR or above Q3 + 1.5 IQR.
E) Calculate the mean and standard deviation of the 'transaction amount' feature for the entire dataset using SQL. Identify outliers as transactions with amounts that fall outside of 3 standard deviations from the mean.

3. You are analyzing website clickstream data stored in Snowflake to identify user behavior patterns. The data includes user ID, timestamp, URL visited, and session ID. Which of the following unsupervised learning techniques, combined with appropriate data transformations in Snowflake SQL, would be most effective in discovering common navigation paths followed by users? (Choose two)

A) DBSCAN clustering on the raw URL data, treating each URL as a separate dimension. This will identify URLs that are frequently visited by many users.
B) K-Means clustering on features extracted from the URL data, such as the frequency of visiting specific domains or the number of pages visited per session. This requires feature engineering using SQL.
C) Principal Component Analysis (PCA) to reduce the dimensionality of the URL data, followed by hierarchical clustering. This will group similar URLs together.
D) Sequence clustering using time-series analysis techniques (e.g., Hidden Markov Models), after transforming the data into a sequence of URLs for each session using Snowflake's LISTAGG function ordered by timestamp.
E) Association rule mining (e.g., Apriori) applied directly to the raw URL data to find frequent itemsets of URLs visited together within the same session. No SQL transformations are required.

4. A data scientist is performing exploratory data analysis on a table named 'CUSTOMER TRANSACTIONS. They need to calculate the standard deviation of transaction amounts C TRANSACTION AMOUNT) for different customer segments CCUSTOMER SEGMENT). The 'CUSTOMER SEGMENT column can contain NULL values. Which of the following SQL statements will correctly compute the standard deviation, excluding NULL transaction amounts, and handling NULL customer segments by treating them as a separate segment called 'Unknown'? Consider using Snowflake-specific functions where appropriate.

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

5. You have deployed a fraud detection model in Snowflake using Snowpark and are monitoring its performance. You observe a significant drift in the transaction data distribution compared to the data used during training. To address this, you want to implement a retraining strategy. Which of the following steps are MOST critical to automate the retraining process using Snowflake's features?

A) Replace the existing model artifact in Snowflake's stage with the newly trained model using Snowpark's model registry functionality.
B) Create a Snowflake Stream on the transaction data table to capture changes since the last training run.
C) Configure Snowflake's data lineage features to automatically track the input data and model lineage for reproducibility.
D) Develop a Python UDF that periodically calculates drift metrics (e.g., Population Stability Index) and triggers retraining when a threshold is exceeded. Use Snowflake's Task feature to schedule the UDF execution.
E) Build and deploy a new docker image for each retraining, containing the new model, and update the external function definition to point to the new image.

Solutions: