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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A data scientist is using Snowflake to perform anomaly detection on sensor data from industrial equipment. The data includes timestamp, sensor ID, and sensor readings. Which of the following approaches, leveraging unsupervised learning and Snowflake features, would be the MOST efficient and scalable for detecting anomalies, assuming anomalies are rare events?

A) Calculate the moving average of sensor readings over a fixed time window using Snowflake SQL and flag data points that deviate significantly from the moving average as anomalies. No ML model needed.
B) Apply Autoencoders to the sensor data using a Snowflake external function. Data points are considered anomalous if the reconstruction error from the autoencoder exceeds a certain threshold.
C) Implement an Isolation Forest model. Train the Isolation Forest model on a representative sample of the sensor data and create UDF to score each row in snowflake.
D) Use K-Means clustering to group sensor readings into clusters and identify data points that are far from the cluster centroids as anomalies. No model training necessary.
E) Use a Support Vector Machine (SVM) with a radial basis function (RBF) kernel trained on the entire dataset to classify data points as normal or anomalous. Implement the SVM model as a Snowflake UDF.

2. You are training a Gradient Boosting model within Snowflake using Snowpark Python to predict customer churn. You are using the Hyperopt library for hyperparameter tuning. You want to use the function to find the best hyperparameters. You have defined your objective function, , and the search space, Which of the following is the MOST efficient and correct way to call the function within a Snowpark Python UDF to ensure the Hyperopt trials data is effectively managed and accessible for further analysis within Snowflake?

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

3. A data scientist is analyzing website conversion rates for an e-commerce platform. They want to estimate the true conversion rate with 95% confidence. They have collected data on 10,000 website visitors, and found that 500 of them made a purchase. Given this information, and assuming a normal approximation for the binomial distribution (appropriate due to the large sample size), which of the following Python code snippets using scipy correctly calculates the 95% confidence interval for the conversion rate? (Assume standard imports like 'import scipy.stats as St' and 'import numpy as np').

A)

B)

C)

D)

E)

4. You're working on a fraud detection system for an e-commerce platform. You have a table 'TRANSACTIONS with a 'TRANSACTION AMOUNT column. You want to bin the transaction amounts into several risk categories ('Low', 'Medium', 'High', 'Very High') using explicit boundaries. You want the bins to be inclusive of the lower boundary and exclusive of the upper boundary (e.g., [0, 100), [100, 500), etc.). Which of the following SQL statements using the 'WIDTH BUCKET function correctly bins the transaction amounts into these categories, assuming these boundaries: 0, 100, 500, 1000, and infinity, and assigns appropriate labels?

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

5. You are tasked with training a machine learning model within Snowflake using a Python UDTF. The UDTF is intended to process incoming sales data, calculate features, and update the model incrementally. The model is a simple linear regression using scikit-learn. Your initial attempt fails with a 'ModuleNotFoundError: No module named 'sklearn" error within the UDTF. You have already confirmed that scikit-learn is available in your Anaconda channel and specified it during session creation. Which of the following actions would MOST directly address this issue and allow the UDTF to successfully import and use scikit-learn?

A) Ensure that the Anaconda channel containing 'sklearn' is explicitly activated at the account level using the 'ALTER ACCOUNT command. Verify the channel is listed in 'SHOW CHANNELS'.
B) When creating the UDTF, use the 'PACKAGES' parameter to explicitly specify the 'skiearn' package. For example: 'CREATE OR REPLACE FUNCTION RETURNS TABLE LANGUAGE PYTHON RUNTIME_VERSION = '3.8' PACKAGES = ('snowflake-snowpark-python','scikit-learn') ...
C) Include ' import snowflake.snowpark; session = snowflake.snowpark.session.get_active_session()' within the UDTF code to explicitly initialize the Snowpark session before importing sklearn. Ensure that scikit-learn is included in the 'imports' argument of the 'create_dataframe' method.
D) Explicitly copy the 'sklearn' directory and its dependencies directly into the same directory as your UDTF definition script on the Snowflake stage, then reference them using relative paths within the UDTF.
E) Recreate the Anaconda environment and ensure that the 'sklearn' package is installed specifically within the environment's 'site-packages' directory. Then, recreate the Snowflake session.

Solutions: