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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

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

2. You are building a machine learning model to predict loan defaults. You have a dataset in Snowflake with the following features: 'income' (annual income in USD), 'loan_amount' (loan amount in USD), and 'credit_score' (FICO score). You need to normalize these features before training your model. The data has outliers in both 'income' and 'loan_amount', and 'credit_score' has a roughly normal distribution but you still want to standardize it to have a mean of 0 and standard deviation of 1. You want to perform these normalizations using only SQL in Snowflake (no UDFs). Which of the following SQL transformations are most suitable?

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

3. 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 B
B) Option D
C) Option C
D) Option A
E) Option E

4. You are developing a Python stored procedure in Snowflake to predict sales for a retail company. You want to incorporate external data (e.g., weather forecasts) into your model. Which of the following methods are valid and efficient ways to access and use external data within your Snowflake Python stored procedure?

A) Load the external data into a Snowflake table and then query the table from within the Python stored procedure using the Snowflake Connector for Pythom
B) Use a Snowflake external function to pre-process the external data and then pass the processed data as input parameters to the Python stored procedure.
C) Embed the external data directly into the Python stored procedure's code as a dictionary or JSON object.
D) Directly call external APIs within the Python stored procedure using libraries like 'requests'. Snowflake's network policy must be configured to allow outbound connections.
E) Use a Snowflake Pipe to continuously ingest external data from a cloud storage location and access the data within the stored procedure.

5. You are developing a real-time fraud detection system using Snowflake and an external function. The system involves scoring incoming transactions against a pre-trained TensorFlow model hosted on Google Cloud A1 Platform Prediction. The transaction data resides in a Snowflake stream. The goal is to minimize latency and cost. Which of the following strategies are most effective to optimize the interaction between Snowflake and the Google Cloud A1 Platform Prediction service via an external function, considering both performance and cost?

A) Batch multiple transactions from the Snowflake stream into a single request to the external function. The external function then sends the batched transactions to the Google Cloud A1 Platform Prediction service in a single request. This increases throughput but might introduce latency.
B) Implement asynchronous invocation of the external function from Snowflake using Snowflake's task functionality. This allows Snowflake to continue processing transactions without waiting for the response from the Google Cloud A1 Platform Prediction service, but requires careful monitoring and handling of asynchronous results.
C) Invoke the external function for each individual transaction in the Snowflake stream, sending the transaction data as a single request to the Google Cloud A1 Platform Prediction service.
D) Implement a caching mechanism within the external function (e.g., using Redis on Google Cloud) to store frequently accessed model predictions, thereby reducing the number of calls to the Google Cloud A1 Platform Prediction service. This requires managing cache invalidation.
E) Use a Snowflake pipe to automatically ingest the data from the stream, and then trigger a scheduled task that periodically invokes a stored procedure to train the model externally.

Solutions: