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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are tasked with deploying a pre-trained sentiment analysis model hosted externally using AWS SageMaker. The model endpoint requires an API key for authentication, and you want to score customer reviews stored in a Snowflake table named 'CUSTOMER REVIEWS. Which of the following steps are necessary to securely and efficiently integrate this external model with Snowflake, assuming you have already created a Snowflake stage to store secrets?

A) Create an external function in Snowflake that invokes the SageMaker endpoint, hardcoding the API key directly into the function definition for simplicity.
B) Create an external function in Snowflake that retrieves the API key from a secure Snowflake secret object. Grant USAGE privilege on the secret to the service account associated with the external function.
C) Use Snowflake's external functions to directly call the SageMaker endpoint from a SQL query, passing the customer review text as input. No separate secure external stage configuration is needed as long as Snowflake has internet access.
D) Create a secret object in Snowflake to store the API key. Grant appropriate privileges on the secret to the role that will execute the external function. Modify external function that references secure external stage.
E) Store the API key in an environment variable within the AWS Lambda function (if using API Gateway) that serves as an intermediary between Snowflake and SageMaker. Snowflake calls the API Gateway endpoint which relays the request to the SageMaker endpoint, and no specific configuration is needed on snowflake.

2. You are using Snowflake Cortex to analyze customer reviews. You have created a vector embedding for each review using a UDF that calls a remote LLM inference endpoint. Now you need to perform a similarity search to identify reviews that are similar to a given query review. Which of the following SQL queries leveraging vector functions in Snowflake is the MOST efficient and appropriate way to achieve this, assuming the 'REVIEW EMBEDDINGS' table has columns 'review_id' and 'embedding' (a VECTOR column) and query_embedding' is a pre-computed vector embedding?

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

3. You are analyzing customer transaction data in Snowflake to identify fraudulent activities. The 'TRANSACTION AMOUNT' column exhibits a right-skewed distribution. Which of the following Snowflake queries is MOST effective in identifying outliers based on the Interquartile Range (IQR) method, specifically targeting unusually large transaction amounts? Assume IQR is already calculated as variable and QI as and Q3 as in snowflake session.

A) SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION_AMOUNT > (SELECT + 3 FROM TRANSACTIONS);
B) SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION_AMOUNT > (SELECT WITHIN GROUP (ORDER BY TRANSACTION_AMOUNT) FROM TRANSACTIONS);
C) SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION_AMOUNT < qi - (1.5 iqr);
D) SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION AMOUNT > q3 + (1.5 iqr);
E) SELECT TRANSACTION ID FROM TRANSACTIONS WHERE TRANSACTION_AMOUNT > (SELECT MEDIAN(TRANSACTION AMOUNT) FROM TRANSACTIONS);

4. You are building a machine learning model using Snowpark Python to predict house prices. The dataset contains a feature column named 'location' which contains free-form text descriptions of house locations. You want to leverage a pre-trained Large Language Model (LLM) hosted externally to extract structured location features like city, state, and zip code from the free-form text within Snowpark. You want to minimize the data transferred out of Snowflake. Which approach is most efficient and secure?

A) Use to load the 'location' column data into a Pandas DataFrame, call the external LLM API in your Python script to enrich the location data and then use to store the enriched data back into a Snowflake table.
B) Use Snowpark's 'createOrReplaceStage' to create an external stage pointing to the LLM API endpoint. Load the 'location' data into this stage and call the LLM API directly from the Snowflake stage using SQL.
C) Create a Snowpark User-Defined Function (UDF) that calls the external LLM API. Pass the 'location' column data to the UDF and retrieve the structured location features. Then apply the UDF directly on the Snowpark DataFrame.
D) Use the Snowflake Connector for Python to directly query the 'location' column and call the external LLM API from the connector. Then write the updated data into a new table.
E) Create a Snowflake External Function that calls the external LLM API. Pass the 'location' column data to the External Function and retrieve the structured location features. Then apply the External Function directly on the Snowpark DataFrame.

5. You are analyzing sales data in Snowflake using Snowpark to identify seasonality. You have a table named 'SALES DATA with columns 'SALE DATE (TIMESTAMP NTZ) and 'AMOUNT (NUMBER). You want to calculate the rolling average sales for each week over a period of 12 weeks using a Snowpark DataFrame. Which of the following Snowpark code snippets correctly implements this calculation?

A)

B)

C)

D)

E)

Solutions: