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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are tasked with forecasting the daily sales of a specific product for the next 30 days using Snowflake. You have historical sales data for the past 3 years, stored in a Snowflake table named 'SALES DATA', with columns 'SALE DATE (DATE type) and 'SALES AMOUNT' (NUMBER type). You want to use the Prophet library within a Snowflake User-Defined Function (UDF) for forecasting. The Prophet model requires the input data to have columns named 'ds' (for dates) and 'y' (for values). Which of the following code snippets demonstrates the CORRECT way to prepare and pass your data to the Prophet UDF in Snowflake, assuming you've already created the Python UDF 'prophet_forecast'?

A)

B)

C)

D)

E)

2. You are building a data science pipeline in Snowflake to perform time series forecasting. You've decided to use a Python UDTF to encapsulate the forecasting logic using a library like 'Prophet'. The UDTF needs to access historical data to train the model and generate forecasts. The data is stored in a Snowflake table named 'SALES DATA with columns 'DATE' and 'SALES'. Which of the following approaches is/are most efficient and secure for accessing the 'SALES DATA table from within the UDTF during model training?

A) Pass the entire 'SALES DATA' table as a Pandas DataFrame to the UDTF as an argument. This approach is suitable for smaller datasets. Do not partition the data frame.
B) Use the 'snowflake.connector' to connect to Snowflake using a dedicated service account with read-only access to the 'SALES DATA' table. Store the service account credentials securely in Snowflake secrets and retrieve them within the UDTF.
C) Create a view on top of 'SALES DATA' and grant access to the UDTF's owner role to the view. Then, query the view using Snowpark within the UDTF.
D) Bypass Snowflake entirely and load data from S3 stage into a Pandas dataframe.
E) Use the Snowpark API within the UDTF to query the 'SALES DATA' table directly, leveraging the existing Snowflake session context. This requires no additional credentials management.

3. You are working with a large sales transaction dataset in Snowflake, stored in a table named 'SALES DATA'. This table contains columns such as 'TRANSACTION_ID (unique identifier), 'CUSTOMER_ID', 'PRODUCT_ID, 'TRANSACTION_DATE' , and 'AMOUNT'. Due to a system error, some transactions were duplicated in the table. Your goal is to remove these duplicates efficiently using Snowpark for Python. You want to use the 'window.partitionBy()' and functions. Which of the following code snippets correctly removes duplicates based on all columns, while also creating a new column 'ROW NUM' to indicate the row number within each partition?

A)

B)

C)

D)

E)

4. A data scientist is analyzing sales data in Snowflake to identify seasonal trends. The 'SALES TABLE' contains columns 'SALE DATE' (DATE) and 'SALE _ AMOUNT' (NUMBER). They want to calculate the average daily sales amount for each month and year in the dataset. Which of the following SQL queries will correctly achieve this, while also handling potential NULL values in 'SALE AMOUNT?

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

5. You've developed a fraud detection model using Snowflake ML and want to estimate the expected payout (loss or gain) based on the model's predictions. The cost of investigating a potentially fraudulent transaction is $50. If a fraudulent transaction goes undetected, the average loss is $1000. The model's confusion matrix on a validation dataset is: Predicted Fraud Predicted Not Fraud Actual Fraud 150 50 Actual Not Fraud 20 780 Which of the following SQL queries in Snowflake, assuming you have a table 'FRAUD PREDICTIONS' with columns 'TRANSACTION ID', 'ACTUAL FRAUD', and 'PREDICTED FRAUD' (1 for Fraud, O for Not Fraud), provides the most accurate estimate of the expected payout for every 1000 transactions?

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

Solutions: