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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are tasked with preparing a Snowflake table named 'PRODUCT REVIEWS' for sentiment analysis. This table contains columns like 'REVIEW ID, 'PRODUCT ID', 'REVIEW TEXT', 'RATING', and 'TIMESTAMP'. Your goal is to remove irrelevant fields to optimize model training. Which of the following options represent valid and effective strategies, using Snowpark SQL, for identifying and removing irrelevant or problematic fields from the 'PRODUCT REVIEWS' table, considering both storage efficiency and model accuracy? Assume that the model only need review text and review id and the rating.

A) All of the above.
B) Dropping rows with 'NULL' values in REVIEW_TEXT and then dropping the 'PRODUCT_ID' and 'TIMESTAMP' columns using 'ALTER TABLE. SQL: 'CREATE OR REPLACE TABLE PRODUCT REVIEWS AS SELECT FROM PRODUCT REVIEWS WHERE REVIEW TEXT IS NOT NULL; ALTER TABLE PRODUCT REVIEWS DROP COLUMN PRODUCT ID; ALTER TABLE PRODUCT REVIEWS DROP COLUMN TIMESTAMP;'
C) Creating a VIEW that only selects the 'REVIEW _ TEXT , 'REVIEW_ID', and 'RATING' columns, effectively hiding the irrelevant columns from the model. SQL: 'CREATE OR REPLACE VIEW REVIEWS FOR ANALYSIS AS SELECT REVIEW TEXT, REVIEW ID, RATING FROM PRODUCT REVIEWS;'
D) creating a new table 'REVIEWS_CLEANED containing only the relevant columns CREVIEW_TEXT , 'REVIEW_ID' , and 'RATING') using 'CREATE TABLE AS SELECT. SQL: 'CREATE OR REPLACE TABLE REVIEWS CLEANED AS SELECT REVIEW TEXT, REVIEW ID, RATING FROM PRODUCT REVIEWS;'
E) Using 'ALTER TABLE DROP COLUMN' to directly remove 'TIMESTAMP column, which is deemed irrelevant for the sentiment analysis model. SQL: 'ALTER TABLE PRODUCT REVIEWS DROP COLUMN TIMESTAMP;'

2. You've trained a sentiment analysis model in Snowflake using Snowpark Python and deployed it as a UDF. After several weeks, you notice the model's performance has degraded significantly. You suspect concept drift. Which of the following actions represent the MOST effective and comprehensive approach to address this situation, considering the entire Machine Learning Lifecycle, including monitoring, retraining, and model versioning? Assume you have monitoring in place that alerted you to the drift.

A) Adjust the existing model's parameters manually to compensate for the observed performance degradation without retraining or versioning.
B) Disable the model and revert to a rule-based system, abandoning the machine learning approach altogether.
C) Retrain the model on a sample of the most recent data, overwriting the original model files in your Snowflake stage and updating the UDF definition. Keep no record of the old model.
D) Analyze the recent data to understand the nature of the concept drift, retrain the model with a combination of historical and recent data, version the new model, and perform AIB testing against the existing model before fully deploying the new version. Log both model version predictions during AIB testing.
E) Immediately replace the current UDF with a newly trained model using the latest data, ignoring model versioning and assuming the latest data will solve the drift issue.

3. You are building a customer churn prediction model in Snowflake using Snowflake ML. After training, you need to evaluate the model's performance and identify areas for improvement. Given the following table 'PREDICTIONS' contains predicted probabilities and actual churn labels, which SQL query effectively calculates both precision and recall for the churn class (where 'CHURN = 1')?

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

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

5. You're building a customer segmentation model and need to aggregate data from various tables. You have the following tables in Snowflake: 'customer demographics' (customer id, age, city, income) 'customer transactionS (transaction_id, customer id, transaction_date, amount) 'product_details' (product_id, category) 'transaction_products' (transaction_id, product_id) Your goal is to create a single Snowpark DataFrame containing customer demographics along with the total amount spent by each customer on products within the 'Electronics' category in the last year. However, ensure that only customers with income greater than 50000 are considered and handle cases where customers have no transaction records, assigning a value of 0 to the 'total_electronics_spending' column for those customers. How can we achieve this using snowpark? Choose the correct options

A) Use a series of INNER JOINs to connect the tables and filter data, followed by grouping and aggregation. This approach guarantees accurate results with good performance.
B) Create a complex SQL query within Snowpark using 'session.sql()' to perform all the joins, filtering, and aggregation in a single step. This will be the most efficient approach.
C) Use a combination of LEFT JOINs and filtering. Start with 'customer_demographics' (filtered for income > 50000) as the base table and LEFT JOIN to subsequent tables. Use the 'coalesce' function to handle customers without transaction data.
D) Create a temporary view to store total electronics expenditure of each customer and left join with customer demographics table.
E) Create a Python UDF that performs the joins and aggregations. This offers flexibility and good performance when dealing with complex data transformations.

Solutions: