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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are working with a dataset containing timestamps representing website user activity. The timestamps are stored as strings in the format 'YYYY-MM-DD HH:MI:SS.SSSSSS' in a Snowflake table named 'website_activity'. You need to extract the hour of the day from these timestamps and encode it as a cyclical feature using sine and cosine transformations. This is to capture the cyclical nature of user activity throughout the day (e.g., 23:00 and 00:00 are close in time). Which of the following Snowflake SQL code snippets correctly implements this cyclical encoding and creates the 'hour_sin' and 'hour_cos' columns?

A)

B)

C)

D)

E)

2. You are using a Snowflake Notebook to analyze customer churn for a telecommunications company. You have a dataset with millions of rows and want to perform feature engineering using a combination of SQL transformations and Python code. Your goal is to create a new feature called 'average_monthly call_duration' which calculates the average call duration for each customer over the last 3 months. You are using the Snowpark DataFrame API within your notebook. Given the following code snippet to start with:

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

3. A data scientist needs to calculate the cumulative moving average of sales for each product in a table. The table contains columns: (INT), (DATE), and (NUMBER). The desired output should include the product_id', 'sale_date', and Which of the following Snowflake SQL statements correctly calculates the cumulative moving average for each product using window functions?

A) SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date ASC) / OVER (PARTITION BY product_id ORDER BY sale_date ASC) AS cumulative_average FROM sales_by_day;
B) SELECT product_id, sale_date, daily_sales, AVG(daily_sales) OVER (ORDER BY sale_date ASC) AS cumulative_average FROM sales_by_day;
C) SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date ASC ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS cumulative_average FROM
D) SELECT product_id, sale_date, daily_sales, AVG(daily_sales) OVER (PARTITION BY product_id) AS cumulative_average FROM sales_by_day;
E) SELECT product_id, sale_date, daily_sales, OVER (PARTITION BY product_id ORDER BY sale_date AS cumulative_average FROM sales_by_day;

4. You are working with a dataset containing customer reviews for various products. The dataset includes a 'REVIEW TEXT column with the raw review text and a 'PRODUCT ID' column. You want to perform sentiment analysis on the reviews and create a new feature called 'SENTIMENT SCORE for each product. You plan to use a UDF to perform the sentiment analysis. Which of the following steps and SQL code snippets are essential for implementing this feature engineering task in Snowflake, ensuring optimal performance and scalability? Select all that apply:

A) Cache the results of the sentiment analysis UDF in a temporary table to avoid recomputing the scores for the same reviews in subsequent queries. Use 'CREATE TEMPORARY TABLE to create a temporary table.
B) Create a Python UDF that takes the 'REVIEW_TEXT as input and returns a sentiment score (e.g., between -1 and 1). Then, use 'CREATE OR REPLACE FUNCTION' statement to register the UDF.
C) Apply the sentiment analysis UDF to the 'REVIEW TEXT column within a 'SELECT statement, grouping by 'PRODUCT ID and calculating the average 'SENTIMENT_SCORE' using
D) Ensure the UDF is vectorized to process batches of reviews at once, improving performance. This can be achieved using decorator on top of the python function.
E) Use the 'SNOWFLAKE.ML' package to train a sentiment analysis model directly within Snowflake, eliminating the need for a separate UDF.

5. You are building a product recommendation system using Snowflake Cortex. You have a table 'PRODUCT DESCRIPTIONS' containing product IDs and textual descriptions. You want to generate vector embeddings for these descriptions to perform similarity searches. However, you need to control the cost and latency of the embedding generation process. Which of the following strategies and considerations are MOST important for optimizing performance and cost when generating vector embeddings in Snowflake Cortex using a UDF?

A) Use a larger Snowflake warehouse size. Increasing the warehouse size always linearly reduces embedding generation time and cost.
B) Optimize the batch size passed to the embedding UDF. Experiment with different batch sizes to find the optimal trade-off between throughput and latency. Too large batches might cause memory issues, while too small batches increase overhead. Consider using a batch size of 64 or 128 as a starting point, adjusting based on your dataset and resource constraints.
C) Partition the 'PRODUCT DESCRIPTIONS' table by product category and generate embeddings for each partition separately. This helps to distribute the workload and reduce the size of the data processed by each UOF call. This makes more sense and is faster to re-create the table.
D) Use the smallest available Cortex embedding model. Smaller models are always faster and cheaper, regardless of the dataset size.
E) Cache the results of the embedding LJDF. Implement a caching mechanism (e.g., using a Snowflake table) to store the embeddings for frequently accessed product descriptions, avoiding redundant embedding calculations. use a materialized view.

Solutions: