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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are using Snowpark for Python to perform feature engineering on a large dataset stored in a Snowflake table named 'transactions'. You need to create a new feature called 'transaction_size category' based on the 'transaction_amount' column. The categories are defined as follows: Small (amount < 10), Medium (10 <= amount < 100), and Large (amount 100). You want to optimize performance by leveraging Snowflake's parallel processing capabilities. Which of the following Snowpark for Python code snippets is the MOST efficient and Pythonic way to achieve this?

A)

B)

C)

D)

E)

2. You are tasked with preparing customer data for a churn prediction model in Snowflake. You have two tables: 'customers' (customer_id, name, signup_date, plan_id) and 'usage' (customer_id, usage_date, data_used_gb). You need to create a Snowpark DataFrame that calculates the total data usage for each customer in the last 30 days and joins it with customer information. However, the 'usage' table contains potentially erroneous entries with negative values, which should be treated as zero. Also, some customers might not have any usage data in the last 30 days, and these customers should be included in the final result with a total data usage of 0. Which of the following Snowpark Python code snippets will correctly achieve this?

A)

B)

C)

D)

E) None of the above

3. 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)

4. You've built a complex machine learning model using scikit-learn and deployed it as a Python UDF in Snowflake. The UDF takes a JSON string as input, containing several numerical features, and returns a predicted probability However, you observe significant performance issues, particularly when processing large batches of data'. Which of the following approaches would be MOST effective in optimizing the performance of this UDF in Snowflake?

A) Serialize the scikit-learn model using 'joblib' instead of 'pickle' for potentially faster deserialization within the UDF.
B) Use Snowflake's vectorized UDF feature to process data in micro-batches, minimizing the overhead of repeated Python interpreter initialization.
C) Pre-process the input data outside of the UDF using SQL transformations, reducing the amount of data passed to the UDF and simplifying the Python code.
D) Increase the warehouse size to improve the overall compute resources available for UDF execution.
E) Rewrite the UDF in Java or Scala to leverage the JVM's performance advantages over Python in Snowflake.

5. A data science team is developing a churn prediction model using Snowpark Python. They have a feature engineering pipeline defined as a series of User Defined Functions (UDFs) that transform raw customer data stored in a Snowflake table named 'CUSTOMER DATA'. Due to the volume of data (billions of rows), they need to optimize UDF execution for performance. Which of the following strategies, when applied individually or in combination, will MOST effectively improve the performance of these UDFs within Snowpark?

A) Repartitioning the DataFrame by a key that distributes data evenly across nodes before applying the UDFs, using the method and minimizing data shuffling.
B) Utilizing vectorized UDFs with NumPy data types wherever possible and carefully tuning batch sizes. Ensure that the input data is already sorted before passing to the UDF.
C) Leveraging external functions that call an API endpoint hosted on a cloud provider to perform data transformation. The API endpoint should utilize a serverless architecture.
D) Using temporary tables to store intermediate results calculated by the UDFs instead of directly writing to the target table.
E) Converting Python UDFs to Java UDFs, compiling the Java code, and deploying as a JAR file in Snowflake. Using a larger warehouse size is always the best first option.

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