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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are using Snowpark Pandas to prepare data for a machine learning model. You have a Snowpark DataFrame named 'transactions df that contains transaction data, including 'transaction id', 'product id', 'customer id', and 'transaction_amount'. You want to create a new feature that represents the average transaction amount per customer. However, you are concerned about potential skewness in the 'transaction_amount' and want to apply a log transformation to reduce its impact before calculating the average. Which of the following steps using Snowpark Pandas would achieve this transformation and calculation most efficiently within Snowflake?

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

2. You are a data scientist working for an e-commerce company. You have a table named 'sales_data' with columns 'product_id' , customer_id' , 'transaction_date' , and 'sale_amount'. You need to identify the top 5 products by total sale amount for each month. Which of the following Snowflake SQL queries is the MOST efficient and correct way to achieve this, while also handling potential ties in sale amounts?

A)

B)

C)

D)

E)

3. You are tasked with deploying a time series forecasting model within Snowflake using Snowpark Python. The model requires significant pre-processing and feature engineering steps that are computationally intensive. These steps include calculating rolling statistics, handling missing values with imputation, and applying various transformations. You aim to optimize the execution time of these pre- processing steps within the Snowpark environment. Which of the following techniques can significantly improve the performance of your data preparation pipeline?

A) Write the feature engineering logic directly in SQL and create a view. Use the Snowpark DataFrame API to query the view, avoiding Python code execution within Snowpark.
B) Force single-threaded execution by setting to avoid overhead associated with parallel processing.
C) Utilize Snowpark's vectorized UDFs and DataFrame operations to leverage Snowflake's distributed computing capabilities.
D) Convert the Snowpark DataFrame to a Pandas DataFrame using and perform all pre-processing operations using Pandas functions before loading the processed data back to Snowflake.
E) Ensure that all data used is small enough to fit within the memory of the client machine running the Snowpark Python script, thus removing the need for distributed computing.

4. A data scientist is preparing customer churn data for a machine learning model in Snowflake. The dataset contains a 'Contract_Type' column with values 'Month-to-Month', 'One Year', and 'Two Year'. They want to use label encoding to transform this categorical feature into numerical values. Which of the following SQL statements correctly implements label encoding for the 'Contract_Type' column, assigning 'Month-to-Month' to 0, 'One Year' to 1, and 'Two Year' to 2, and creates a new column named 'Contract_Type_Encoded'? Additionally, the data scientist wants to handle potential NULL values in 'Contract_Type' by assigning them the value of -1.

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

5. You have a Snowpark DataFrame named 'product_reviews' containing customer reviews for different products. The DataFrame includes columns like 'product_id' , 'review_text' , and 'rating'. You want to perform sentiment analysis on the 'review_text' to identify the overall sentiment towards each product. You decide to use Snowpark for Python to create a user-defined function (UDF) that utilizes a pre-trained sentiment analysis model hosted externally. You need to ensure secure access to this model and efficient execution. Which of the following represents the BEST approach, considering security and performance?

A) Create an external function in Snowflake that calls a serverless function. Configure the API gateway in front of the serverless function to enforce authentication via Mutual TLS (mTLS) using Snowflake-managed certificates.
B) Create a Snowpark Pandas UDF that calls the external sentiment analysis API. Use Snowflake secrets management to store the API key and retrieve it within the UDF.
C) Create an external function in Snowflake that calls a serverless function (e.g., AWS Lambda, Azure Function) that performs the sentiment analysis. Use Snowflake's network policies to restrict access to the serverless function and secrets management to handle API keys.
D) Create a Java UDF that utilizes a library to call the sentiment analysis API. Pass the API key as a parameter to the UDF each time it is called.
E) Create an inline Python UDF that directly calls the external sentiment analysis API with hardcoded API keys within the UDF code.

Solutions: