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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are tasked with feature engineering a dataset containing customer transaction data stored in a Snowflake table named 'CUSTOMER TRANSACTIONS'. This table includes columns like 'CUSTOMER ID', 'TRANSACTION DATE, and 'TRANSACTION AMOUNT. You need to create a new feature representing the 'Recency' of the customer, which is the number of days since their last transaction. Using Snowpark Pandas, which of the following code snippets will correctly calculate the Recency feature as a new column in a Snowpark DataFrame?

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

2. You are building a data science pipeline in Snowflake to predict customer churn. The pipeline involves extracting data, transforming it using Dynamic Tables, training a model using Snowpark ML, and deploying the model for inference. The raw data arrives in a Snowflake stage daily as Parquet files. You want to optimize the pipeline for cost and performance. Which of the following strategies are MOST effective, considering resource utilization and potential data staleness?

A) Implement a series of smaller Dynamic Tables, each responsible for a specific transformation step, with well-defined refresh intervals tailored to the data's volatility and the downstream model's requirements.
B) Load all data into traditional Snowflake tables and use scheduled tasks with stored procedures written in Python to perform the transformations and model training.
C) Use a single, large Dynamic Table to perform all transformations in one step, relying on Snowflake's optimization to handle dependencies and incremental updates.
D) Use a combination of Dynamic Tables for feature engineering and Snowpark ML for model training and deployment, ensuring proper dependency management and refresh intervals for each Dynamic Table based on data freshness requirements.
E) Schedule all data transformations and model training as a single large Snowpark Python script executed by a Snowflake task, ignoring data freshness requirements.

3. You have a dataset in Snowflake containing customer reviews. One of the columns, 'review_text', contains free-text customer feedback. You want to perform sentiment analysis on these reviews and include the sentiment score as a feature in your machine learning model. Furthermore, you wish to categorize the sentiment into 'Positive', 'Negative', and 'Neutral'. Given the need for scalability and efficiency within Snowflake, which methods could be employed?

A) Create a series of Snowflake SQL queries utilizing complex string matching and keyword analysis to determine sentiment based on predefined lexicons. Categories are assigned through CASE statements.
B) Use a Snowflake procedure that reads all 'review_text' data, transfers data outside of Snowflake to an external server running sentiment analysis software, and then writes results back into a new table.
C) Use a Python UDF (User-Defined Function) with a pre-trained sentiment analysis library (e.g., NLTK or spaCy) to calculate the sentiment score and categorize it. Deploy the UDF in Snowflake and apply it to the 'review_text' column.
D) Utilize Snowflake's external functions to call a pre-existing sentiment analysis API (e.g., Google Cloud Natural Language API or AWS Comprehend) passing the review text and storing the returned sentiment score and category. Ensure proper API key management and network configuration.
E) Create a Snowpark Python DataFrame from the Snowflake table, use a sentiment analysis library within the Snowpark environment, categorize the sentiments, and then save the resulting DataFrame back to Snowflake as a new table.

4. You are developing a regression model in Snowflake to predict housing prices. You've trained a model using Snowflake ML functions and now need to rigorously validate its performance. You have a separate validation dataset stored in a table named 'HOUSING VALIDATION'. Which of the following SQL statements, when executed in Snowflake, would accurately calculate the Root Mean Squared Error (RMSE) of your model's predictions against the actual prices in the validation dataset, assuming your model is named 'HOUSING PRICE MODEL' and the prediction function generated by CREATE SNOWFLAKE.ML.FORECAST is called PREDICT?

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

5. 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) Utilize Snowpark's vectorized UDFs and DataFrame operations to leverage Snowflake's distributed computing capabilities.
B) 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.
C) 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.
D) Force single-threaded execution by setting to avoid overhead associated with parallel processing.
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.

Solutions: