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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A marketing team at 'RetailSphere' wants to segment their customer base using unstructured textual data (customer reviews) stored in a Snowflake VARIANT column named 'REVIEW TEXT within the table 'CUSTOMER REVIEWS'. They aim to identify distinct customer segments based on sentiment and topics discussed in their reviews. They want to use a Supervised Learning approach for this task. Which of the following strategies best describes the appropriate approach within Snowflake, considering performance and scalability? Assume you have pre-trained sentiment and topic models deployed as Snowflake external functions.

A) Create a Snowflake external function to call a pre-trained sentiment analysis and topic modeling model hosted on Azure ML. Apply these functions to the REVIEW_TEXT column to generate sentiment scores and topic probabilities. Subsequently, use these features as input to an unsupervised clustering algorithm (e.g., DBSCAN) within Snowflake, relying solely on data density to define segments.
B) Extract the column, apply sentiment analysis and topic modeling using Python within a Snowflake UDF, and then perform K-Means clustering directly on the resulting features within Snowflake. Define the labels after clustering based on the majority class of the topics and sentiments in each cluster.
C) Extract the ' REVIEW_TEXT column, manually categorize a small subset of reviews into predefined segments. Train a text classification model (e.g., using scikit-learn) externally, deploy it as a Snowflake external function, and then apply this function to the entire 'REVIEW TEXT column to predict segment assignments. Manually adjust cluster centroids to represent the manually labeled dataset.
D) Extract the 'REVIEW TEXT column, apply sentiment analysis and topic modeling using Java within a Snowflake UDF, and then perform hierarchical clustering directly on the resulting features within Snowflake. Manually label the clusters after visual inspection.
E) Create a Snowflake external function to call a pre-trained sentiment analysis and topic modeling model hosted on AWS SageMaker. Apply these functions to the ' REVIEW_TEXT column to generate sentiment scores and topic probabilities. Subsequently, use these features as input to a supervised classification model (e.g., XGBoost) also deployed as a Snowflake external function, training on a manually labeled subset of reviews.

2. You are tasked with developing a multi-class image classification model to categorize product images stored in Snowflake external stage. The categories are 'Electronics', 'Clothing', 'Furniture', 'Books', and 'Food'. You plan to use a pre-trained Convolutional Neural Network (CNN) model and fine-tune it using your dataset. However, you're facing challenges in efficiently loading and preprocessing the image data within the Snowflake environment before feeding it to your model. Which of the following approaches would be MOST efficient for image data loading and preprocessing in Snowflake, minimizing data movement and leveraging Snowflake's scalability, for a large dataset exceeding 1 TB of images?

A) Use Snowflake's Snowpark to read images from the external stage into a Snowpark DataFrame. Then, implement image preprocessing using Snowpark DataFrame operations, such as resizing and normalization, within the DataFrame transformations before sending the data to the model.
B) Utilize Snowflake's external function integration with AWS Lambda to preprocess images as they are uploaded to S3, storing the preprocessed data back in S3 and creating an external table pointing to the preprocessed data.
C) Download all the images from the external stage to a local machine, preprocess them using a standard Python library like OpenCV, and then upload the processed data back into Snowflake as a table for model training.
D) Create a Snowflake Stream to continuously ingest new images into a Snowflake table. Use a task to periodically trigger a Python UDF that preprocesses the newly ingested images and stores them in another table for model training.
E) Write a Python User-Defined Function (UDF) that loads each image from the external stage directly into memory, performs preprocessing (resizing, normalization), and returns the processed image data. The UDF is then called in a SQL query to process the image data.

3. You've trained a machine learning model using Scikit-learn and saved it as 'model.joblib'. You need to deploy this model to Snowflake. Which sequence of commands will correctly stage the model and create a Snowflake external function to use it for inference, assuming you already have a Snowflake stage named 'model_stage'?

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

4. You are developing a data transformation pipeline in Python that reads data from Snowflake, performs complex operations using Pandas DataFrames, and writes the transformed data back to Snowflake. You've implemented a function, 'transform data(df)', which processes a Pandas DataFrame. You want to leverage Snowflake's compute resources for the DataFrame operations as much as possible, even for intermediate transformations before loading the final result. Which of the following strategies could you employ to optimize this process, assuming you have a configured Snowflake connection "conn"?

A) Chunk the Snowflake table into smaller DataFrames using 'fetchmany()' , apply to each chunk, and then append each transformed chunk to a Snowflake table using multiple INSERT statements. Call columns=[col[0] for col in cur.description]))'
B) Use Snowpark Python DataFrame API to perform the transformation directly on Snowflake's compute and then load results into the same table. Call 'df_snowpark = session.create_dataframe(df)'.
C) Create a series of Snowflake UDFs that perform the individual transformations within Snowflake, load the data into Pandas DataFrames, apply UDFs on these DataFrames, and use to upload to Snowflake.
D) Use 'snowflake.connector.pandas_tools.write_pandas(conn, df, table_name, auto_create_table=Truey to write the transformed DataFrame to Snowflake and let Snowflake handle the transformations using SQL.
E) Read the entire Snowflake table into a single Pandas DataFrame, apply , and then write the entire transformed DataFrame back to Snowflake.

5. You are tasked with building a data pipeline using Snowpark Python to process customer feedback data stored in a Snowflake table called FEEDBACK DATA'. This table contains free-text feedback, and you need to clean and prepare this data for sentiment analysis. Specifically, you need to remove stop words, perform stemming, and handle missing values. Which of the following code snippets and strategies, potentially used in conjunction, provide the most effective and performant solution for this task within the Snowpark environment?

A) Use a Python UDF that utilizes the NLTK library to remove stop words and perform stemming on the feedback text. Handle missing values by replacing them with an empty string using the .fillna(")' method on the Snowpark DataFrame after applying the UDF.
B) Leverage Snowflake's built-in string functions within SQL to remove common stop words based on a predefined list. Use a Snowpark DataFrame to execute this SQL transformation. For stemming, research and deploy a Java UDF implementing stemming algorithms, then chain it within a Snowpark transformation pipeline. Replace missing values with the string 'N/A' during the DataFrame construction using 'na.fill('N/A')'.
C) Utilize Snowpark's 'call_function' with a Java UDF pre-loaded into Snowflake, which removes stop words and performs stemming with libraries like Lucene. Missing values can be handled with SQL's 'NVL' function during the initial data extraction into a Snowpark DataFrame.

D) Implement all data cleaning tasks within a single SQL stored procedure including removing stop words using REPLACE functions, stemming using a custom lookup table, and handling NULL values using COALESC Call this stored procedure from Snowpark for Python.

E) Load the FEEDBACK DATA' table into a Pandas DataFrame using perform stop word removal and stemming using libraries like spacy or NLTK, handle missing values using Pandas' 'fillna()' method. Then, convert the cleaned Pandas DataFrame back into a Snowpark DataFrame. Use vectorization of text column in dataframe after above step

Solutions: