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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are using Snowpark Python to process a large dataset of website user activity logs stored in a Snowflake table named 'WEB ACTIVITY'. The table contains columns such as 'USER ID', 'TIMESTAMP', 'PAGE URL', 'BROWSER', and 'IP ADDRESS'. You need to remove irrelevant data to improve model performance. Which of the following actions, either alone or in combination, would be the MOST effective for removing irrelevant data for a model predicting user conversion rates, and which Snowpark Python code snippets demonstrate these actions? Assume that conversion depends on page interaction and a model will only leverage session id and session duration.

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

2. A data science team is using Snowpark ML to train a classification model. They want to log model metadata (e.g., training parameters, evaluation metrics) and artifacts (e.g., the serialized model file) for reproducibility and model governance purposes. Which of the following approaches is the most appropriate for integrating model logging and artifact management within the Snowpark ML workflow, minimizing operational overhead?

A) Leverage the MLflow integration within Snowpark, utilizing its ability to track experiments, log parameters and metrics, and store model artifacts directly within Snowflake stages or external storage.
B) Employ a separate, external model management platform (e.g., Databricks MLflow, SageMaker Model Registry) and configure Snowpark to interact with it via API calls during model training and deployment.
C) Only track basic model performance metrics in a Snowflake table and rely on code versioning (e.g., Git) for model artifact management.
D) Use a custom Python function to manually write model metadata to a Snowflake table and store the model file in a Snowflake stage.
E) Serialize the model object to a string and store it as a VARIANT column in a Snowflake table, alongside the model metadata.

3. You are tasked with identifying fraudulent transactions from unstructured log data stored in Snowflake. The logs contain various fields, including timestamps, user IDs, and transaction details embedded within free-text descriptions. You plan to use a supervised learning approach, having labeled a subset of transactions as 'fraudulent' or 'not fraudulent.' Which of the following methods best describes the extraction and processing of this data for training a machine learning model within Snowflake?

A) Export the entire log data to an external machine learning platform (e.g., AWS SageMaker) and perform feature extraction, NLP processing, and model training there. Import the trained model back into Snowflake as a UDF for prediction.
B) Use regular expressions within a Snowflake UDF to extract relevant information (e.g., amount, item description) from the log descriptions. Convert extracted data into numerical features using one-hot encoding within the UDF. Then, train a model using the extracted numerical features directly within Snowflake using SQL extensions for machine learning.
C) Use a combination of regular expressions and natural language processing (NLP) techniques within Snowflake UDFs to extract key features such as transaction amounts, product categories, and sentiment scores from the log descriptions. Then, combine these extracted features with other structured data (e.g., user demographics) and train a classification model using these features. The NLP steps include tokenization, stop word removal, and TF-IDF vectorization.
D) Treat the unstructured log description as a categorical feature and directly apply one-hot encoding within Snowflake, then train a classification model. Due to high dimensionality perform PCA for dimensionality reduction before training.
E) Extract the entire log description field and train a word embedding model (e.g., Word2Vec) on the entire dataset. Average the word vectors for each transaction's log description to create a document vector. Train a classification model (e.g., Random Forest) on these document vectors within Snowflake.

4. You are a data scientist working with a Snowflake table named 'CUSTOMER TRANSACTIONS' that contains sensitive PII data, including customer names and email addresses. You need to create a representative sample of 1% of the data for model development, ensuring that the sample is anonymized and protects customer privacy. The sample must be reproducible for future model iterations.
Which of the following steps are most appropriate using Snowpark for Python and SQL?

A) Use the 'SAMPLE clause in a SQL query to extract 1% of the rows, then apply SHA256 hashing to the 'customer_name' and 'email_addresS columns within Snowpark using a UDF. Seed the sampling for reproducibility.
B) Use Snowpark DataFrame's 'sample' function with a fraction of 0.01 and a fixed random seed. Before sampling, create a view that masks 'customer_name' and 'email_address' columns, and then sample from the view.
C) Use the 'QUALIFY OVER (ORDER BY RANDOM()) (SELECT COUNT( ) 0.01 FROM CUSTOMER_TRANSACTIONS)' clause with SHA256 on sensitive columns directly within a CREATE TABLE AS statement to generate an anonymized sample. The function should return only 1 percentage of row.
D) Employ stratified sampling based on a customer segment column, then anonymize data. Use the TABLESAMPLE BERNOULLI function in SQL with a 1 percent sample rate. Apply SHA256 hashing to the 'customer_name' and 'email_addresS columns using SQL functions.
E) Create a new table using 'CREATE TABLE AS SELECT statement combined with 'SAMPLE clause and SHA256 hashing functions in SQL to create the sample and anonymize data. Manually seed the random number generator in Python before executing the SQL statement via Snowpark.

5. You have deployed a sentiment analysis model on AWS SageMaker and want to integrate it with Snowflake using an external function. You've created an API integration object. Which of the following SQL statements is the most secure and efficient way to create an external function that utilizes this API integration, assuming the model expects a JSON payload with a 'text' field, the API integration is named 'sagemaker_integration' , the SageMaker endpoint URL is 'https://your-sagemaker-endpoint.com/invoke' , and you want the Snowflake function to be named 'predict_sentiment'?

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

Solutions: