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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. 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 A
B) Option E
C) Option B
D) Option C
E) Option D

2. You've built a machine learning model in scikit-learn and want to deploy it to Snowflake for real-time inference. You have the following options for deploying the model. Select all that apply and are considered a best practice for cost and time optimization:

A) Migrate your entire Snowflake data warehouse to a different platform which better supports real-time ML inference.
B) Implement a custom microservice that reads data from Snowflake, performs inference using the scikit-learn model, and writes the predictions back to Snowflake.
C) Package the scikit-learn model using 'joblib' or 'pickle' , store it in a Snowflake stage, and create a Snowflake UDF (User-Defined Function) in Python to load the model from the stage and perform inference.
D) Use Snowflake's Snowpark Python API to directly load the model from a stage and execute inference using Snowpark DataFrames, which will implicitly handle the distributed processing of the data.
E) Create a Snowflake external function that calls a cloud-based (AWS SageMaker, Azure Machine Learning, GCP Vertex A1) endpoint for inference, passing the input data to the endpoint and receiving the prediction back.

3. You are tasked with building a machine learning model in Python using data stored in Snowflake. You need to efficiently load a large table (100GB+) into a Pandas DataFrame for model training, minimizing memory footprint and network transfer time. You are using the Snowflake Connector for Python. Which of the following approaches would be MOST efficient for loading the data, considering potential memory limitations on your client machine and the need for data transformations during the load process?

A) Utilize the 'execute_stream' method of the Snowflake cursor to fetch data in chunks, apply transformations in each chunk, and append to a larger DataFrame or process iteratively without creating a large in-memory DataFrame.
B) Load the entire table into a Pandas DataFrame using with a simple 'SELECT FROM my_table' query and then perform data transformations in Pandas.
C) Use 'snowsql' to unload the table to a local CSV file, then load the CSV file into a Pandas DataFrame.
D) Use the 'COPY INTO' command to unload the table to an Amazon S3 bucket and then use bot03 in your python script to fetch data from s3 and load into pandas dataframe.
E) Create a Snowflake view with the necessary transformations, and then load the view into a Pandas DataFrame using 'pd.read_sql()'.

4. You are working with a Snowflake table 'CUSTOMER TRANSACTIONS containing customer IDs, transaction dates, and transaction amounts. You need to identify customers who are likely to churn (stop making transactions) in the next month using a supervised learning model. Which of the following strategies would be MOST appropriate to define the target variable (churned vs. not churned) and create features for this churn prediction problem, suitable for a Snowflake-based machine learning pipeline?

A) Define churn based on a fixed threshold of total transaction value over a predefined period. Feature Engineering should purely consist of time series decomposition using Snowflake's built-in functions.
B) Define churn as customers with no transactions in the next month (the prediction target). Create features including: Recency (days since last transaction), Frequency (number of transactions in the past 3 months), Monetary Value (average transaction amount over the past 3 months), and trend of transaction amounts (using linear regression slope over the past 6 months).
C) Define churn as customers who haven't made a transaction in the past 6 months. Create a single feature representing the total number of transactions the customer has ever made.
D) Define churn as customers with zero transactions in the last month. Create features like average transaction amount over the past year, number of transactions in the past month, and recency (time since the last transaction).
E) Define churn as customers with a significant decrease (e.g., 50%) in transaction amounts compared to the previous month. Create features based on demographic data and customer segmentation information, joined from other Snowflake tables.

5. You are building a product recommendation system using Snowflake Cortex. You have a table 'PRODUCT DESCRIPTIONS' containing product IDs and textual descriptions. You want to generate vector embeddings for these descriptions to perform similarity searches. However, you need to control the cost and latency of the embedding generation process. Which of the following strategies and considerations are MOST important for optimizing performance and cost when generating vector embeddings in Snowflake Cortex using a UDF?

A) Cache the results of the embedding LJDF. Implement a caching mechanism (e.g., using a Snowflake table) to store the embeddings for frequently accessed product descriptions, avoiding redundant embedding calculations. use a materialized view.
B) Optimize the batch size passed to the embedding UDF. Experiment with different batch sizes to find the optimal trade-off between throughput and latency. Too large batches might cause memory issues, while too small batches increase overhead. Consider using a batch size of 64 or 128 as a starting point, adjusting based on your dataset and resource constraints.
C) Use a larger Snowflake warehouse size. Increasing the warehouse size always linearly reduces embedding generation time and cost.
D) Partition the 'PRODUCT DESCRIPTIONS' table by product category and generate embeddings for each partition separately. This helps to distribute the workload and reduce the size of the data processed by each UOF call. This makes more sense and is faster to re-create the table.
E) Use the smallest available Cortex embedding model. Smaller models are always faster and cheaper, regardless of the dataset size.

Solutions: