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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A financial institution wants to use Snowflake Cortex to analyze customer reviews and feedback extracted from various online sources to gauge customer sentiment towards their new mobile banking application. The goal is to identify positive, negative, and neutral sentiments, and also extract key phrases that drive these sentiments. Which of the following steps represent a viable workflow for achieving this using Snowflake Cortex and related functionalities?

A) 1. Create a Streamlit application hosted externally that connects to the Snowflake database. 2. The Streamlit app uses a Python library like 'transformers' to perform sentiment analysis and key phrase extraction on the customer reviews read from Snowflake. 3. The results are then written back to a separate Snowflake table.
B) 1. Ingest the customer reviews into a Snowflake table. 2. Use the 'SNOWFLAKE.ML.PREDICT' function with a sentiment analysis model to determine the overall sentiment score for each review. 3. Apply a separate key phrase extraction model via 'SNOWFLAKE.ML.PREDICT' to identify important keywords in the reviews.
C) 1. Ingest the customer reviews into a Snowflake table. 2. Use Snowflake's built-in 'NLP_SENTIMENT' function (if available) or a similar UDF based on a pre- trained sentiment analysis model to get the sentiment score. 3. Use regular expressions in SQL to extract key phrases based on frequency and context.
D) 1. Ingest the customer reviews into a Snowflake table. 2. Create a custom JavaScript UDF that calls the Snowflake Cortex 'COMPLETE' endpoint with a prompt that asks for both sentiment and key phrases. 3. Store the results in a new Snowflake table.
E) 1. Ingest the customer reviews into a Snowflake table. 2. Use the 'SNOWFLAKML.PREDICT' function with the appropriate task-specific model to determine the sentiment score for each review. 3. Further fine-tune the sentiment model with customer review data to improve the score and accuracy.

2. You are building a data science pipeline in Snowflake to perform time series forecasting. You've decided to use a Python UDTF to encapsulate the forecasting logic using a library like 'Prophet'. The UDTF needs to access historical data to train the model and generate forecasts. The data is stored in a Snowflake table named 'SALES DATA with columns 'DATE' and 'SALES'. Which of the following approaches is/are most efficient and secure for accessing the 'SALES DATA table from within the UDTF during model training?

A) Use the 'snowflake.connector' to connect to Snowflake using a dedicated service account with read-only access to the 'SALES DATA' table. Store the service account credentials securely in Snowflake secrets and retrieve them within the UDTF.
B) Create a view on top of 'SALES DATA' and grant access to the UDTF's owner role to the view. Then, query the view using Snowpark within the UDTF.
C) Pass the entire 'SALES DATA' table as a Pandas DataFrame to the UDTF as an argument. This approach is suitable for smaller datasets. Do not partition the data frame.
D) Bypass Snowflake entirely and load data from S3 stage into a Pandas dataframe.
E) Use the Snowpark API within the UDTF to query the 'SALES DATA' table directly, leveraging the existing Snowflake session context. This requires no additional credentials management.

3. You are developing a churn prediction model using Snowpark Python and Scikit-learn. After initial model training, you observe significant overfitting. Which of the following hyperparameter tuning strategies and code snippets, when implemented within a Snowflake Python UDF, would be MOST effective to address overfitting in a Ridge Regression model and how can you implement a reproducible model with minimal code?

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

4. You're deploying a pre-trained model for fraud detection that's hosted as a serverless function on Google Cloud Functions. This function requires two Snowflake tables: 'TRANSACTIONS (containing transaction details) and 'CUSTOMER PROFILES (containing customer information), to be joined and used as input for the model. The external function in Snowflake, 'DETECT FRAUD', should process batches of records efficiently. Which of the following approaches are most suitable for optimizing data transfer and processing between Snowflake and the Google Cloud Function?

A) Utilize Snowflake's external functions feature to send batches of data from the joined 'TRANSACTIONS' and 'CUSTOMER PROFILES tables to the 'DETECT_FRAUD function in a structured format (e.g., JSON) using HTTP requests. Implement proper error handling and retry mechanisms.
B) Use Snowflake's Java UDF functionality to directly connect to the Google Cloud Function's database, bypassing the need for an external function or data transfer through HTTP.
C) Create a Snowflake pipe that automatically streams new transaction data to the Google Cloud Function whenever new records are inserted into the 'TRANSACTIONS' table, triggering the fraud detection model in real-time.
D) Serialize the joined 'TRANSACTIONS' and 'CUSTOMER_PROFILES data into a large CSV file, store it in a cloud storage bucket, and then pass the URL of the CSV file to the 'DETECT FRAUD function.
E) Within the 'DETECT FRAUD function, execute SQL queries directly against Snowflake using the Snowflake JDBC driver to fetch the necessary data from the "TRANSACTIONS' and 'CUSTOMER PROFILES' tables.

5. You have implemented a Python UDTF in Snowflake to train a machine learning model incrementally using incoming data'. The UDTF performs well initially, but as the volume of data processed increases significantly, you observe a noticeable degradation in performance and an increase in query execution time. You suspect that the bottleneck is related to the way the model is being updated and persisted within the UDTF. Which of the following optimization strategies, or combination of strategies, would be MOST effective in addressing this performance issue?

A) Instead of updating the model incrementally within the UDTF for each row, batch the incoming data into larger chunks and perform model updates only on these batches. Use Snowflake's VARIANT data type to store these batches temporarily.
B) Persist the trained model to a Snowflake stage after each batch update. Use a separate UDF (User-Defined Function) to load the model from the stage before processing new data. This decouples model training from inference.
C) Use the 'cachetools' library within the UDTF to cache intermediate results and reduce redundant calculations during each function call. Configure the cache with a maximum size and eviction policy appropriate for the data volume.
D) Rewrite the UDTF in Java or Scala, as these languages generally offer better performance compared to Python for computationally intensive tasks. Use the same machine learning libraries that you used with Python.
E) Leverage Snowflake's external functions and a cloud-based ML platform (e.g., SageMaker, Vertex A1) to offload the model training process. The UDTF would then only be responsible for data preparation and calling the external function.

Solutions: