Snowflake SnowPro Advanced: Data Scientist Certification - DSA-C03

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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You're working with a Snowflake stage named that contains several versions of your machine learning model, named 'model_vl .pkl' , 'model_v2.pkl' , and You want to programmatically list all files in the stage and retrieve the creation time of the latest version (i.e., using SnowSQL. Which of the following approaches is most efficient and correct?

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

2. A data scientist is performing exploratory data analysis on a table named 'CUSTOMER TRANSACTIONS. They need to calculate the standard deviation of transaction amounts C TRANSACTION AMOUNT) for different customer segments CCUSTOMER SEGMENT). The 'CUSTOMER SEGMENT column can contain NULL values. Which of the following SQL statements will correctly compute the standard deviation, excluding NULL transaction amounts, and handling NULL customer segments by treating them as a separate segment called 'Unknown'? Consider using Snowflake-specific functions where appropriate.

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

3. You are deploying a machine learning model to Snowflake using a Python UDF. The model predicts customer churn based on a set of features. You need to handle missing values in the input data'. Which of the following methods is the MOST efficient and robust way to handle missing values within the UDF, assuming performance is critical and you don't want to modify the underlying data tables?

A) Pre-process the data in Snowflake using SQL queries to replace missing values with the mean for numerical features and the mode for categorical features before calling the UDF.
B) Raise an exception within the UDF when a missing value is encountered, forcing the calling application to handle the missing values.
C) Implement a custom imputation strategy using 'numpy.where' within the UDF, basing the imputation value on a weighted average of other features in the row.
D) Use within the UDF to forward fill missing values. This assumes the data is ordered in a meaningful way, allowing for reasonable imputation.
E) Use within the UDF, replacing missing values with a global constant (e.g., 0) defined outside the UDF. This constant is pre-calculated based on the training dataset's missing value distribution.

4. A data scientist is tasked with building a real-time customer support system using Snowflake Cortex. The system needs to analyze incoming customer messages and categorize them into predefined issue types (e.g., billing, technical support, account management) for efficient routing to the appropriate support team. Considering the need for low latency and high accuracy, which of the following approaches would be the MOST suitable for implementing this categorization task using Snowflake Cortex, considering the costs and trade-offs involved?

A) Directly calling the Snowflake Cortex 'COMPLETE' endpoint with a detailed prompt for each incoming message, instructing it to categorize the message based on the predefined issue types.
B) Developing a custom Python UDF that uses a third-party LLM API (e.g., OpenAl) to categorize the messages and deploying it in Snowflake, handling API authentication and rate limiting within the UDF.
C) Fine-tuning a pre-trained language model within Snowflake using the 'CREATE SNOWFLAKE.ML.ANACONDA_MODEL' command on a dataset of historical customer messages and their corresponding issue types, then deploying this fine-tuned model for real-time categorization via a user-defined function (UDF).
D) Leveraging the Snowflake Cortex built-in categorization task-specific model (e.g., using the 'SNOWFLAKE.ML.PREDICT' function with the appropriate model name) to categorize incoming messages without any fine-tuning.
E) Creating a series of SQL 'CASE' statements to categorize the messages based on keyword matching within the message text. Use regular expressions for more complex pattern matching.

5. You are tasked with optimizing the hyperparameter tuning process for a complex deep learning model within Snowflake using Snowpark Python. The model is trained on a large dataset stored in Snowflake, and you need to efficiently explore a wide range of hyperparameter values to achieve optimal performance. Which of the following approaches would provide the MOST scalable and performant solution for hyperparameter tuning in this scenario, considering the constraints and capabilities of Snowflake?

A) Employing Scikit-learn's 'GridSearchCV' or 'RandomizedSearchCV' within a single Snowpark Python UDF, processing the entire dataset and hyperparameter search sequentially.
B) Using a simple 'for' loop to iterate through a predefined set of hyperparameter combinations, training the model for each combination within a Snowpark Python UDF, and storing the results in a Snowflake table.
C) Implementing a custom hyperparameter optimization algorithm using Snowpark Python UDFs, manually distributing the hyperparameter search across multiple Snowflake virtual warehouses, and aggregating the results in a central table.
D) Using 'Hyperopt' within a Snowpark Python UDF, relying on its sequential optimization approach without any form of parallelization or distribution of the workload.
E) Leveraging a distributed hyperparameter tuning framework like Ray Tune or Dask-ML within Snowpark Python, utilizing Snowflake's compute resources for parallel training and evaluation of different hyperparameter configurations.

Solutions: