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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

Question 1

You are building a binary classification model in Snowflake to predict customer churn based on historical customer data, including demographics, purchase history, and engagement metrics. You are using the SNOWFLAKE.ML.ANOMALY package. You notice a significant class imbalance, with churn representing only 5% of your dataset. Which of the following techniques is LEAST appropriate to handle this class imbalance effectively within the SNOWFLAKE.ML framework for structured data and to improve the model's performance on the minority (churn) class?

A. Using a clustering algorithm (e.g., K-Means) on the features and then training a separate binary classification model for each cluster to capture potentially different patterns of churn within different customer segments.
B. Adjusting the decision threshold of the trained model to optimize for a specific metric, such as precision or recall, using a validation set. This can be done by examining the probability outputs and choosing a threshold that maximizes the desired balance.
C. Downsampling the majority class to create a more balanced training dataset within Snowflake using SQL before feeding the data to the modeling function.
D. Applying a SMOTE (Synthetic Minority Over-sampling Technique) or similar oversampling technique to generate synthetic samples of the minority class before training the model outside of Snowflake, and then loading the augmented data into Snowflake for model training.
E. Using the 'sample_weight' parameter in the 'SNOWFLAKE.ML.ANOMALY.FIT function to assign higher weights to the minority class instances during model training.

Question 2

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 B
B. Option E
C. Option D
D. Option A
E. Option C

Question 3

You are tasked with training a model within Snowflake to predict customer churn for a telecommunications company. The dataset is stored in a Snowflake table named 'CUSTOMER DATA. The features include 'age', and 'data_usage'. The target variable is 'churned' (boolean). You want to use the SNOWFLAKE.ML.ANACONDA INTEGRATION to leverage Scikit-learn for model training. Which of the following code snippets correctly performs model training with Snowflake ML, addressing potential issues like feature scaling and data type handling within the stored procedure?

A.

B.

C.

D.

Question 4

You have successfully deployed a real-time prediction service using Snowpark Container Services, consuming events from a Kafka topic. The service leverages a large language model (LLM) stored in the Snowflake Model Registry. You observe that inference latency is high and the service is struggling to keep up with the incoming event rate. You need to optimize the service for higher throughput and lower latency. Which of the following actions, when implemented together, would most effectively improve the performance of your Snowpark Container Services deployment?

A. Enable autoscaling for the service based on CPU utilization. Remove all logging statements from the containerized application to reduce 1/0 overhead.
B. Increase the 'container.resources.memory' allocation for the service. Implement caching of frequently accessed data within the containerized application.
C. Increase the number of replicas for the service. Implement batching within the containerized application to process multiple events in a single inference call.
D. Switch to a smaller, less accurate LLM. Increase the 'container.resources.cpu' allocation for the service. Ensure data is pre-processed before sending to kafka.
E. Implement custom monitoring solution outside of snowflake and determine bottleneck of your application. Increase the container.resources.gpu allocation for the service.

Question 5

You've created a Python UDF in Snowflake that uses the 'numpy' and libraries to perform complex statistical calculations on time-series data'. The UDF is deployed successfully, but when you execute it on a large dataset, you observe significant performance bottlenecks. Analyzing the execution plan reveals that the UDF is being executed serially for each row of the input data, preventing Snowflake from leveraging its parallel processing capabilities. What strategies can you employ to improve the performance and enable parallel execution of the UDF in Snowflake?

A. Use the 'snowflake.snowpark' library to create a distributed Pandas DataFrame and perform computations directly within the Snowflake engine in a parallel manner.
B. Rewrite the UDF using Snowflake's Java UDF functionality instead of Python, as Java is inherently faster for numerical computations.
C. Modify the UDF to accept a Pandas DataFrame as input instead of individual row values. Ensure your UDF is vectorized to process the entire DataFrame at once.
D. Increase the Snowflake warehouse size to provide more resources for serial execution.
E. Decompose the UDF into smaller, more manageable functions and register each as a separate UDF, hoping Snowflake will parallelize the execution of these smaller UDFs automatically.

Solutions: