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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You've built a regression model in Snowflake using Snowpark Python to predict customer churn. After evaluating the model on a holdout dataset, you generate a residuals plot. The plot shows a distinct 'U' shape. Which of the following interpretations and subsequent actions are most appropriate?

A) The 'U' shape suggests that the learning rate is too high. Reduce the learning rate of the model.
B) The 'U' shape indicates that the residuals are normally distributed. This is a positive sign and no changes are required.
C) The 'U' shape implies multicollinearity is present. Use techniques like Variance Inflation Factor (VIF) to identify and remove highly correlated features.
D) The 'U' shape indicates homoscedasticity. No changes to the model are necessary.
E) The 'U' shape suggests the model is missing important non-linear relationships. Consider adding polynomial features or using a non-linear model like a Random Forest or Gradient Boosting Machine.

2. 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) Use a larger Snowflake warehouse size. Increasing the warehouse size always linearly reduces embedding generation time and cost.
B) 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.
C) Use the smallest available Cortex embedding model. Smaller models are always faster and cheaper, regardless of the dataset size.
D) 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.
E) 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.

3. 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) Implement custom monitoring solution outside of snowflake and determine bottleneck of your application. Increase the container.resources.gpu allocation for the service.
E) 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.

4. You are working with a Snowflake table named 'CUSTOMER DATA' containing customer information, including a 'PHONE NUMBER' column. Due to data entry errors, some phone numbers are stored as NULL, while others are present but in various inconsistent formats (e.g., with or without hyphens, parentheses, or country codes). You want to standardize the 'PHONE NUMBER column and replace missing values using Snowpark for Python. You have already created a Snowpark DataFrame called 'customer df representing the 'CUSTOMER DATA' table. Which of the following approaches, used in combination, would be MOST efficient and reliable for both cleaning the existing data and handling future data ingestion, given the need for scalability?

A) Create a Snowflake Pipe with a COPY INTO statement and a transformation that uses a SQL function within the COPY INTO statement to format the phone numbers and replace NULL values during data loading. Also, implement a Python UDF for correcting already existing data.
B) Use a series of and methods on the Snowpark DataFrame to handle NULL values and different phone number formats directly within the DataFrame operations.
C) Leverage Snowflake's data masking policies to mask any invalid phone number and create a view that replaces NULL values with 'UNKNOWN'. This approach doesn't correct existing data but hides the issue.
D) Use a UDF (User-Defined Function) written in Python that formats the phone numbers based on a regular expression and applies it to the DataFrame using For NULL values, replace them with a default value of 'UNKNOWN'.
E) Create a Snowflake Stored Procedure in SQL that uses regular expressions and 'CASE statements to format the "PHONE_NUMBER column and replace NULL values. Call this stored procedure from a Snowpark Python script.

5. You are working with a dataset in Snowflake containing customer reviews stored in a 'REVIEWS' table. The 'SENTIMENT SCORE column contains continuous values ranging from -1 (negative) to 1 (positive). You need to create a new column, 'SENTIMENT CATEGORY, based on the following rules: 'Negative': 'SENTIMENT SCORE < -0.5 'Neutral': -0.5 'SENTIMENT SCORE 0.5 'Positive': 'SENTIMENT SCORE > 0.5 You also want to binarize this 'SENTIMENT CATEGORY column into three separate columns: 'IS NEGATIVE, 'IS NEUTRAL', and 'IS POSITIVE. Which of the following SQL statements correctly implements both the categorization and subsequent binarization?

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

Solutions: