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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are tasked with deploying a fraud detection model in Snowflake using the Model Registry. The model is trained on a dataset that is updated daily. You need to ensure that your deployed model uses the latest approved version and that you can easily roll back to a previous version if any issues arise. Which of the following approaches would provide the most robust and maintainable solution for model versioning and deployment, considering minimal downtime during updates and rollback?

A) Register each new model version in the Snowflake Model Registry and promote the desired version to 'PRODUCTION' stage. Update a single UDF that dynamically fetches the model based on the 'PRODUCTION' stage metadata.
B) Deploy a new Snowflake UDF referencing the model file directly in cloud storage every time the model is retrained. Rely on cloud storage versioning for rollback.
C) Store all model versions within a single model registry entry without versioning, overwriting the existing file with each new training run.
D) Use Snowflake Tasks to periodically refresh a table containing the latest model weights. The UDF directly queries this table for predictions.
E) Create multiple Snowflake UDFs, each corresponding to a different model version. Manually switch the active UDF by updating application code when a new model is deployed.

2. 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) 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.
B) Increase the Snowflake warehouse size to provide more resources for serial execution.
C) 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.
D) Rewrite the UDF using Snowflake's Java UDF functionality instead of Python, as Java is inherently faster for numerical computations.
E) Use the 'snowflake.snowpark' library to create a distributed Pandas DataFrame and perform computations directly within the Snowflake engine in a parallel manner.

3. You are tasked with developing a Snowpark Python function to identify and remove near-duplicate text entries from a table named 'PRODUCT DESCRIPTIONS. The table contains a 'PRODUCT ONT) and 'DESCRIPTION' (STRING) column. Near duplicates are defined as descriptions with a Jaccard similarity score greater than 0.9. You need to implement this using Snowpark and UDFs. Which of the following approaches is most efficient, secure, and correct to implement?

A) Define a Python UDF that calculates the Jaccard similarity between all pairs of descriptions in the table. Use a cross join to compare all rows, then filter based on the Jaccard similarity threshold. Finally, delete the near-duplicate rows based on a chosen tie-breaker (e.g., smallest PRODUCT_ID).
B) Define a Python UDF that calculates the Jaccard similarity. Use 'GROUP BY to group descriptions by the 'PRODUCT ID. Apply the UDF on this grouped data to remove duplicates with similarity score greater than threshold.
C) Define a Python UDF that calculates the Jaccard similarity. Create a new table, 'PRODUCT DESCRIPTIONS NO DUPES , and insert the distinct descriptions based on the similarity score. Rows in the original table with similar product description must be inserted with lowest product id into new table.
D) Use the function directly in a SQL query without a UDF. Partition the data by 'PRODUCT_ID' and remove near duplicates where the approximate Jaccard index is above 0.9.
E) Define a Python UDF to calculate Jaccard similarity. Create a temporary table with a ROW NUMBER() column partitioned by a hash of the DESCRIPTION column. Calculate the Jaccard similarity between descriptions within each partition. Filter and remove near duplicates based on a tie-breaker (smallest PRODUCT_ID).

4. You are using Snowflake Cortex to build a customer support chatbot that leverages LLMs to answer customer questions. You have a knowledge base stored in a Snowflake table. The following options describe different methods for using this knowledge base in conjunction with the LLM to generate responses. Which of the following approaches will likely result in the MOST accurate, relevant, and cost-effective responses from the LLM?

A) Use Snowflake Cortex's 'COMPLETE function without any external knowledge base. Rely solely on the LLM's pre-trained knowledge.
B) Directly prompt the LLM with the entire knowledge base content for each customer question. Concatenate all knowledge base entries into a single string and include it in the prompt.
C) Partition your database by different subject matter and then query the specific partitions for your information.
D) Use Retrieval-Augmented Generation (RAG). Generate vector embeddings for the knowledge base entries, perform a similarity search to find the most relevant entries for each customer question, and include those entries in the prompt.
E) Fine-tune the LLM on the entire knowledge base. Train a custom LLM model specifically on the knowledge base data.

5. You have built a customer churn prediction model using Snowflake ML and deployed it as a Python stored procedure. The model outputs a churn probability for each customer. To assess the model's stability and potential business impact, you need to estimate confidence intervals for the average churn probability across different customer segments. Which of the following approaches is MOST appropriate for calculating these confidence intervals, considering the complexities of deploying and monitoring models within Snowflake?

A) Implement a custom SQL function to approximate confidence intervals based on the Central Limit Theorem, assuming the churn probabilities are normally distributed.
B) Pre-calculate confidence intervals during model training and store them as metadata alongside the model in Snowflake. This avoids runtime computation.
C) Calculate a single confidence interval for the overall average churn probability across all customers. Customer segmentation confidence intervals are statistically invalid and not applicable for Snowflake ML models.
D) Use a separate SQL query to extract the churn probabilities and customer segment information from the table where the stored procedure writes its output. Then, use a statistical programming language like Python (outside of Snowflake) to calculate the confidence intervals for each segment.
E) Calculate confidence intervals directly within the Python stored procedure using bootstrapping techniques and appropriate libraries (e.g., scikit-learn) before returning the churn probability.

Solutions: