Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are building a multi-class classification model in Snowflake to predict the category of customer support tickets (e.g., 'Billing', 'Technical Support', 'Sales Inquiry', 'Account Management', 'Feature Request') based on the ticket's text content. The initial model evaluation shows an overall accuracy of 75%, but the 'Feature Request' category has a significantly lower precision and recall compared to other categories. Which of the following strategies would be MOST effective in addressing this issue, considering the limitations and advantages of Snowflake's data processing capabilities and typical machine learning practices?

A) Apply a cost-sensitive learning approach during model training, assigning a higher misclassification cost to errors involving the 'Feature Request' category. This encourages the model to prioritize correctly classifying feature requests.
B) Engineer new features specifically designed to improve the model's ability to distinguish 'Feature Request' tickets from other categories. This could involve creating sentiment scores for 'innovation' or using topic modeling to identify key themes related to feature requests.
C) Oversample the 'Feature Request' category in the training dataset before training the model. This involves creating synthetic data points or duplicating existing data to balance the class distribution. This can be done using SQL and Snowflake's internal stage for storing temporary data before training.
D) All of the above.
E) Increase the threshold for classifying a ticket as 'Feature Request' to improve precision, even if it further reduces recall. This prioritizes accurate identification of feature requests over capturing all of them.

2. You're developing a model to predict equipment failure using sensor data stored in Snowflake. The dataset is highly imbalanced, with failure events (positive class) being rare compared to normal operation (negative class). To improve model performance, you're considering both up-sampling the minority class and down-sampling the majority class. Which of the following statements regarding the potential benefits and drawbacks of combining up-sampling and down-sampling techniques in this scenario are TRUE? (Select TWO)

A) Over-sampling, combined with downsampling, makes the model more prone to overfitting since this causes the model to train on a large dataset.
B) Combining up-sampling and down-sampling can lead to a more balanced dataset, potentially improving the model's ability to learn patterns from both classes without introducing excessive bias from solely up-sampling.
C) The optimal sampling ratio for both up-sampling and down-sampling must always be 1:1, regardless of the initial class distribution.
D) Using both up-sampling and down-sampling always guarantees improved model performance compared to using only one of these techniques, regardless of the dataset characteristics.
E) Down-sampling, when combined with up-sampling, can exacerbate the risk of losing important information from the majority class, leading to underfitting, especially if the majority class is already relatively small.

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. 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.
B) 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).
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) 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).
E) 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.

4. A data scientist is using association rule mining with the Apriori algorithm on customer purchase data in Snowflake to identify product bundles. After generating the rules, they obtain the following metrics for a specific rule: Support = 0.05, Confidence = 0.7, Lift = 1.2. Consider that the overall purchase probability of the consequent (right-hand side) of the rule is 0.4. Which of the following statements are CORRECT interpretations of these metrics in the context of business recommendations for product bundling?

A) Customers who purchase the items in the antecedent are 70% more likely to also purchase the items in the consequent, compared to the overall purchase probability of the consequent.
B) The lift value of 1.2 indicates that customers are 20% more likely to purchase the consequent items when they have also purchased the antecedent items, compared to the baseline purchase probability of the consequent items.
C) The rule applies to 5% of all transactions in the dataset, meaning 5% of the transactions contain both the antecedent and the consequent.
D) The confidence of 0.7 indicates that 70% of transactions containing the antecedent also contain the consequent.
E) The lift value of 1.2 suggests a strong negative correlation between the antecedent and consequent, indicating that purchasing the antecedent items decreases the likelihood of purchasing the consequent items.

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) 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.
B) 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.
C) 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.
D) 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.
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: