You are tasked with preparing a Snowflake table named 'PRODUCT REVIEWS' for sentiment analysis. This table contains columns like 'REVIEW ID, 'PRODUCT ID', 'REVIEW TEXT', 'RATING', and 'TIMESTAMP'. Your goal is to remove irrelevant fields to optimize model training. Which of the following options represent valid and effective strategies, using Snowpark SQL, for identifying and removing irrelevant or problematic fields from the 'PRODUCT REVIEWS' table, considering both storage efficiency and model accuracy? Assume that the model only need review text and review id and the rating.
Correct Answer: E
All of the options are valid strategies. A directly removes the irrelevant 'TIMESTAMP' column, saving storage. B creates a VIEW which offers a non-destructive way to filter columns. C creates a new table with only the necessary columns. D handles rows with missing review text and removes other irrelevant columns. Therefore, choosing 'All of the above' is the correct response. Depending on use case and downstream application we can make use of any of the options, hence more than one option is correct.
Question 52
You are tasked with building a data science pipeline in Snowflake to predict customer churn. You have trained a scikit-learn model and want to deploy it using a Python UDTF for real-time predictions. The model expects a specific feature vector format. You've defined a UDTF named 'PREDICT CHURN' that loads the model and makes predictions. However, when you call the UDTF with data from a table, you encounter inconsistent prediction results across different rows, even when the input features seem identical. Which of the following are the most likely reasons for this behavior and how would you address them?
Correct Answer: A,C
Options A and C address the most common causes of inconsistent UDTF predictions with scikit-learn models. A covers the essential aspect of correct serialization/deserialization for model persistence and retrieval in the Snowflake environment, which ensures model state consistency. C focuses on the critical data type compatibility between the input data and the model expectations, which, if mismatched, can lead to unexpected prediction variations. Option B is incorrect, the model should be loaded in the process method. Option D is only relevant if you are using a stateful model, but it is still not the most likely cause. Option E is incorrect as the Model prediction method gives deterministic ouput for given inputs.
Question 53
A data scientist is tasked with identifying customer segments for a new marketing campaign using transaction data stored in Snowflake. The transaction data includes features like transaction amount, frequency, recency, and product category. Which unsupervised learning algorithm would be MOST appropriate for this task, considering scalability and Snowflake's data processing capabilities, and what preprocessing steps are crucial before applying the algorithm?
Correct Answer: E
K-Means clustering is a suitable algorithm for customer segmentation due to its scalability and efficiency. Min-max scaling is important to ensure that features with larger ranges don't dominate the distance calculations. Converting categorical features to numerical representation (e.g., one-hot encoding) is also essential for K-Means. The elbow method or silhouette analysis helps determine the optimal number of clusters. Options A, B, C, and D have flaws related to scaling requirements, algorithm suitability for large datasets, or lack of pre-processing.
Question 54
You have a Snowflake Model Registry set up and are managing multiple versions of a machine learning model. You want to programmatically retrieve a specific version of the model and load it for inference within a Snowflake Snowpark Python UDE Assume your registry name is 'my_registry', the model name is 'credit risk_model', and you want to retrieve version 'v2'. How would you achieve this using Snowpark Python?
Correct Answer: A
Option A correctly uses the method to directly load the model into memory for inference. This is the intended method for retrieving and using models managed by the Snowflake Model Registry. Option B uses 'joblib.load' which bypasses the Model Registry completely after getting the path. Option C is suitable if the model was trained using MLFlow, not generic scikit learn. Option D is an imaginary command not present in Model Registry and Option E involves calling udf to load and that is not right way to programatically load the model from registry and do inference with it.
Question 55
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?
Correct Answer: D
Option D is the most efficient, secure, and correct approach for removing near-duplicate text entries using Snowpark and UDFs. It correctly addresses both the computational complexity and the security implications of the task. - It create a temporary table because we are doing operations of delete and create a table which is best done via temporary table. - It uses bucketing (hashing descriptions) to reduce the number of comparisons. This significantly improves performance compared to comparing all possible pairs of descriptions which is what options A and B do. - Use ROW_NUMBER() to flag duplicate for deletion with threshold. Option A is not optimal due to the complexity of cross join. Option B is incorrect because there is data and functionality that is lost with the insertion of distinct entries based on score. Also, it would be inefficient as it required re-evaluation of score on insertion. Option C is incorrect because Grouping by Product ID will not allow for similarity calculation across different product IDs. Option E is not applicable because Snowflake does not have a built-in 'APPROX JACCARD INDEX' function to apply directly in a SQL query.
