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最新的 SnowPro Advanced DSA-C03 免費考試真題:

1. You're developing a fraud detection system in Snowflake. You're using Snowflake Cortex to generate embeddings from transaction descriptions, aiming to cluster similar fraudulent transactions. Which of the following approaches are MOST effective for optimizing the performance and cost of generating embeddings for a large dataset of millions of transaction descriptions using Snowflake Cortex, especially considering the potential cost implications of generating embeddings at scale? Select two options.

A) Generate embeddings on the entire dataset every day to capture all potential fraudulent transactions and ensure the model is always up-to-date.
B) Generate embeddings using snowflake-cortex-embed-text function, using the OPENAI embedding model
C) Create a materialized view containing pre-computed embeddings for all transaction descriptions.
D) Implement caching mechanism based on a hash of transaction description if transaction description does not change then no need to recompute the emebeddings again.
E) Use a Snowflake Task to incrementally generate embeddings only for new transactions that have been added since the last embedding generation run.

2. You've developed a binary classification model using Snowpark ML to predict customer subscription renewal (0 for churn, 1 for renew). You want to visualize feature importance using a permutation importance technique calculated within Snowflake. You perform feature permutation and calculate the decrease in model performance (e.g., AUC) after each permutation. Suppose the following query represents the results of this process:

The 'feature_importance_results' table contains the following data:

Based on this output, which of the following statements are the MOST accurate interpretations regarding feature impact and model behavior?

A) The 'support_calls' feature is the least important feature; removing it entirely from the model will have little impact on its AUC performance.
B) The 'contract_length' feature is the most important feature for the model's predictive performance; shuffling it causes the largest drop in AUC.
C) Permutation importance only reveals the importance of features within the current model. Different models trained with different features or algorithms might have different feature rankings.
D) The 'contract_length' and 'monthly_charges' features are equally important.
E) Increasing the 'contract_length' for customers will always lead to a higher probability of renewal. However, there could be correlation between contract length and monthly charges.

3. 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.

A) Using 'ALTER TABLE DROP COLUMN' to directly remove 'TIMESTAMP column, which is deemed irrelevant for the sentiment analysis model. SQL: 'ALTER TABLE PRODUCT REVIEWS DROP COLUMN TIMESTAMP;'
B) All of the above.
C) Creating a VIEW that only selects the 'REVIEW _ TEXT , 'REVIEW_ID', and 'RATING' columns, effectively hiding the irrelevant columns from the model. SQL: 'CREATE OR REPLACE VIEW REVIEWS FOR ANALYSIS AS SELECT REVIEW TEXT, REVIEW ID, RATING FROM PRODUCT REVIEWS;'
D) Dropping rows with 'NULL' values in REVIEW_TEXT and then dropping the 'PRODUCT_ID' and 'TIMESTAMP' columns using 'ALTER TABLE. SQL: 'CREATE OR REPLACE TABLE PRODUCT REVIEWS AS SELECT FROM PRODUCT REVIEWS WHERE REVIEW TEXT IS NOT NULL; ALTER TABLE PRODUCT REVIEWS DROP COLUMN PRODUCT ID; ALTER TABLE PRODUCT REVIEWS DROP COLUMN TIMESTAMP;'
E) creating a new table 'REVIEWS_CLEANED containing only the relevant columns CREVIEW_TEXT , 'REVIEW_ID' , and 'RATING') using 'CREATE TABLE AS SELECT. SQL: 'CREATE OR REPLACE TABLE REVIEWS CLEANED AS SELECT REVIEW TEXT, REVIEW ID, RATING FROM PRODUCT REVIEWS;'

4. You've built a regression model in Snowflake to predict customer churn. You've calculated the R-squared score on your test data and found it to be 0.65. However, after deploying the model to production and monitoring its performance over several weeks, you notice the model's predictive accuracy has significantly decreased. Which of the following factors could contribute to this performance degradation?
Select all that apply.

A) Feature engineering inconsistencies: The feature engineering steps applied to the production data are different from those applied during training.
B) Data drift: The distribution of the input features in the production data has changed significantly compared to the training data.
C) Increased data volume: The production data volume has increased significantly, causing resource contention and impacting model performance in Snowflake.
D) Overfitting: The model learned the training data too well, capturing noise and specific patterns that do not generalize to new data.
E) Bias Variance trade off : Model is having high bias.

5. You are deploying a machine learning model to Snowflake using a Python UDF. The model predicts customer churn based on a set of features. You need to handle missing values in the input data'. Which of the following methods is the MOST efficient and robust way to handle missing values within the UDF, assuming performance is critical and you don't want to modify the underlying data tables?

A) Use within the UDF to forward fill missing values. This assumes the data is ordered in a meaningful way, allowing for reasonable imputation.
B) Implement a custom imputation strategy using 'numpy.where' within the UDF, basing the imputation value on a weighted average of other features in the row.
C) Use within the UDF, replacing missing values with a global constant (e.g., 0) defined outside the UDF. This constant is pre-calculated based on the training dataset's missing value distribution.
D) Pre-process the data in Snowflake using SQL queries to replace missing values with the mean for numerical features and the mode for categorical features before calling the UDF.
E) Raise an exception within the UDF when a missing value is encountered, forcing the calling application to handle the missing values.

問題與答案：