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You are training a Gradient Boosting model within Snowflake using Snowpark Python to predict customer churn. You are using the Hyperopt library for hyperparameter tuning. You want to use the function to find the best hyperparameters. You have defined your objective function, , and the search space, Which of the following is the MOST efficient and correct way to call the function within a Snowpark Python UDF to ensure the Hyperopt trials data is effectively managed and accessible for further analysis within Snowflake?
A. Option A
B. Option E
C. Option B
D. Option D
E. Option C
Answer: D
Explanation:
Option D is the most complete. It correctly uses 'Trials' to store results, ensures reproducibility with 'rstate' (important for controlled experiments), and demonstrates the correct way to save the trials to a Snowflake table using session.createDataFrame(trials.trials).write.save_as_table('HYPEROPT TRIALS')'. Option C also attempts to save results but saves 'trials.trials', not 'trials.results'. 'trials.trials' contains more detailed information for the hyperopt run. Reproducibility is also not ensured, which makes Option D slightly preferable. SparkTrials is only used for Spark not Snowflake, thus eliminating Option E. Option A does not store the output, and Option B saves 'trials.results' but lacks reproducibility and only processes 'trials.results'.
NEW QUESTION # 97
You are working on a customer churn prediction model and are using Snowpark Feature Store. One of your features, is updated daily. You notice that your model's performance degrades over time, likely due to stale feature values being used during inference. You want to ensure that the model always uses the most up-to-date feature values. Which of the following strategies would be the MOST effective way to address this issue using Snowpark Feature Store and avoid model staleness during online inference?
A. Define a custom User-Defined Function (UDF) in Snowflake that retrieves the 'customer_lifetime_value' from the Feature Store on demand whenever the model makes a prediction and set 'feature_retrieval_mode='fresh'S.
B. Configure the Feature Group containing to automatically refresh every hour using a scheduled Snowpark Python function.
C. Use the method on the Feature Store client during inference, ensuring that you always pass the current timestamp.
D. Configure with the attribute to manage data staleness and use the during inference, ensuring that the model always uses recent feature values.
E. Implement a real-time feature retrieval service that directly queries the underlying Snowflake table containing the using Snowpark, bypassing the Feature Store.
Answer: D
Explanation:
Option E is the most effective. Configuring the feature group with is important to reduce model staleness during online inference. Setting the in the configuration will serve as an indicator for staleness and use the method to retrieve the latest feature value available.
NEW QUESTION # 98
Which of the following statements are TRUE regarding the 'Data Understanding' and 'Data Preparation' steps within the Machine Learning lifecycle, specifically concerning handling data directly within Snowflake for a large, complex dataset?
A. Data Understanding primarily involves identifying potential data quality issues like missing values, outliers, and inconsistencies, and Snowflake features like 'QUALIFY and 'APPROX TOP can aid in this process.
B. Data Preparation should always be performed outside of Snowflake using external tools to avoid impacting Snowflake performance.
C. During Data Preparation, you should always prioritize creating a single, wide table containing all possible features to simplify the modeling process.
D. The 'Data Understanding' step is unnecessary when working with data stored in Snowflake because Snowflake automatically validates and cleans the data during ingestion.
E. Data Preparation in Snowflake can involve feature engineering using SQL functions, creating aggregated features with window functions, and handling missing values using 'NVL' or 'COALESCE. Furthermore, Snowpark Python provides richer data manipulation using DataFrame APIs directly on Snowflake data.
Answer: A,E
Explanation:
Data Understanding is crucial for identifying data quality issues using tools such as 'QUALIFY' and 'APPROX TOP Data Preparation within Snowflake using SQL and Snowpark Python enables efficient feature engineering and data cleaning. Option C is incorrect because Snowflake doesn't automatically validate and clean your data. Option D is incorrect as leveraging Snowflake's compute for data preparation alongside Snowpark can drastically increase speed. Option E is not desirable, feature selection is important, and feature stores help in organization.
NEW QUESTION # 99
A data scientist uses bootstrapping to estimate the sampling distribution of a statistic calculated from a dataset stored in Snowflake. They observe that the bootstrap distribution is significantly different from the original data distribution. Which of the following statements best describes the possible reasons for this difference, considering both the theoretical underpinnings of bootstrapping and potential limitations?
A. The original sample may not be representative of the population, and the bootstrap procedure is simply amplifying the biases present in the original sample. Additionally, the statistic itself may be highly sensitive to outliers or specific data points, leading to a distorted bootstrap distribution.
B. Bootstrapping always provides accurate estimates of sampling distributions, any significant difference indicates an error in the code implementation.
C. Bootstrapping is only appropriate for normally distributed data; if the original data is not normal, the bootstrap distribution will inevitably differ significantly.
D. The statistic being estimated is inherently unstable and has a high variance, causing the bootstrap distribution to be wider and potentially different in shape compared to the original data distribution. This is a normal outcome when dealing with such statistics.
E. The difference is unexpected; the bootstrap distribution should always closely resemble the original data distribution, regardless of the statistic being estimated.
Answer: A,D
Explanation:
Options B and C are correct. Bootstrapping relies on the assumption that the original sample is representative of the population. If it isn't, the bootstrap distribution will reflect the biases of the sample. Also certain statistics, particularly those sensitive to outliers or with high variance, can produce bootstrap distributions that differ significantly from the original data distribution. Option A is incorrect because the bootstrap distribution doesn't necessarily have to be same as sample distribution. Option D is incorrect since Bootstrapping makes no assumptions regarding the distribution of original dataset and can be used for any data distribution. Option E is not correct. Bootstrapping is not always accurate and relies on assumptions to perform correctly.
NEW QUESTION # 100
You are developing a Python UDTF in Snowflake to perform time series forecasting. You need to incorporate data from an external REST API as part of your feature engineering process within the UDTF. However, you are encountering intermittent network connectivity issues that cause the UDTF to fail. You want to implement a robust error handling mechanism to gracefully handle these network errors and ensure that the UDTF continues to function, albeit with potentially less accurate forecasts when external data is unavailable. Which of the following approaches is the MOST appropriate and effective for handling these network errors within your Python UDTF?
A. Configure Snowflake's network policies to allow outbound network access from the UDTF to the specific REST API endpoint. This will eliminate the network connectivity issues and prevent the UDTF from failing.
B. Before making the API call, check the network connectivity using the 'ping' command. If the ping fails, skip the API call and return a default forecast value. This prevents the UDTF from attempting to connect to an unavailable endpoint.
C. Use a combination of retry mechanisms (like the tenacity library) with exponential backoff around the API call. If the retry fails after a predefined number of attempts, then return pre-computed data or use a simplified model as the UDTF's output.
D. Use the 'try...except' block specifically around the code that makes the API call. Within the 'except block, catch specific network-related exceptions (e.g., requests.exceptions.RequestException', 'socket.timeout'). Log the error to a Snowflake stage using the 'logging' module and retry the API call a limited number of times with exponential backoff.
E. Implement a global exception handler within the UDTF that catches all exceptions, logs the error message to a Snowflake table, and returns a default forecast value when a network error occurs. Ensure the error logging table exists and has sufficient write permissions for the UDTF.
Answer: C,D
Explanation:
Options B and E are the MOST appropriate for handling network errors. Using a 'try...except block (B) specifically targets the API call and allows for handling network-related exceptions gracefully. Logging the error to a Snowflake stage provides valuable debugging information. Retry with exponential backoff increases the chances of success during transient network issues. Option E improves upon option B with external and maintained libraries such as tenacity and returning a model output, not just a single value, when the error is recoverable or the data is missing. Option A, a global exception handler, is too broad and might mask other errors. Option C is a necessary prerequisite but does not address intermittent connectivity issues. Option D's 'ping' command is not reliable for determining API availability and might introduce unnecessary delays or false negatives. A complete end-to-end, complete solution must focus on addressing all aspects of code and execution.
NEW QUESTION # 101
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