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最新的 SnowPro Advanced DSA-C03 免費考試真題:

1. You are tasked with automating the retraining of a fraud detection model in Snowflake. The model is deployed as a Snowflake User-Defined Function (UDF). The training data resides in a Snowflake table named 'TRANSACTIONS. You want to trigger retraining if the model's performance, as measured by AUC, drops below 0.80. The model's AUC is tracked in a Snowflake table named 'MODEL PERFORMANCE. Which of the following strategies provides the MOST efficient and robust solution for automating this retraining process within Snowflake, minimizing latency and external dependencies?

A) Create an external function that is invoked periodically by a Snowflake Task. The external function queries 'MODEL PERFORMANCE and uses a cloud provider's machine learning service (e.g., AWS SageMaker) to retrain the model and update the UDF using Snowflake's external functions capabilities for model deployment.
B) Schedule a job on an external system (e.g., a cron job on a Linux server) to periodically query 'MODEL PERFORMANCE and trigger a model retraining process if the AUC is below 0.80. This process would retrain the model externally and update the UDF in Snowflake.
C) Implement a Snowflake Task that executes a stored procedure. The stored procedure queries 'MODEL_PERFORMANCE. If the AUC is below 0.80, it executes a Snowflake ML pipeline using 'snowflake.ml.modeling' to retrain the model directly within Snowflake and updates the UDF in place using 'CREATE OR REPLACE FUNCTION'.
D) Use a Snowflake Task that executes a stored procedure. The stored procedure queries 'MODEL PERFORMANCE, and if the AUC is below 0.80, it triggers a Data Engineering pipeline (e.g., using Airflow or Databricks) to retrain the model and update the UDF.
E) Manually monitor on a dashboard and trigger retraining via a Snowflake Worksheet when needed.

2. A marketing analyst is building a propensity model to predict customer response to a new product launch. The dataset contains a 'City' column with a large number of unique city names. Applying one-hot encoding to this feature would result in a very high-dimensional dataset, potentially leading to the curse of dimensionality. To mitigate this, the analyst decides to combine Label Encoding followed by binarization techniques. Which of the following statements are TRUE regarding the benefits and challenges of this combined approach in Snowflake compared to simply label encoding?

A) While label encoding itself adds an ordinal relationship, applying binarization techniques like binary encoding (converting the label to binary representation and splitting into multiple columns) after label encoding will remove the arbitrary ordinal relationship.
B) Label encoding introduces an arbitrary ordinal relationship between the cities, which may not be appropriate. Binarization alone cannot remove this artifact.
C) Binarization following label encoding may enhance model performance if a specific split based on a defined threshold is meaningful for the target variable (e.g., distinguishing between cities above/below a certain average income level related to marketing success).
D) Label encoding followed by binarization will reduce the memory required to store the 'City' feature compared to one-hot encoding, and Snowflake's columnar storage optimizes storage for integer data types used in label encoding.
E) Binarizing a label encoded column using a simple threshold (e.g., creating a 'high_city_id' flag) addresses the curse of dimensionality by reducing the number of features to one, but it loses significant information about the individual cities.

3. You are developing a regression model in Snowflake to predict housing prices. You've trained a model using Snowflake ML functions and now need to rigorously validate its performance. You have a separate validation dataset stored in a table named 'HOUSING VALIDATION'. Which of the following SQL statements, when executed in Snowflake, would accurately calculate the Root Mean Squared Error (RMSE) of your model's predictions against the actual prices in the validation dataset, assuming your model is named 'HOUSING PRICE MODEL' and the prediction function generated by CREATE SNOWFLAKE.ML.FORECAST is called PREDICT?

A) Option E
B) Option B
C) Option C
D) Option A
E) Option D

4. You've trained a machine learning model using Scikit-learn and saved it as 'model.joblib'. You need to deploy this model to Snowflake. Which sequence of commands will correctly stage the model and create a Snowflake external function to use it for inference, assuming you already have a Snowflake stage named 'model_stage'?

A) Option E
B) Option B
C) Option C
D) Option A
E) Option D

5. You are working with a dataset of customer transaction logs stored in Snowflake. Due to legal restrictions, you are unable to directly access or analyze the entire dataset. However, you can query aggregate statistics. You need to estimate the standard error of the mean transaction amount using bootstrapping. Knowing that you cannot retrieve the individual transaction amounts directly, which of the following approaches, while technically feasible within Snowflake and its stored procedure capabilities, is the least appropriate and potentially misleading application of bootstrapping?

A) Even without individual transaction data, bootstrapping is fundamentally impossible in this scenario, as bootstrapping requires resampling from the original data . All given options are therefore equally inappropriate.
B) Develop a stored procedure that generates random samples from a normal distribution with the same mean and standard deviation as the aggregated transaction data available to you, then calculates the standard error of the mean from these synthetic resamples.
C) Use the available aggregate statistics to create many synthetic datasets, all adhering to the same mean, variance, and total sample size. Then, compute the statistic of interest (mean transaction amount) for each of these synthetic datasets, and use this collection to estimate the standard error. This is a valid approach.
D) Construct a stored procedure that uses the available aggregated statistics (e.g., mean, standard deviation, and sample size) to generate bootstrap samples based on an assumed parametric distribution (e.g., gamma or log-normal) fitted to the data, and then estimate the standard error from these resamples.
E) Attempt to apply the central limit theorem rather than bootstrapping.

問題與答案：