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>> DSA-C03基礎問題集 <<

DSA-C03試験問題、DSA-C03関連試験

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Snowflake SnowPro Advanced: Data Scientist Certification Exam 認定 DSA-C03 試験問題 (Q240-Q245):

質問 # 240
You have developed a customer churn prediction model using Python and deployed it as a Snowflake UDE You are monitoring its performance and notice a significant drop in accuracy over time. To address this, you need to implement automated model retraining with regular validation. Which of the following steps and validation techniques are MOST critical for ensuring the retrained model is effective and avoids overfitting to recent data? (Select THREE)

A. Monitor the model's performance on a live dataset and trigger retraining only when the performance drops below a predefined threshold, using metrics like accuracy, precision, or recall. Save Model Performance to 'MODEL_PERFORMANCE.
B. Use cross-validation techniques (e.g., k-fold cross-validation) during the retraining process to estimate the model's performance on unseen data and prevent overfitting. Evaluate on a held-out validation set.
C. Retrain the model using the entire available dataset, as this will maximize the amount of data the model learns from.
D. Implement a data drift detection mechanism. Monitor the distribution of input features over time and trigger retraining if significant drift is detected using tools such as Snowflake's Anomaly Detection features or custom drift metrics calculated in SQL.
E. Update the UDF in place using 'CREATE OR REPLACE FUNCTION' immediately after retraining completes, regardless of the validation results.

正解：A、B、D

解説：
B, C, and D are the most critical steps. Option B is essential because data drift can significantly impact model performance. Detecting and addressing data drift is crucial for maintaining accuracy over time. Option C is vital for preventing overfitting and ensuring the model generalizes well to unseen data. Cross-validation provides a more robust estimate of model performance than a single train-test split. Option D is necessary to ensure that the retraining process is only triggered when the model's performance degrades. Monitoring live data and using performance metrics as triggers is a key component of automated retraining. Option A is incorrect because retraining on the entire dataset without validation can lead to overfitting. Option E is dangerous, as it deploys the retrained model without confirming its effectiveness.

質問 # 241
You are tasked with building a data pipeline using Snowpark Python to process customer feedback data stored in a Snowflake table called FEEDBACK DATA'. This table contains free-text feedback, and you need to clean and prepare this data for sentiment analysis. Specifically, you need to remove stop words, perform stemming, and handle missing values. Which of the following code snippets and strategies, potentially used in conjunction, provide the most effective and performant solution for this task within the Snowpark environment?

A. Load the FEEDBACK DATA' table into a Pandas DataFrame using perform stop word removal and stemming using libraries like spacy or NLTK, handle missing values using Pandas' 'fillna()' method. Then, convert the cleaned Pandas DataFrame back into a Snowpark DataFrame. Use vectorization of text column in dataframe after above step
B. Leverage Snowflake's built-in string functions within SQL to remove common stop words based on a predefined list. Use a Snowpark DataFrame to execute this SQL transformation. For stemming, research and deploy a Java UDF implementing stemming algorithms, then chain it within a Snowpark transformation pipeline. Replace missing values with the string 'N/A' during the DataFrame construction using 'na.fill('N/A')'.
C. Implement all data cleaning tasks within a single SQL stored procedure including removing stop words using REPLACE functions, stemming using a custom lookup table, and handling NULL values using COALESC Call this stored procedure from Snowpark for Python.
D. Use a Python UDF that utilizes the NLTK library to remove stop words and perform stemming on the feedback text. Handle missing values by replacing them with an empty string using the .fillna(")' method on the Snowpark DataFrame after applying the UDF.
E. Utilize Snowpark's 'call_function' with a Java UDF pre-loaded into Snowflake, which removes stop words and performs stemming with libraries like Lucene. Missing values can be handled with SQL's 'NVL' function during the initial data extraction into a Snowpark DataFrame.

正解：B、E

解説：
Options B and C provide the most effective and performant solutions.Option B leverages a combination of SQL and Java UDF to efficiently handle different parts of the cleaning process. The use of Snowflake's built-in string functions for removing stop words in SQL is efficient for common stop words, and Java UDF provides a more flexible and potentially more efficient solution for stemming. DataFrame .na.fill' is the most appropriate way to fill the missing values during the DataFrame creation. Option C: Utilizes pre-loaded Java UDFs for word processing, combined with SQL's NVL for missing value handling, is a strategy to leverage different components of Snowflake for performance and efficiency.Option A: While Python UDFs are flexible, they can be less performant than SQL or Java UDFs, especially for large datasets. Loading entire dataframe is an anti pattern. Also using .fillna on the dataframe instead of on the dataframe construction will reduce the performance. Option D: Loading all data into pandas is a bad habit and might reduce the performance. Also vectorization is not appropriate for cleaning the data. Option E: Stored procedures can be performant, relying solely on nested REPLACE functions for stop word removal can be cumbersome, and difficult to maintain compared to other approaches.

質問 # 242
A telecom company, 'ConnectPlus', observes that the individual call durations of its customers are heavily skewed towards shorter calls, following an exponential distribution. A data science team aims to analyze call patterns and requires to perform hypothesis testing on the average call duration. Which of the following statements regarding the applicability of the Central Limit Theorem (CLT) in this scenario are correct if the sample size is sufficiently large?

A. The CLT is applicable, and the sample mean will converge to the population median.
B. The CLT is applicable, and the distribution of sample means of call durations will approximate a normal distribution, regardless of the skewness of the individual call durations.
C. The CLT is applicable as long as the sample size is reasonably large (typically n > 30), and the distribution of sample means will be approximately normal. The specific minimum sample size depends on the severity of the skewness.
D. The CLT is applicable only if the sample size is extremely large (e.g., greater than 10,000), due to the exponential distribution's heavy tail.
E. The CLT is not applicable because the population distribution (call durations) is heavily skewed.

正解：B、C

解説：
The Central Limit Theorem (CLT) states that the distribution of sample means will be approximately normally distributed, regardless of the shape of the population distribution, as long as the sample size is large enough. While the rule of thumb is typically n > 30, the skewness of the original population distribution can influence how large the 'large enough' sample size needs to be. In this scenario, since the call durations follow an exponential distribution (which is skewed), a reasonably large sample size will still allow the CLT to be applicable, and the sample means' distribution will approach normality. The CLT ensures convergence toward normality in the distribution of sample means, not convergence of the sample mean to the population median.

質問 # 243
You are tasked with optimizing the hyperparameter tuning process for a complex deep learning model within Snowflake using Snowpark Python. The model is trained on a large dataset stored in Snowflake, and you need to efficiently explore a wide range of hyperparameter values to achieve optimal performance. Which of the following approaches would provide the MOST scalable and performant solution for hyperparameter tuning in this scenario, considering the constraints and capabilities of Snowflake?

A. Employing Scikit-learn's 'GridSearchCV' or 'RandomizedSearchCV' within a single Snowpark Python UDF, processing the entire dataset and hyperparameter search sequentially.
B. Using 'Hyperopt' within a Snowpark Python UDF, relying on its sequential optimization approach without any form of parallelization or distribution of the workload.
C. Using a simple 'for' loop to iterate through a predefined set of hyperparameter combinations, training the model for each combination within a Snowpark Python UDF, and storing the results in a Snowflake table.
D. Leveraging a distributed hyperparameter tuning framework like Ray Tune or Dask-ML within Snowpark Python, utilizing Snowflake's compute resources for parallel training and evaluation of different hyperparameter configurations.
E. Implementing a custom hyperparameter optimization algorithm using Snowpark Python UDFs, manually distributing the hyperparameter search across multiple Snowflake virtual warehouses, and aggregating the results in a central table.

正解：D

解説：
Option B is the most scalable and performant solution. Distributed hyperparameter tuning frameworks like Ray Tune or Dask-ML are designed to efficiently parallelize the hyperparameter search process across multiple compute resources. By integrating these frameworks with Snowpark Python, you can leverage Snowflake's scalable compute infrastructure to train and evaluate multiple hyperparameter configurations simultaneously, significantly reducing the overall tuning time. Option A is inefficient as it relies on a serial process. Option C is limited by the computational resources of a single Snowpark Python UDF. Option D is complex and requires manual management of distributed tasks, making it less efficient and scalable than using a dedicated framework. Option E is also limited by its sequential nature and does not take advantage of Snowflake's distributed computing capabilities.

質問 # 244
You are tasked with predicting sales (SALES AMOUNT') for a retail company using linear regression in Snowflake. The dataset includes features like 'ADVERTISING SPEND', 'PROMOTIONS', 'SEASONALITY INDEX', and 'COMPETITOR PRICE'. After training a linear regression model named 'sales model', you observe that the model performs poorly on new data, indicating potential issues with multicollinearity or overfitting. Which of the following strategies, applied directly within Snowflake, would be MOST effective in addressing these issues and improving the model's generalization performance? Choose ALL that apply.

A. Manually remove highly correlated features (e.g., if 'ADVERTISING SPEND and 'PROMOTIONS' have a correlation coefficient above 0.8) based on a correlation matrix calculated using 'CORR function and feature selection techniques.
B. Decrease the 'MAX_ITERATIONS' parameter in the 'CREATE MODEL' statement to prevent the model from overfitting to the training data.
C. Apply Ridge Regression by adding an L2 regularization term during model training. This can be achieved by setting the 'REGULARIZATION' parameter of the 'CREATE MODEL' statement to 'L2'.
D. Perform feature scaling (e.g., standardization or min-max scaling) on the input features before training the model, using Snowflake's built-in functions or user-defined functions (UDFs) for scaling.
E. Increase the size of the training dataset significantly by querying data from external sources.

正解：A、C、D

解説：
Options A, B, and D are the most effective strategies for addressing multicollinearity and overfitting in this scenario. Ridge Regression (A) adds an L2 regularization term, which penalizes large coefficients and reduces overfitting. Manually removing highly correlated features (B) addresses multicollinearity directly. Performing feature scaling (D) ensures that features with different scales do not disproportionately influence the model. Increasing training data (C) is generally helpful, but doesn't directly solve multicollinearity. Decreasing MAX ITERATIONS (E) might prevent the model from fully converging, but is a less targeted approach than regularization or feature selection.

質問 # 245
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