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MLS-C01試験問題集、MLS-C01関連試験

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Amazon AWS Certified Machine Learning - Specialty 認定 MLS-C01 試験問題 (Q256-Q261):

質問 # 256
A company that manufactures mobile devices wants to determine and calibrate the appropriate sales price for its devices. The company is collecting the relevant data and is determining data features that it can use to train machine learning (ML) models. There are more than 1,000 features, and the company wants to determine the primary features that contribute to the sales price.
Which techniques should the company use for feature selection? (Choose three.)

• A. Univariate selection
• B. Feature importance with a tree-based classifier
• C. Data augmentation
• D. Data scaling with standardization and normalization
• E. Correlation plot with heat maps
• F. Data binning

正解：A、B、E

解説：
Feature selection is the process of selecting a subset of extracted features that are relevant and contribute to minimizing the error rate of a trained model. Some techniques for feature selection are:
Correlation plot with heat maps: This technique visualizes the correlation between features using a color-coded matrix. Features that are highly correlated with each other or with the target variable can be identified and removed to reduce redundancy and noise.
Univariate selection: This technique evaluates each feature individually based on a statistical test, such as chi-square, ANOVA, or mutual information, and selects the features that have the highest scores or p-values. This technique is simple and fast, but it does not consider the interactions between features.
Feature importance with a tree-based classifier: This technique uses a tree-based classifier, such as random forest or gradient boosting, to rank the features based on their importance in splitting the nodes. Features that have low importance scores can be dropped from the model. This technique can capture the non-linear relationships and interactions between features.
The other options are not techniques for feature selection, but rather for feature engineering, which is the process of creating, transforming, or extracting features from the original data. Feature engineering can improve the performance and interpretability of the model, but it does not reduce the number of features.
Data scaling with standardization and normalization: This technique transforms the features to have a common scale, such as zero mean and unit variance, or a range between 0 and 1. This technique can help some algorithms, such as k-means or logistic regression, to converge faster and avoid numerical instability, but it does not change the number of features.
Data binning: This technique groups the continuous features into discrete bins or categories based on some criteria, such as equal width, equal frequency, or clustering. This technique can reduce the noise and outliers in the data, and also create ordinal or nominal features that can be used for some algorithms, such as decision trees or naive Bayes, but it does not reduce the number of features.
Data augmentation: This technique generates new data from the existing data by applying some transformations, such as rotation, flipping, cropping, or noise addition. This technique can increase the size and diversity of the data, and help prevent overfitting, but it does not reduce the number of features.
References:
Feature engineering - Machine Learning Lens
Amazon SageMaker Autopilot now provides feature selection and the ability to change data types while creating an AutoML experiment Feature Selection in Machine Learning | Baeldung on Computer Science Feature Selection in Machine Learning: An easy Introduction

 

質問 # 257
A Machine Learning Specialist is building a convolutional neural network (CNN) that will classify 10 types of animals. The Specialist has built a series of layers in a neural network that will take an input image of an animal, pass it through a series of convolutional and pooling layers, and then finally pass it through a dense and fully connected layer with 10 nodes The Specialist would like to get an output from the neural network that is a probability distribution of how likely it is that the input image belongs to each of the 10 classes Which function will produce the desired output?

• A. Rectified linear units (ReLU)
• B. Softmax
• C. Dropout
• D. Smooth L1 loss

正解：A

 

質問 # 258
A media company wants to deploy a machine learning (ML) model that uses Amazon SageMaker to recommend new articles to the company's readers. The company's readers are primarily located in a single city.
The company notices that the heaviest reader traffic predictably occurs early in the morning, after lunch, and again after work hours. There is very little traffic at other times of day. The media company needs to minimize the time required to deliver recommendations to its readers. The expected amount of data that the API call will return for inference is less than 4 MB.
Which solution will meet these requirements in the MOST cost-effective way?

• A. Serverless inference with provisioned concurrency
• B. A batch transform task
• C. Asynchronous inference
• D. Real-time inference with auto scaling

正解：A

解説：
Serverless inference in SageMaker is designed for workloads with intermittent traffic and unpredictable usage patterns, which aligns with the media company's periodic high-traffic windows. Because the payload is less than 4 MB (serverless inference supports payloads up to 4 MB), serverless inference is appropriate and provisioned concurrency ensures the endpoints are warm and ready during peak times to minimize latency.
From AWS documentation:
"Amazon SageMaker Serverless Inference is ideal for applications with intermittent or unpredictable traffic.
You can optionally enable provisioned concurrency to ensure that your endpoints are always ready to process requests during anticipated peak traffic hours."
- AWS SageMaker Serverless Inference documentation
Using real-time inference with auto scaling (A) incurs a higher cost as it requires always-on instances. Batch transform (D) is for offline, large-scale inferences, not low-latency real-time inference. Asynchronous inference (C) is typically used for large payloads (>4 MB) or long processing times.

 

質問 # 259
A health care company is planning to use neural networks to classify their X-ray images into normal and abnormal classes. The labeled data is divided into a training set of 1,000 images and a test set of 200 images.
The initial training of a neural network model with 50 hidden layers yielded 99% accuracy on the training set, but only 55% accuracy on the test set.
What changes should the Specialist consider to solve this issue? (Choose three.)

• A. Enable early stopping
• B. Include all the images from the test set in the training set
• C. Enable dropout
• D. Choose a smaller learning rate
• E. Choose a higher number of layers
• F. Choose a lower number of layers

正解：B、C、E

 

質問 # 260
A machine learning engineer is building a bird classification model. The engineer randomly separates a dataset into a training dataset and a validation dataset. During the training phase, the model achieves very high accuracy. However, the model did not generalize well during validation of the validation dataset. The engineer realizes that the original dataset was imbalanced.
What should the engineer do to improve the validation accuracy of the model?

• A. Use a smaller, randomly sampled version of the training dataset.
• B. Acquire additional data about the majority classes in the original dataset.
• C. Perform stratified sampling on the original dataset.
• D. Perform systematic sampling on the original dataset.

正解：C

解説：
Stratified sampling is a technique that preserves the class distribution of the original dataset when creating a smaller or split dataset. This means that the proportion of examples from each class in the original dataset is maintained in the smaller or split dataset. Stratified sampling can help improve the validation accuracy of the model by ensuring that the validation dataset is representative of the original dataset and not biased towards any class. This can reduce the variance and overfitting of the model and increase its generalization ability.
Stratified sampling can be applied to both oversampling and undersampling methods, depending on whether the goal is to increase or decrease the size of the dataset.
The other options are not effective ways to improve the validation accuracy of the model. Acquiring additional data about the majority classes in the original dataset will only increase the imbalance and make the model more biased towards the majority classes. Using a smaller, randomly sampled version of the training dataset will not guarantee that the class distribution is preserved and may result in losing important information from the minority classes. Performing systematic sampling on the original dataset will also not ensure that the class distribution is preserved and may introduce sampling bias if the original dataset is ordered or grouped by class.
References:
*Stratified Sampling for Imbalanced Datasets
*Imbalanced Data
*Tour of Data Sampling Methods for Imbalanced Classification

 

質問 # 261
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