真実的な-権威のあるMLS-C01日本語版問題解説試験-試験の準備方法MLS-C01試験攻略

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>> MLS-C01日本語版問題解説 <<

MLS-C01試験攻略 & MLS-C01テスト模擬問題集

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Amazon AWS Certified Machine Learning - Specialty 認定 MLS-C01 試験問題 (Q317-Q322):

質問 # 317
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. Dropout
C. Softmax
D. Smooth L1 loss

正解：C

解説：
The softmax function is a function that can transform a vector of arbitrary real values into a vector of real values in the range (0,1) that sum to 1. This means that the softmax function can produce a valid probability distribution over multiple classes. The softmax function is often used as the activation function of the output layer in a neural network, especially for multi-class classification problems. The softmax function can assign higher probabilities to the classes with higher scores, which allows the network to make predictions based on the most likely class. In this case, the Machine Learning Specialist wants 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 of animals. Therefore, the softmax function is the most suitable function to produce the desired output.
References:
Softmax Activation Function for Deep Learning: A Complete Guide
What is Softmax in Machine Learning? - reason.town
machine learning - Why is the softmax function often used as activation ...
Multi-Class Neural Networks: Softmax | Machine Learning | Google for ...

質問 # 318
A Machine Learning Specialist is developing recommendation engine for a photography blog Given a picture, the recommendation engine should show a picture that captures similar objects The Specialist would like to create a numerical representation feature to perform nearest-neighbor searches What actions would allow the Specialist to get relevant numerical representations?

A. Use Amazon Mechanical Turk to label image content and create a one-hot representation indicating the presence of specific labels
B. Run images through a neural network pie-trained on ImageNet, and collect the feature vectors from the penultimate layer
C. Average colors by channel to obtain three-dimensional representations of images.
D. Reduce image resolution and use reduced resolution pixel values as features

正解：B

解説：
Explanation
A neural network pre-trained on ImageNet is a deep learning model that has been trained on a large dataset of images containing 1000 classes of objects. The model can learn to extract high-level features from the images that capture the semantic and visual information of the objects. The penultimate layer of the model is the layer before the final output layer, and it contains a feature vector that represents the input image in a lower-dimensional space. By running images through a pre-trained neural network and collecting the feature vectors from the penultimate layer, the Specialist can obtain relevant numerical representations that can be used for nearest-neighbor searches. The feature vectors can capture the similarity between images based on the presence and appearance of similar objects, and they can be compared using distance metrics such as Euclidean distance or cosine similarity. This approach can enable the recommendation engine to show a picture that captures similar objects to a given picture.
References:
ImageNet - Wikipedia
How to use a pre-trained neural network to extract features from images | by Rishabh Anand | Analytics Vidhya | Medium Image Similarity using Deep Ranking | by Aditya Oke | Towards Data Science

質問 # 319
A financial services company wants to automate its loan approval process by building a machine learning (ML) model. Each loan data point contains credit history from a third-party data source and demographic information about the customer. Each loan approval prediction must come with a report that contains an explanation for why the customer was approved for a loan or was denied for a loan. The company will use Amazon SageMaker to build the model.
Which solution will meet these requirements with the LEAST development effort?

A. Use AWS Lambda to provide feature importance and partial dependence plots. Use the plots to generate and attach the explanation report.
B. Use SageMaker Clarify to generate the explanation report. Attach the report to the predicted results.
C. Use SageMaker Model Debugger to automatically debug the predictions, generate the explanation, and attach the explanation report.
D. Use custom Amazon Cloud Watch metrics to generate the explanation report. Attach the report to the predicted results.

正解：B

解説：
The best solution for this scenario is to use SageMaker Clarify to generate the explanation report and attach it to the predicted results. SageMaker Clarify provides tools to help explain how machine learning (ML) models make predictions using a model-agnostic feature attribution approach based on SHAP values. It can also detect and measure potential bias in the data and the model. SageMaker Clarify can generate explanation reports during data preparation, model training, and model deployment. The reports include metrics, graphs, and examples that help understand the model behavior and predictions. The reports can be attached to the predicted results using the SageMaker SDK or the SageMaker API.
The other solutions are less optimal because they require more development effort and additional services. Using SageMaker Model Debugger would require modifying the training script to save the model output tensors and writing custom rules to debug and explain the predictions. Using AWS Lambda would require writing code to invoke the ML model, compute the feature importance and partial dependence plots, and generate and attach the explanation report. Using custom Amazon CloudWatch metrics would require writing code to publish the metrics, create dashboards, and generate and attach the explanation report.
References:
Bias Detection and Model Explainability - Amazon SageMaker Clarify - AWS Amazon SageMaker Clarify Model Explainability Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability GitHub - aws/amazon-sagemaker-clarify: Fairness Aware Machine Learning

質問 # 320
A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset Which tool should be used to improve the validation accuracy?

A. Natural Language Toolkit (NLTK) stemming and stop word removal
B. Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers
C. Amazon SageMaker BlazingText allow mode
D. Amazon Comprehend syntax analysts and entity detection

正解：B

質問 # 321
A retail company wants to update its customer support system. The company wants to implement automatic routing of customer claims to different queues to prioritize the claims by category.
Currently, an operator manually performs the category assignment and routing. After the operator classifies and routes the claim, the company stores the claim's record in a central database. The claim's record includes the claim's category.
The company has no data science team or experience in the field of machine learning (ML). The company's small development team needs a solution that requires no ML expertise.
Which solution meets these requirements?

A. Export the database to a .csv file with one column: claim_text. Use the Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm and the .csv file to train a model. Use the LDA algorithm to detect labels automatically. Use SageMaker to deploy the model to an inference endpoint. Develop a service in the application to use the inference endpoint to process incoming claims, predict the labels, and route the claims to the appropriate queue.
B. Export the database to a .csv file with two columns: claim_label and claim_text. Use Amazon Comprehend custom classification and the .csv file to train the custom classifier. Develop a service in the application to use the Amazon Comprehend API to process incoming claims, predict the labels, and route the claims to the appropriate queue.
C. Export the database to a .csv file with two columns: claim_label and claim_text. Use the Amazon SageMaker Object2Vec algorithm and the .csv file to train a model. Use SageMaker to deploy the model to an inference endpoint. Develop a service in the application to use the inference endpoint to process incoming claims, predict the labels, and route the claims to the appropriate queue.
D. Use Amazon Textract to process the database and automatically detect two columns: claim_label and claim_text. Use Amazon Comprehend custom classification and the extracted information to train the custom classifier. Develop a service in the application to use the Amazon Comprehend API to process incoming claims, predict the labels, and route the claims to the appropriate queue.

正解：B

解説：
Amazon Comprehend is a natural language processing (NLP) service that can analyze text and extract insights such as sentiment, entities, topics, and language. Amazon Comprehend also provides custom classification and custom entity recognition features that allow users to train their own models using their own data and labels. For the scenario of routing customer claims to different queues based on categories, Amazon Comprehend custom classification is a suitable solution. The custom classifier can be trained using a .csv file that contains the claim text and the claim label as columns. The custom classifier can then be used to process incoming claims and predict the labels using the Amazon Comprehend API. The predicted labels can be used to route the claims to the appropriate queue. This solution does not require any machine learning expertise or model deployment, and it can be easily integrated with the existing application.
The other options are not suitable because:
* Option A: Amazon SageMaker Object2Vec is an algorithm that can learn embeddings of objects such as words, sentences, or documents. It can be used for tasks such as text classification, sentiment analysis, or recommendation systems. However, using this algorithm requires machine learning expertise and model deployment using SageMaker, which are not available for the company.
* Option B: Amazon SageMaker Latent Dirichlet Allocation (LDA) is an algorithm that can discover the topics or themes in a collection of documents. It can be used for tasks such as topic modeling, document clustering, or text summarization. However, using this algorithm requires machine learning expertise and model deployment using SageMaker, which are not available for the company. Moreover, LDA does not provide labels for the topics, but rather a distribution of words for each topic, which may not match the existing categories of the claims.
* Option C: Amazon Textract is a service that can extract text and data from scanned documents or images. It can be used for tasks such as document analysis, data extraction, or form processing.
However, using this service is unnecessary and inefficient for the scenario, since the company already has the claim text and label in a database. Moreover, Amazon Textract does not provide custom classification features, so it cannot be used to train a custom classifier using the existing data and labels.
References:
* Amazon Comprehend Custom Classification
* Amazon SageMaker Object2Vec
* Amazon SageMaker Latent Dirichlet Allocation
* Amazon Textract

質問 # 322
......

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けれど、男女のように、もっと二人の仲が今以上に深まるのではないかと思ったから、読んでくださってありがとうございました、時代の変遷とともに、業界の競争がますます激しくなりますから、IT業界での人たちはMLS-C01認定試験に参加する必要があります。

ハイパスレート-有効的なMLS-C01日本語版問題解説試験-試験の準備方法MLS-C01試験攻略

あなたはほかの資料を購入する必要はありません、MLS-C01試験問題集の迅速で安全な支払い、弊社の専門家たちのMLS-C01問題集（AWS Certified Machine Learning - Specialty）への研究は試験の高効率に保障があります、私たちの学習資料の価格は、他の販売者と比較して最も合理です。

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真実的な-権威のあるMLS-C01日本語版問題解説試験-試験の準備方法MLS-C01試験攻略

MLS-C01試験攻略 & MLS-C01テスト模擬問題集

Amazon AWS Certified Machine Learning - Specialty 認定 MLS-C01 試験問題 (Q317-Q322):

ハイパスレート-有効的なMLS-C01日本語版問題解説試験-試験の準備方法MLS-C01試験攻略

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