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MLS-C01テスト参考書、MLS-C01認定資格

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MLS-C01認定資格 & MLS-C01模擬モード

MLS-C01テストガイドは、時間の無駄を避けるために、できるだけ早くこれらの資料を学習できることを保証できます。 AWS Certified Machine Learning - Specialty Study Questionは、不明瞭な概念を簡素化することにより、学習方法を最適化するのに役立ちます。 MLS-C01試験問題は、アフターサービスを完璧にするための努力をspareしみません。

Amazon AWS Certified Machine Learning - Specialty 認定 MLS-C01 試験問題 (Q223-Q228):

質問 # 223
A company uses camera images of the tops of items displayed on store shelves to determine which items were removed and which ones still remain. After several hours of data labeling, the company has a total of
1,000 hand-labeled images covering 10 distinct items. The training results were poor.
Which machine learning approach fulfills the company's long-term needs?

A. Attach different colored labels to each item, take the images again, and build the model
B. Reduce the number of distinct items from 10 to 2, build the model, and iterate
C. Convert the images to grayscale and retrain the model
D. Augment training data for each item using image variants like inversions and translations, build the model, and iterate.

正解：D

解説：
Data augmentation is a technique that can increase the size and diversity of the training data by applying various transformations to the original images, such as inversions, translations, rotations, scaling, cropping, flipping, and color variations. Data augmentation can help improve the performance and generalization of image classification models by reducing overfitting and introducing more variability to the data. Data augmentation is especially useful when the original data is limited or imbalanced, as in the case of the company's problem. By augmenting the training data for each item using image variants, the company can build a more robust and accurate model that can recognize the items on the store shelves from different angles, positions, and lighting conditions. The company can also iterate on the model by adding more data or fine-tuning the hyperparameters to achieve better results.
References:
Build high performing image classification models using Amazon SageMaker JumpStart The Effectiveness of Data Augmentation in Image Classification using Deep Learning Data augmentation for improving deep learning in image classification problem Class-Adaptive Data Augmentation for Image Classification

質問 # 224
A Machine Learning team runs its own training algorithm on Amazon SageMaker. The training algorithm requires external assets. The team needs to submit both its own algorithm code and algorithm-specific parameters to Amazon SageMaker.
What combination of services should the team use to build a custom algorithm in Amazon SageMaker?
(Choose two.)

A. AWS CodeStar
B. Amazon S3
C. Amazon ECR
D. Amazon ECS
E. AWS Secrets Manager

正解：B、C

解説：
The Machine Learning team wants to use its own training algorithm on Amazon SageMaker, and submit both its own algorithm code and algorithm-specific parameters. The best combination of services to build a custom algorithm in Amazon SageMaker are Amazon ECR and Amazon S3.
Amazon ECR is a fully managed container registry service that allows you to store, manage, and deploy Docker container images. You can use Amazon ECR to create a Docker image that contains your training algorithm code and any dependencies or libraries that it requires. You can also use Amazon ECR to push, pull, and manage your Docker images securely and reliably.
Amazon S3 is a durable, scalable, and secure object storage service that can store any amount and type of data. You can use Amazon S3 to store your training data, model artifacts, and algorithm-specific parameters.
You can also use Amazon S3 to access your data and parameters from your training algorithm code, and to write your model output to a specified location.
Therefore, the Machine Learning team can use the following steps to build a custom algorithm in Amazon SageMaker:
Write the training algorithm code in Python, using the Amazon SageMaker Python SDK or the Amazon SageMaker Containers library to interact with the Amazon SageMaker service. The code should be able to read the input data and parameters from Amazon S3, and write the model output to Amazon S3.
Create a Dockerfile that defines the base image, the dependencies, the environment variables, and the commands to run the training algorithm code. The Dockerfile should also expose the ports that Amazon SageMaker uses to communicate with the container.
Build the Docker image using the Dockerfile, and tag it with a meaningful name and version.
Push the Docker image to Amazon ECR, and note the registry path of the image.
Upload the training data, model artifacts, and algorithm-specific parameters to Amazon S3, and note the S3 URIs of the objects.
Create an Amazon SageMaker training job, using the Amazon SageMaker Python SDK or the AWS CLI.
Specify the registry path of the Docker image, the S3 URIs of the input and output data, the algorithm- specific parameters, and other configuration options, such as the instance type, the number of instances, the IAM role, and the hyperparameters.
Monitor the status and logs of the training job, and retrieve the model output from Amazon S3.
Use Your Own Training Algorithms
Amazon ECR - Amazon Web Services
Amazon S3 - Amazon Web Services

質問 # 225
A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.

Based on this information, which model would have the HIGHEST accuracy?

A. Long short-term memory (LSTM) model with scaled exponential linear unit (SELU)
B. Support vector machine (SVM) with non-linear kernel
C. Logistic regression
D. Single perceptron with tanh activation function

正解：C

解説：
Explanation/Reference: https://machinelearningmastery.com/logistic-regression-for-machine-learning/

質問 # 226
A retail company is selling products through a global online marketplace. The company wants to use machine learning (ML) to analyze customer feedback and identify specific areas for improvement. A developer has built a tool that collects customer reviews from the online marketplace and stores them in an Amazon S3 bucket. This process yields a dataset of 40 reviews. A data scientist building the ML models must identify additional sources of data to increase the size of the dataset.
Which data sources should the data scientist use to augment the dataset of reviews? (Choose three.)

A. A publicly available collection of customer reviews
B. Product sales revenue figures for the company
C. A publicly available collection of news articles
D. Social media posts containing the name of the company or its products
E. Instruction manuals for the company's products
F. Emails exchanged by customers and the company's customer service agents

正解：A、D、F

解説：
The data sources that the data scientist should use to augment the dataset of reviews are those that contain relevant and diverse customer feedback about the company or its products. Emails exchanged by customers and the company's customer service agents can provide valuable insights into the issues and complaints that customers have, as well as the solutions and responses that the company offers. Social media posts containing the name of the company or its products can capture the opinions and sentiments of customers and potential customers, as well as their reactions to marketing campaigns and product launches. A publicly available collection of customer reviews can provide a large and varied sample of feedback from different online platforms and marketplaces, which can help to generalize the ML models and avoid bias.
References:
Detect sentiment from customer reviews using Amazon Comprehend | AWS Machine Learning Blog How to Apply Machine Learning to Customer Feedback

質問 # 227
An ecommerce company sends a weekly email newsletter to all of its customers. Management has hired a team of writers to create additional targeted content. A data scientist needs to identify five customer segments based on age, income, and location. The customers' current segmentation is unknown. The data scientist previously built an XGBoost model to predict the likelihood of a customer responding to an email based on age, income, and location.
Why does the XGBoost model NOT meet the current requirements, and how can this be fixed?

A. The XGBoost model is a supervised machine learning algorithm. Train a k-Nearest-Neighbors (kNN) model with K = 5 on the same dataset to predict a segment.
B. The XGBoost model provides a true/false binary output. Apply principal component analysis (PCA) with five feature dimensions to predict a segment.
C. The XGBoost model is a supervised machine learning algorithm. Train a k-means model with K = 5 on the same dataset to predict a segment.
D. The XGBoost model provides a true/false binary output. Increase the number of classes the XGBoost model predicts to five classes to predict a segment.

正解：C

質問 # 228
......

もちろん、資格試験を審査するとき、非公開にすることはできません。テストMLS-C01認定に関連する新しいポリシーと情報に注意する必要があります。ユーザーの便宜を図るため、ホームページでMLS-C01テスト資料を更新し、資格試験に関連する情報をタイムリーに更新します。年次認定試験は、内容はほぼ同じですが、各年のポリシーとして、対応する試験パターンのグレーディング基準とホットスポットが変更されます。MLS-C01テスト準備は、ユーザーが最短時間で合格するのに役立ちます。試験。

MLS-C01認定資格: https://www.jptestking.com/MLS-C01-exam.html

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MLS-C01テスト参考書 & 資格試験のリーダープロバイダー　& MLS-C01認定資格

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MLS-C01試験の準備の質を実践し、経験すると、それらの保守性と有用性を思い出すでしょう。