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The AWS Certified Machine Learning - Specialty certification exam covers a wide range of topics related to machine learning, including data preparation, feature engineering, modeling, and optimization. It also includes topics such as deep learning, natural language processing, and computer vision. MLS-C01 exam is designed to test the candidate's ability to design, implement, deploy, and maintain machine learning solutions on the AWS platform.

To be eligible for the Amazon MLS-C01 Certification Exam, candidates must have a minimum of one year of experience in designing and implementing machine learning solutions using AWS services. They should also have experience in data pre-processing, feature engineering, model selection, and model evaluation. Additionally, candidates should have knowledge of programming languages such as Python, R, and Java.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q266-Q271):

NEW QUESTION # 266
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 dropout
B. Choose a lower number of layers
C. Choose a smaller learning rate
D. Include all the images from the test set in the training set
E. Choose a higher number of layers
F. Enable early stopping

Answer: A,B,F

Explanation:
The problem described in the question is a case of overfitting, where the neural network model performs well on the training data but poorly on the test data. This means that the model has learned the noise and specific patterns of the training data, but cannot generalize to new and unseen data. To solve this issue, the Specialist should consider the following changes:
* Choose a lower number of layers: Reducing the number of layers can reduce the complexity and capacity of the neural network model, making it less prone to overfitting. A model with 50 hidden layers is likely too deep for the given data size and task. A simpler model with fewer layers can learn the essential features of the data without memorizing the noise.
* Enable dropout: Dropout is a regularization technique that randomly drops out some units in the neural network during training. This prevents the units from co-adapting too much and forces the model to learn more robust features. Dropout can improve the generalization and test performance of the model by reducing overfitting.
* Enable early stopping: Early stopping is another regularization technique that monitors the validation error during training and stops the training process when the validation error stops decreasing or starts increasing. This prevents the model from overtraining on the training data and reduces overfitting.
Deep Learning - Machine Learning Lens
How to Avoid Overfitting in Deep Learning Neural Networks
How to Identify Overfitting Machine Learning Models in Scikit-Learn

NEW QUESTION # 267
A Machine Learning Specialist is working with a media company to perform classification on popular articles from the company's website. The company is using random forests to classify how popular an article will be before it is published A sample of the data being used is below.
Given the dataset, the Specialist wants to convert the Day-Of_Week column to binary values.
What technique should be used to convert this column to binary values.

A. One-hot encoding
B. Normalization transformation
C. Tokenization
D. Binarization

Answer: A

NEW QUESTION # 268
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. Correlation plot with heat maps
B. Univariate selection
C. Feature importance with a tree-based classifier
D. Data binning
E. Data scaling with standardization and normalization
F. Data augmentation

Answer: A,B,C

Explanation:
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.
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

NEW QUESTION # 269
An online delivery company wants to choose the fastest courier for each delivery at the moment an order is placed. The company wants to implement this feature for existing users and new users of its application. Data scientists have trained separate models with XGBoost for this purpose, and the models are stored in Amazon S3. There is one model fof each city where the company operates.
The engineers are hosting these models in Amazon EC2 for responding to the web client requests, with one instance for each model, but the instances have only a 5% utilization in CPU and memory, ....operation engineers want to avoid managing unnecessary resources.
Which solution will enable the company to achieve its goal with the LEAST operational overhead?

A. Prepare an Amazon SageMaker Docker container based on the open-source multi-model server.
Remove the existing instances and create a multi-model endpoint in SageMaker instead, pointing to the S3 bucket containing all the models Invoke the endpoint from the web client at runtime, specifying the TargetModel parameter according to the city of each request.
B. Create an Amazon SageMaker notebook instance for pulling all the models from Amazon S3 using the boto3 library. Remove the existing instances and use the notebook to perform a SageMaker batch transform for performing inferences offline for all the possible users in all the cities. Store the results in different files in Amazon S3. Point the web client to the files.
C. Prepare a Docker container based on the prebuilt images in Amazon SageMaker. Replace the existing instances with separate SageMaker endpoints. one for each city where the company operates. Invoke the endpoints from the web client, specifying the URL and EndpomtName parameter according to the city of each request.
D. Keep only a single EC2 instance for hosting all the models. Install a model server in the instance and load each model by pulling it from Amazon S3. Integrate the instance with the web client using Amazon API Gateway for responding to the requests in real time, specifying the target resource according to the city of each request.

Answer: A

Explanation:
The best solution for this scenario is to use a multi-model endpoint in Amazon SageMaker, which allows hosting multiple models on the same endpoint and invoking them dynamically at runtime. This way, the company can reduce the operational overhead of managing multiple EC2 instances and model servers, and leverage the scalability, security, and performance of SageMaker hosting services. By using a multi-model endpoint, the company can also save on hosting costs by improving endpoint utilization and paying only for the models that are loaded in memory and the API calls that are made. To use a multi-model endpoint, the company needs to prepare a Docker container based on the open-source multi-model server, which is a framework-agnostic library that supports loading and serving multiple models from Amazon S3. The company can then create a multi-model endpoint in SageMaker, pointing to the S3 bucket containing all the models, and invoke the endpoint from the web client at runtime, specifying the TargetModel parameter according to the city of each request. This solution also enables the company to add or remove models from the S3 bucket without redeploying the endpoint, and to use different versions of the same model for different cities if needed. References:
* Use Docker containers to build models
* Host multiple models in one container behind one endpoint
* Multi-model endpoints using Scikit Learn
* Multi-model endpoints using XGBoost

NEW QUESTION # 270
A tourism company uses a machine learning (ML) model to make recommendations to customers. The company uses an Amazon SageMaker environment and set hyperparameter tuning completion criteria to MaxNumberOfTrainingJobs.
An ML specialist wants to change the hyperparameter tuning completion criteria. The ML specialist wants to stop tuning immediately after an internal algorithm determines that tuning job is unlikely to improve more than 1% over the objective metric from the best training job.
Which completion criteria will meet this requirement?

A. TargetObjectiveMetricValue
B. CompleteOnConvergence
C. MaxRuntimelnSeconds
D. MaxNumberOfTrainingJobsNotlmproving

Answer: B

Explanation:
In Amazon SageMaker, hyperparameter tuning jobs optimize model performance by adjusting hyperparameters. Amazon SageMaker's hyperparameter tuning supports completion criteria settings that enable efficient management of tuning resources. In this scenario, the ML specialist aims to set a completion criterion that will terminate the tuning job as soon as SageMaker detects that further improvements in the objective metric are unlikely to exceed 1%.
The CompleteOnConvergence setting is designed for such requirements. This criterion enables the tuning job to automatically stop when SageMaker determines that additional hyperparameter evaluations are unlikely to improve the objective metric beyond a certain threshold, allowing for efficient tuning completion. The convergence process relies on an internal optimization algorithm that continuously evaluates the objective metric during tuning and stops when performance stabilizes without further improvement.
This is supported by AWS documentation, which explains that CompleteOnConvergence is an efficient way to manage tuning by stopping unnecessary evaluations once the model performance stabilizes within the specified threshold.

NEW QUESTION # 271
......

In today's competitive Amazon industry, only the brightest and most qualified candidates are hired for high-paying positions. Obtaining MLS-C01 certification is a wonderful approach to be successful because it can draw in prospects and convince companies that you are the finest in your field. Pass the AWS Certified Machine Learning - Specialty to establish your expertise in your field and receive certification. However, passing the AWS Certified Machine Learning - Specialty MLS-C01 Exam is challenging.

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