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It is similar to the AWS-Certified-Machine-Learning-Specialty desktop-based software, with all the elements of the desktop practice exam. This mock exam can be accessed from any browser and does not require installation. The AWS Certified Machine Learning - Specialty (AWS-Certified-Machine-Learning-Specialty) questions in the mock test are the same as those in the real exam. And candidates will be able to take the web-based AWS Certified Machine Learning - Specialty (AWS-Certified-Machine-Learning-Specialty) practice test immediately through any operating system and browsers.

Achieving the AWS Certified Machine Learning - Specialty certification through the Amazon MLS-C01 exam can demonstrate to employers and clients that you have the skills and knowledge needed to design and implement machine learning solutions on AWS. AWS Certified Machine Learning - Specialty certification can help individuals advance their careers as data scientists, machine learning engineers, and solution architects.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q223-Q228):

NEW QUESTION # 223
A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences and trends to enhance the website for better service and smart recommendations.
Which solution should the Specialist recommend?

• A. A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database
• B. Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database
• C. Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.
• D. Collaborative filtering based on user interactions and correlations to identify patterns in the customer database

Answer: D

Explanation:
Collaborative filtering is a machine learning technique that recommends products or services to users based on the ratings or preferences of other users. This technique is well-suited for identifying customer shopping patterns and preferences because it takes into account the interactions between users and products.

NEW QUESTION # 224
A company is building a predictive maintenance model based on machine learning (ML). The data is stored in a fully private Amazon S3 bucket that is encrypted at rest with AWS Key Management Service (AWS KMS) CMKs. An ML specialist must run data preprocessing by using an Amazon SageMaker Processing job that is triggered from code in an Amazon SageMaker notebook. The job should read data from Amazon S3, process it, and upload it back to the same S3 bucket. The preprocessing code is stored in a container image in Amazon Elastic Container Registry (Amazon ECR). The ML specialist needs to grant permissions to ensure a smooth data preprocessing workflow.
Which set of actions should the ML specialist take to meet these requirements?

• A. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. Create an Amazon SageMaker Processing job with an IAM role that has read and write permissions to the relevant S3 bucket, and appropriate KMS and ECR permissions.
• B. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs and to access Amazon ECR. Attach the role to the SageMaker notebook instance. Set up both an S3 endpoint and a KMS endpoint in the default VPC. Create Amazon SageMaker Processing jobs from the notebook.
• C. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs, S3 read and write access to the relevant S3 bucket, and appropriate KMS and ECR permissions. Attach the role to the SageMaker notebook instance. Create an Amazon SageMaker Processing job from the notebook.
• D. Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. Set up an S3 endpoint in the default VPC. Create Amazon SageMaker Processing jobs with the access key and secret key of the IAM user with appropriate KMS and ECR permissions.

Answer: A

Explanation:
The correct solution for granting permissions for data preprocessing is to use the following steps:
* Create an IAM role that has permissions to create Amazon SageMaker Processing jobs. Attach the role to the SageMaker notebook instance. This role allows the ML specialist to run Processing jobs from the notebook code1
* Create an Amazon SageMaker Processing job with an IAM role that has read and write permissions to the relevant S3 bucket, and appropriate KMS and ECR permissions. This role allows the Processing job to access the data in the encrypted S3 bucket, decrypt it with the KMS CMK, and pull the container image from ECR23 The other options are incorrect because they either miss some permissions or use unnecessary steps. For example:
* Option A uses a single IAM role for both the notebook instance and the Processing job. This role may have more permissions than necessary for the notebook instance, which violates the principle of least privilege4
* Option C sets up both an S3 endpoint and a KMS endpoint in the default VPC. These endpoints are not required for the Processing job to access the data in the encrypted S3 bucket. They are only needed if the Processing job runs in network isolation mode, which is not specified in the question.
* Option D uses the access key and secret key of the IAM user with appropriate KMS and ECR permissions. This is not a secure way to pass credentials to the Processing job. It also requires the ML specialist to manage the IAM user and the keys.
1: Create an Amazon SageMaker Notebook Instance - Amazon SageMaker
2: Create a Processing Job - Amazon SageMaker
3: Use AWS KMS-Managed Encryption Keys - Amazon Simple Storage Service
4: IAM Best Practices - AWS Identity and Access Management
Network Isolation - Amazon SageMaker
Understanding and Getting Your Security Credentials - AWS General Reference

NEW QUESTION # 225
A company builds computer-vision models that use deep learning for the autonomous vehicle industry. A machine learning (ML) specialist uses an Amazon EC2 instance that has a CPU: GPU ratio of 12:1 to train the models.
The ML specialist examines the instance metric logs and notices that the GPU is idle half of the time The ML specialist must reduce training costs without increasing the duration of the training jobs.
Which solution will meet these requirements?

• A. Switch to an instance type that has only CPUs.
• B. Switch to an instance type that has a CPU GPU ratio of 6:1.
• C. Use memory-optimized EC2 Spot Instances for the training jobs.
• D. Use a heterogeneous cluster that has two different instances groups.

Answer: B

Explanation:
Explanation
Switching to an instance type that has a CPU: GPU ratio of 6:1 will reduce the training costs by using fewer CPUs and GPUs, while maintaining the same level of performance. The GPU idle time indicates that the CPU is not able to feed the GPU with enough data, so reducing the CPU: GPU ratio will balance the workload and improve the GPU utilization. A lower CPU: GPU ratio also means less overhead for inter-process communication and synchronization between the CPU and GPU processes. References:
Optimizing GPU utilization for AI/ML workloads on Amazon EC2
Analyze CPU vs. GPU Performance for AWS Machine Learning

NEW QUESTION # 226
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. Choose a lower number of layers
• C. Enable dropout
• D. Choose a higher number of layers
• E. Choose a smaller learning rate
• F. Include all the images from the test set in the training set

Answer: C,D,F

NEW QUESTION # 227
A retail company intends to use machine learning to categorize new products A labeled dataset of current products was provided to the Data Science team The dataset includes 1 200 products The labeled dataset has
15 features for each product such as title dimensions, weight, and price Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.
Which model should be used for categorizing new products using the provided dataset for training?

• A. A regression forest where the number of trees is set equal to the number of product categories
• B. An XGBoost model where the objective parameter is set to multi: softmax
• C. A DeepAR forecasting model based on a recurrent neural network (RNN)
• D. A deep convolutional neural network (CNN) with a softmax activation function for the last layer

Answer: B

Explanation:
Explanation
XGBoost is a machine learning framework that can be used for classification, regression, ranking, and other tasks. It is based on the gradient boosting algorithm, which builds an ensemble of weak learners (usually decision trees) to produce a strong learner. XGBoost has several advantages over other algorithms, such as scalability, parallelization, regularization, and sparsity handling. For categorizing new products using the provided dataset, an XGBoost model would be a suitable choice, because it can handle multiple features and multiple classes efficiently and accurately. To train an XGBoost model for multi-class classification, the objective parameter should be set to multi: softmax, which means that the model will output a probability distribution over the classes and predict the class with the highest probability. Alternatively, the objective parameter can be set to multi: softprob, which means that the model will output the raw probability of each class instead of the predicted class label. This can be useful for evaluating the model performance or for post-processing the predictions. References:
XGBoost: A tutorial on how to use XGBoost with Amazon SageMaker.
XGBoost Parameters: A reference guide for the parameters of XGBoost.

NEW QUESTION # 228
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