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How to book the AWS Certified Machine Learning - Specialty

These are following steps for registering the Amazon MLS exam:

Step 1 : Signup/Login to AWS account
Step 2 : Search for the exam
Step 3 : Select Date and Center of examination and confirm with payment

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Amazon MLS-C01 exam covers a range of topics such as data engineering, exploratory data analysis, feature engineering, model selection and training, tuning and optimization, machine learning algorithms, and deploying and maintaining machine learning models. Candidates are expected to have a deep understanding of these topics and be able to apply their knowledge to real-world scenarios. AWS-Certified-Machine-Learning-Specialty exam is designed to test the candidate's ability to create efficient and effective machine learning solutions on AWS. Successful candidates will be able to demonstrate their expertise in the field and show that they are capable of designing and deploying machine learning models that meet business requirements.

Amazon AWS-Certified-Machine-Learning-Specialty is a certification exam that validates the skills and knowledge of professionals in machine learning on the Amazon Web Services (AWS) platform. AWS-Certified-Machine-Learning-Specialty exam is designed for individuals who want to demonstrate their ability to design, implement, deploy, and maintain machine learning solutions on AWS. By passing AWS-Certified-Machine-Learning-Specialty exam, professionals can showcase their expertise in machine learning, which is a highly in-demand skill in the tech industry.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q194-Q199):

NEW QUESTION # 194
A Machine Learning Specialist built an image classification deep learning model. However the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%r respectively.
How should the Specialist address this issue and what is the reason behind it?

A. The learning rate should be increased because the optimization process was trapped at a local minimum.
B. The dropout rate at the flatten layer should be increased because the model is not generalized enough.
C. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.
D. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.

Answer: C

NEW QUESTION # 195
A data science team is planning to build a natural language processing (NLP) application. The application's text preprocessing stage will include part-of-speech tagging and key phase extraction. The preprocessed text will be input to a custom classification algorithm that the data science team has already written and trained using Apache MXNet.
Which solution can the team build MOST quickly to meet these requirements?

A. Use Amazon Comprehend for the part-of-speech tagging, key phase extraction, and classification tasks.
B. Use Amazon Comprehend for the part-of-speech tagging and key phase extraction tasks. Use Amazon SageMaker built-in Latent Dirichlet Allocation (LDA) algorithm to build the custom classifier.
C. Use an NLP library in Amazon SageMaker for the part-of-speech tagging. Use Amazon Comprehend for the key phase extraction. Use AWS Deep Learning Containers with Amazon SageMaker to build the custom classifier.
D. Use Amazon Comprehend for the part-of-speech tagging and key phase extraction tasks. Use AWS Deep Learning Containers with Amazon SageMaker to build the custom classifier.

Answer: D

Explanation:
Explanation
Amazon Comprehend is a natural language processing (NLP) service that can perform part-of-speech tagging and key phrase extraction tasks. AWS Deep Learning Containers are Docker images that are pre-installed with popular deep learning frameworks such as Apache MXNet. Amazon SageMaker is a fully managed service that can help build, train, and deploy machine learning models. Using Amazon Comprehend for the text preprocessing tasks and AWS Deep Learning Containers with Amazon SageMaker to build the custom classifier is the solution that can be built most quickly to meet the requirements.
References:
Amazon Comprehend
AWS Deep Learning Containers
Amazon SageMaker

NEW QUESTION # 196
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. Augment training data for each item using image variants like inversions and translations, build the model, and iterate.
B. Attach different colored labels to each item, take the images again, and build the model
C. Reduce the number of distinct items from 10 to 2, build the model, and iterate
D. Convert the images to grayscale and retrain the model

Answer: A

Explanation:
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

NEW QUESTION # 197
A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist.
Which machine learning model type should the Specialist use to accomplish this task?

A. Classification
B. Linear regression
C. Reinforcement learning
D. Clustering

Answer: A

Explanation:
Explanation
The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/non-churner) - answers that need to be predicted - to train an algorithm. With classification, businesses can answer the following questions:
* Will this customer churn or not?
* Will a customer renew their subscription?
* Will a user downgrade a pricing plan?
* Are there any signs of unusual customer behavior?

NEW QUESTION # 198
A machine learning (ML) specialist is using Amazon SageMaker hyperparameter optimization (HPO) to improve a model's accuracy. The learning rate parameter is specified in the following HPO configuration:

During the results analysis, the ML specialist determines that most of the training jobs had a learning rate between 0.01 and 0.1. The best result had a learning rate of less than 0.01. Training jobs need to run regularly over a changing dataset. The ML specialist needs to find a tuning mechanism that uses different learning rates more evenly from the provided range between MinValue and MaxValue.
Which solution provides the MOST accurate result?

A. Modify the HPO configuration as follows:

Select the most accurate hyperparameter configuration form this HPO job.
B. Modify the HPO configuration as follows:

Select the most accurate hyperparameter configuration form this training job.
C. Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue. Divide the number of training jobs for each HPO job by three:
[0.01, 0.1]
[0.001, 0.01]
[0.0001, 0.001]
Select the most accurate hyperparameter configuration form these three HPO jobs.
D. Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue while using the same number of training jobs for each HPO job:
[0.01, 0.1]
[0.001, 0.01]
[0.0001, 0.001]
Select the most accurate hyperparameter configuration form these three HPO jobs.

Answer: B

Explanation:
The solution C modifies the HPO configuration to use a logarithmic scale for the learning rate parameter. This means that the values of the learning rate are sampled from a log-uniform distribution, which gives more weight to smaller values. This can help to explore the lower end of the range more evenly and find the optimal learning rate more efficiently. The other solutions either use a linear scale, which may not sample enough values from the lower end, or divide the range into sub-intervals, which may miss some combinations of hyperparameters. References:
How Hyperparameter Tuning Works - Amazon SageMaker
Tuning Hyperparameters - Amazon SageMaker

NEW QUESTION # 199
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Test AWS-Certified-Machine-Learning-Specialty Prep & AWS-Certified-Machine-Learning-Specialty Exam Topics Pdf

How to book the AWS Certified Machine Learning - Specialty

Pass Guaranteed AWS-Certified-Machine-Learning-Specialty - Unparalleled Test AWS Certified Machine Learning - Specialty Prep

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q194-Q199):