This kind of prep method is effective when preparing for the Amazon AWS-Certified-Machine-Learning-Specialty certification exam since the cert demands polished skills and an inside-out understanding of the syllabus. These skills can be achieved when you go through intensive Amazon AWS-Certified-Machine-Learning-Specialty Exam Training and attempt actual Amazon AWS-Certified-Machine-Learning-Specialty.

Understanding functional and technical aspects of AWS Certified Machine Learning Specialty Exam Modeling

The following will be dicussed here:

Select the appropriate model(s) for a given machine learning problemPerform hyperparameter optimizationFrame business problems as machine learning problemsEvaluate machine learning modelsTrain machine learning models

The AWS Certified Machine Learning - Specialty exam is designed to test the skills and knowledge of professionals who work with machine learning technologies on the Amazon Web Services (AWS) platform. The certification is intended for individuals who have experience with data science, machine learning algorithms, and AWS services. The exam is designed to test candidates' ability to design, implement, deploy, and maintain machine learning solutions on AWS.

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Introduction to AWS Certified Machine Learning Specialty Exam

The AWS Certified Machine Learning Exam - Special Exam (MLS-C01) is designed for people who have a development or data science function. This exam validates a candidate's ability to create, train, adapt and implement machine learning (ML) models using the AWS cloud. Validation of a candidate's ability to design, implement, implement and maintain ML solutions for certain business problems. It will validate the candidate's ability to: Select and justify the appropriate LD approach for a given business problem. Identify the appropriate AWS services to implement ML solutions. Design and implement scalable, economic, reliable and safe ML solutions.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q76-Q81):

NEW QUESTION # 76
A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker. The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant.
Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test?

A. Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker.B. Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the log data as it is generated by Amazon SageMaker.C. Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon QuickSight to visualize logs as they are being produced.D. Send Amazon CloudWatch Logs that were generated by Amazon SageMaker to Amazon ES and use Kibana to query and visualize the log data.

Answer: A

Explanation:
Explanation/Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/monitoring-cloudwatch.html

NEW QUESTION # 77
A Machine Learning Specialist is given a structured dataset on the shopping habits of a company's customer base. The dataset contains thousands of columns of data and hundreds of numerical columns for each customer. The Specialist wants to identify whether there are natural groupings for these columns across all customers and visualize the results as quickly as possible.
What approach should the Specialist take to accomplish these tasks?

A. Run k-means using the Euclidean distance measure for different values of k and create an elbow plot.B. Run k-means using the Euclidean distance measure for different values of k and create box plots for each numerical column within each cluster.C. Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a scatter plot.D. Embed the numerical features using the t-distributed stochastic neighbor embedding (t-SNE) algorithm and create a line graph.

Answer: A

NEW QUESTION # 78
A large consumer goods manufacturer has the following products on sale:
* 34 different toothpaste variants
* 48 different toothbrush variants
* 43 different mouthwash variants
The entire sales history of all these products is available in Amazon S3. Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products. The company wants to predict the demand for a new product that will soon be launched.
Which solution should a Machine Learning Specialist apply?

A. Train a custom ARIMA model to forecast demand for the new product.B. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.C. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product.D. Train a custom XGBoost model to forecast demand for the new product.

Answer: C

Explanation:
The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN). Classical forecasting methods, such as autoregressive integrated moving average (ARIMA) or exponential smoothing (ETS), fit a single model to each individual time series. They then use that model to extrapolate the time series into the future.
Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html

NEW QUESTION # 79
A company wants to predict the sale prices of houses based on available historical sales dat a. The target variable in the company's dataset is the sale price. The features include parameters such as the lot size, living area measurements, non-living area measurements, number of bedrooms, number of bathrooms, year built, and postal code. The company wants to use multi-variable linear regression to predict house sale prices.
Which step should a machine learning specialist take to remove features that are irrelevant for the analysis and reduce the model's complexity?

A. Build a heatmap showing the correlation of the dataset against itself. Remove features with low mutual correlation scores.B. Plot a histogram of the features and compute their standard deviation. Remove features with high variance.C. Plot a histogram of the features and compute their standard deviation. Remove features with low variance.D. Run a correlation check of all features against the target variable. Remove features with low target variable correlation scores.

Answer: D

NEW QUESTION # 80
A gaming company has launched an online game where people can start playing for free, but they need to pay if they choose to use certain features. The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year.
The company has gathered a labeled dataset from 1 million users.
The training dataset consists of 1,000 positive samples (from users who ended up paying within 1 year) and 999,000 negative samples (from users who did not use any paid features). Each data sample consists of 200 features including user age, device, location, and play patterns.
Using this dataset for training, the Data Science team trained a random forest model that converged with over 99% accuracy on the training set. However, the prediction results on a test dataset were not satisfactory Which of the following approaches should the Data Science team take to mitigate this issue?
(Choose two.)

A. Change the cost function so that false positives have a higher impact on the cost value than false negatives.B. Change the cost function so that false negatives have a higher impact on the cost value than false positives.C. Include a copy of the samples in the test dataset in the training dataset.D. Add more deep trees to the random forest to enable the model to learn more features.E. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.

Answer: B,C

NEW QUESTION # 81
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