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The AWS Certified Machine Learning - Specialty exam covers a wide range of topics such as data engineering, data analysis, machine learning algorithms, and deployment. AWS-Certified-Machine-Learning-Specialty exam also tests the candidate's ability to design and implement machine learning solutions that are cost-effective, scalable, and reliable. It is a challenging exam that requires a deep understanding of machine learning concepts and their practical applications in real-world scenarios.

Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) certification exam is designed for professionals who want to demonstrate their expertise in machine learning on the Amazon Web Services (AWS) platform. AWS Certified Machine Learning - Specialty certification is intended for individuals with a strong background in data and machine learning, and it validates their ability to design, implement, and deploy machine learning models on AWS.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q106-Q111):

NEW QUESTION # 106
A data engineer at a bank is evaluating a new tabular dataset that includes customer dat a. The data engineer will use the customer data to create a new model to predict customer behavior. After creating a correlation matrix for the variables, the data engineer notices that many of the 100 features are highly correlated with each other.
Which steps should the data engineer take to address this issue? (Choose two.)

A. Use a linear-based algorithm to train the model.
B. Apply one-hot encoding category-based variables.
C. Remove a portion of highly correlated features from the dataset.
D. Apply principal component analysis (PCA).
E. Apply min-max feature scaling to the dataset.

Answer: D,E

Explanation:
Reference:
https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html

NEW QUESTION # 107
A machine learning (ML) specialist is developing a model for a company. The model will classify and predict sequences of objects that are displayed in a video. The ML specialist decides to use a hybrid architecture that consists of a convolutional neural network (CNN) followed by a classifier three-layer recurrent neural network (RNN).
The company developed a similar model previously but trained the model to classify a different set of objects.
The ML specialist wants to save time by using the previously trained model and adapting the model for the current use case and set of objects.
Which combination of steps will accomplish this goal with the LEAST amount of effort? (Select TWO.)

A. Reinitialize the weights of the last layer of the RNN. Retrain the entire model on the prediction task by using the new set of objects.
B. Reinitialize the weights of the entire RNN. Retrain the entire model on the prediction task by using the new set of objects.
C. Reinitialize the weights of the entire CNN. Retrain the CNN on the classification task by using the new set of objects.
D. Reinitialize the weights of the last fully connected layer of the CNN. Retrain the CNN on the classification task by using the new set of objects.
E. Reinitialize the weights of the entire network. Retrain the entire network on the prediction task by using the new set of objects.

Answer: A,D

Explanation:
To adapt a previously trained model to a new but related task efficiently, the best practice is to leverage transfer learning. This involves retaining the learned features from the earlier model and only retraining the final layers to accommodate the new classification categories.
In the context of a hybrid architecture combining a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN):
* CNN Component: The CNN is responsible for extracting spatial features from video frames. Since the early layers of a CNN capture generic features like edges and textures, they are often transferable across tasks. Therefore, only the last fully connected layer, which maps these features to specific object classes, needs to be reinitialized and retrained for the new set of objects.
* RNN Component: The RNN handles the temporal dynamics of the sequence data. Similar to the CNN, the earlier layers of the RNN capture general sequence patterns. Thus, reinitializing and retraining only the last layer of the RNN allows the model to adapt to the new prediction task without the need to retrain the entire network.
This approach minimizes training time and computational resources while effectively adapting the model to new tasks.

NEW QUESTION # 108
A data scientist at a financial services company used Amazon SageMaker to train and deploy a model that predicts loan defaults. The model analyzes new loan applications and predicts the risk of loan default. To train the model, the data scientist manually extracted loan data from a database. The data scientist performed the model training and deployment steps in a Jupyter notebook that is hosted on SageMaker Studio notebooks.
The model's prediction accuracy is decreasing over time. Which combination of slept in the MOST operationally efficient way for the data scientist to maintain the model's accuracy? (Select TWO.)

A. Store the model predictions in Amazon S3 Create a daily SageMaker Processing job that reads the predictions from Amazon S3, checks for changes in model prediction accuracy, and sends an email notification if a significant change is detected.
B. Rerun the steps in the Jupyter notebook that is hosted on SageMaker Studio notebooks to retrain the model and redeploy a new version of the model.
C. Configure SageMaker Model Monitor with an accuracy threshold to check for model drift. Initiate an Amazon CloudWatch alarm when the threshold is exceeded. Connect the workflow in SageMaker Pipelines with the CloudWatch alarm to automatically initiate retraining.
D. Export the training and deployment code from the SageMaker Studio notebooks into a Python script.Package the script into an Amazon Elastic Container Service (Amazon ECS) task that an AWS Lambda function can initiate.
E. Use SageMaker Pipelines to create an automated workflow that extracts fresh data, trains the model, and deploys a new version of the model.

Answer: C,E

Explanation:
* Option A is correct because SageMaker Pipelines is a service that enables you to create and manage automated workflows for your machine learning projects. You can use SageMaker Pipelines to orchestrate the steps of data extraction, model training, and model deployment in a repeatable and scalable way1.
* Option B is correct because SageMaker Model Monitor is a service that monitors the quality of your models in production and alerts you when there are deviations in the model quality. You can use SageMaker Model Monitor to set an accuracy threshold for your model and configure a CloudWatch alarm that triggers when the threshold is exceeded. You can then connect the alarm to the workflow in SageMaker Pipelines to automatically initiate retraining and deployment of a new version of the model2.
* Option C is incorrect because it is not the most operationally efficient way to maintain the model's accuracy. Creating a daily SageMaker Processing job that reads the predictions from Amazon S3 and checks for changes in model prediction accuracy is a manual and time-consuming process. It also requires you to write custom code to perform the data analysis and send the email notification.
Moreover, it does not automatically retrain and deploy the model when the accuracy drops.
* Option D is incorrect because it is not the most operationally efficient way to maintain the model's accuracy. Rerunning the steps in the Jupyter notebook that is hosted on SageMaker Studio notebooks to retrain the model and redeploy a new version of the model is a manual and error-prone process. It also requires you to monitor the model's performance and initiate the retraining and deployment steps yourself. Moreover, it does not leverage the benefits of SageMaker Pipelines and SageMaker Model Monitor to automate and streamline the workflow.
* Option E is incorrect because it is not the most operationally efficient way to maintain the model's accuracy. Exporting the training and deployment code from the SageMaker Studio notebooks into a Python script and packaging the script into an Amazon ECS task that an AWS Lambda function can initiate is a complex and cumbersome process. It also requires you to manage the infrastructure and resources for the Amazon ECS task and the AWS Lambda function. Moreover, it does not leverage the benefits of SageMaker Pipelines and SageMaker Model Monitor to automate and streamline the workflow.
1: SageMaker Pipelines - Amazon SageMaker
2: Monitor data and model quality - Amazon SageMaker

NEW QUESTION # 109
A machine learning specialist works for a fruit processing company and needs to build a system that categorizes apples into three types. The specialist has collected a dataset that contains 150 images for each type of apple and applied transfer learning on a neural network that was pretrained on ImageNet with this dataset.
The company requires at least 85% accuracy to make use of the model.
After an exhaustive grid search, the optimal hyperparameters produced the following:
68% accuracy on the training set
67% accuracy on the validation set
What can the machine learning specialist do to improve the system's accuracy?

A. Train a new model using the current neural network architecture.
B. Add more data to the training set and retrain the model using transfer learning to reduce the bias.
C. Use a neural network model with more layers that are pretrained on ImageNet and apply transfer learning to increase the variance.
D. Upload the model to an Amazon SageMaker notebook instance and use the Amazon SageMaker HPO feature to optimize the model's hyperparameters.

Answer: B

Explanation:
Explanation
The problem described in the question is a case of underfitting, where the neural network model performs poorly on both the training and validation sets. This means that the model has not learned the features of the data well enough and has high bias. To solve this issue, the machine learning specialist should consider the following change:
Add more data to the training set and retrain the model using transfer learning to reduce the bias:
Adding more data to the training set can help the model learn more patterns and variations in the data and improve its performance. Transfer learning can also help the model leverage the knowledge from the pre-trained network and adapt it to the new data. This can reduce the bias and increase the accuracy of the model.
References:
Transfer learning for TensorFlow image classification models in Amazon SageMaker Transfer learning for custom labels using a TensorFlow container and "bring your own algorithm" in Amazon SageMaker Machine Learning Concepts - AWS Training and Certification

NEW QUESTION # 110
A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.
The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:
Based on the model evaluation results, why is this a viable model for production?

A. The precision of the model is 86%, which is greater than the accuracy of the model.
B. The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.
C. The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.
D. The precision of the model is 86%, which is less than the accuracy of the model.

Answer: B

NEW QUESTION # 111
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