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Recommended Experience

Before registering for the AWS Certified Machine Learning – Specialty exam, the applicant should ensure meeting some prerequisites as stated by the vendor. First, working experience of 1-2 years in running ML workloads as well as their development and architecting on AWS cloud is a must for the candidate. Moreover, it is recommended to have practical skills in executing the hyperparameter optimization, deep learning and ML frameworks, and operational and model-training best practices for AWS Machine learning.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q27-Q32):

NEW QUESTION # 27
A data scientist is working on a public sector project for an urban traffic system. While studying the traffic patterns, it is clear to the data scientist that the traffic behavior at each light is correlated, subject to a small stochastic error term. The data scientist must model the traffic behavior to analyze the traffic patterns and reduce congestion.
How will the data scientist MOST effectively model the problem?

A. The data scientist should obtain a correlated equilibrium policy by formulating this problem as a multi- agent reinforcement learning problem.
B. Rather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using unlabeled simulated data representing the new traffic patterns in the city and applying an unsupervised learning approach.
C. Rather than finding an equilibrium policy, the data scientist should obtain accurate predictors of traffic flow by using historical data through a supervised learning approach.
D. The data scientist should obtain the optimal equilibrium policy by formulating this problem as a single- agent reinforcement learning problem.

Answer: A

Explanation:
The data scientist should obtain a correlated equilibrium policy by formulating this problem as a multi-agent reinforcement learning problem. This is because:
* Multi-agent reinforcement learning (MARL) is a subfield of reinforcement learning that deals with learning and coordination of multiple agents that interact with each other and the environment 1. MARL can be applied to problems that involve distributed decision making, such as traffic signal control, where each traffic light can be modeled as an agent that observes the traffic state and chooses an action (e.g., changing the signal phase) to optimize a reward function (e.g., minimizing the delay or congestion) 2.
* A correlated equilibrium is a solution concept in game theory that generalizes the notion of Nash equilibrium. It is a probability distribution over the joint actions of the agents that satisfies the following condition: no agent can improve its expected payoff by deviating from the distribution, given that it knows the distribution and the actions of the other agents 3. A correlated equilibrium can capture the correlation among the agents' actions, which is useful for modeling the traffic behavior at each light that is subject to a small stochastic error term.
* A correlated equilibrium policy is a policy that induces a correlated equilibrium in a MARL setting. It can be obtained by using various methods, such as policy gradient, actor-critic, or Q-learning algorithms, that can learn from the feedback of the environment and the communication among the agents 4. A correlated equilibrium policy can achieve a better performance than a Nash equilibrium policy, which assumes that the agents act independently and ignore the correlation among their actions 5.
Therefore, by obtaining a correlated equilibrium policy by formulating this problem as a MARL problem, the data scientist can most effectively model the traffic behavior and reduce congestion.
Multi-Agent Reinforcement Learning
Multi-Agent Reinforcement Learning for Traffic Signal Control: A Survey Correlated Equilibrium Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments Correlated Q-Learning

NEW QUESTION # 28
A Data Scientist is developing a machine learning model to classify whether a financial transaction is fraudulent. The labeled data available for training consists of 100,000 non-fraudulent observations and 1,000 fraudulent observations.
The Data Scientist applies the XGBoost algorithm to the data, resulting in the following confusion matrix when the trained model is applied to a previously unseen validation dataset. The accuracy of the model is
99.1%, but the Data Scientist needs to reduce the number of false negatives.

Which combination of steps should the Data Scientist take to reduce the number of false negative predictions by the model? (Choose two.)

A. Change the XGBoost eval_metric parameter to optimize based on Root Mean Square Error (RMSE).
B. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.
C. Change the XGBoost eval_metric parameter to optimize based on Area Under the ROC Curve (AUC).
D. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
E. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.

Answer: C,E

Explanation:
The Data Scientist should increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights and change the XGBoost eval_metric parameter to optimize based on Area Under the ROC Curve (AUC). This will help reduce the number of false negative predictions by the model.
The scale_pos_weight parameter controls the balance of positive and negative weights in the XGBoost algorithm. It is useful for imbalanced classification problems, such as fraud detection, where the number of positive examples (fraudulent transactions) is much smaller than the number of negative examples (non- fraudulent transactions). By increasing the scale_pos_weight parameter, the Data Scientist can assign more weight to the positive class and make the model more sensitive to detecting fraudulent transactions.
The eval_metric parameter specifies the metric that is used to measure the performance of the model during training and validation. The default metric for binary classification problems is the error rate, which is the fraction of incorrect predictions. However, the error rate is not a good metric for imbalanced classification problems, because it does not take into account the cost of different types of errors. For example, in fraud detection, a false negative (failing to detect a fraudulent transaction) is more costly than a false positive (flagging a non-fraudulent transaction as fraudulent). Therefore, the Data Scientist should use a metric that reflects the trade-off between the true positive rate (TPR) and the false positive rate (FPR), such as the Area Under the ROC Curve (AUC). The AUC is a measure of how well the model can distinguish between the positive and negative classes, regardless of the classification threshold. A higher AUC means that the model can achieve a higher TPR with a lower FPR, which is desirable for fraud detection.
References:
* XGBoost Parameters - Amazon Machine Learning
* Using XGBoost with Amazon SageMaker - AWS Machine Learning Blog

NEW QUESTION # 29
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. Send Amazon CloudWatch Logs that were generated by Amazon SageMaker lo Amazon ES and use Kibana to query and visualize the log data.
B. Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the 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 OuickSight to visualize logs as they are being produced
D. 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

Answer: D

Explanation:
Amazon CloudWatch is a service that can monitor and collect various metrics and logs from AWS resources, such as Amazon SageMaker. Amazon CloudWatch can also generate dashboards to create a single view for the metrics and logs that are of interest. By using Amazon CloudWatch, the Machine Learning Specialist can review the latency, memory utilization, and CPU utilization during the load test, as these are some of the metrics that are outputted by Amazon SageMaker. The Specialist can create a custom dashboard that displays these metrics in different widgets, such as graphs, tables, or text. The dashboard can also be configured to refresh automatically and show the latest data as the load test is running. This approach will allow the Specialist to monitor the performance and resource utilization of the model variant and adjust the Auto Scaling configuration accordingly.
References:
[Monitoring Amazon SageMaker with Amazon CloudWatch - Amazon SageMaker]
[Using Amazon CloudWatch Dashboards - Amazon CloudWatch]
[Create a CloudWatch Dashboard - Amazon CloudWatch]

NEW QUESTION # 30
A company is observing low accuracy while training on the default built-in image classification algorithm in Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture instead of a ResNet architecture.
Which of the following will accomplish this? (Select TWO.)

A. Create a support case with the SageMaker team to change the default image classification algorithm to Inception.
B. Download and apt-get install the inception network code into an Amazon EC2 instance and use this instance as a Jupyter notebook in Amazon SageMaker.
C. Customize the built-in image classification algorithm to use Inception and use this for model training.
D. Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training.
E. Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network and use this for model training.

Answer: C,E

NEW QUESTION # 31
A Machine Learning team runs its own training algorithm on Amazon SageMaker. The training algorithm requires external assets. The team needs to submit both its own algorithm code and algorithm-specific parameters to Amazon SageMaker.
What combination of services should the team use to build a custom algorithm in Amazon SageMaker?
(Choose two.)

A. Amazon ECS
B. AWS Secrets Manager
C. AWS CodeStar
D. Amazon ECR
E. Amazon S3

Answer: D,E

NEW QUESTION # 32
......

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Exam AWS-Certified-Machine-Learning-Specialty Demo - Latest AWS-Certified-Machine-Learning-Specialty Test Vce

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q27-Q32):

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