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The AWS-Certified-Machine-Learning-Specialty exam is a multiple-choice exam that consists of 65 questions. AWS-Certified-Machine-Learning-Specialty exam duration is 3 hours, and the passing score is 750 out of 1000. AWS-Certified-Machine-Learning-Specialty Exam Fee is $300, and it can be taken at any of the AWS testing centers or through online proctoring.
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To take the Amazon MLS-C01 exam, candidates must first pass the AWS Certified Cloud Practitioner or AWS Certified Solutions Architect Associate exam. AWS-Certified-Machine-Learning-Specialty Exam consists of 65 multiple-choice and multiple-response questions and must be completed in 180 minutes. AWS-Certified-Machine-Learning-Specialty exam is available in several languages, including English, Japanese, Korean, and Simplified Chinese. Upon passing the exam, candidates will receive an AWS Certified Machine Learning – Specialty certification, which is valid for three years. AWS Certified Machine Learning - Specialty certification demonstrates to employers and clients that the individual has the skills and knowledge needed to design, implement, and maintain machine learning solutions on the AWS platform.
Achieving the AWS Certified Machine Learning - Specialty certification demonstrates the candidate's expertise in machine learning on the AWS platform and can help them stand out in the job market. AWS Certified Machine Learning - Specialty certification is highly valued by employers and can lead to exciting career opportunities in data science, machine learning engineering, and other related fields.
Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q253-Q258):
NEW QUESTION # 253
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 has been asked to reduce the number of false negatives.
Which combination of steps should the Data Scientist take to reduce the number of false positive predictions by the model? (Select TWO.)
Answer: B,D
Explanation:
* The XGBoost algorithm is a popular machine learning technique for classification problems. It is based on the idea of boosting, which is to combine many weak learners (decision trees) into a strong learner (ensemble model).
* The XGBoost algorithm can handle imbalanced data by using the scale_pos_weight parameter, which controls the balance of positive and negative weights in the objective function. A typical value to consider is the ratio of negative cases to positive cases in the data. By increasing this parameter, the algorithm will pay more attention to the minority class (positive) and reduce the number of false negatives.
* The XGBoost algorithm can also use different evaluation metrics to optimize the model performance.
The default metric is error, which is the misclassification rate. However, this metric can be misleading for imbalanced data, as it does not account for the different costs of false positives and false negatives.
A better metric to use is AUC, which is the area under the receiver operating characteristic (ROC) curve. The ROC curve plots the true positive rate against the false positive rate for different threshold values. The AUC measures how well the model can distinguish between the two classes, regardless of the threshold. By changing the eval_metric parameter to AUC, the algorithm will try to maximize the AUC score and reduce the number of false negatives.
* Therefore, the combination of steps that should be taken to reduce the number of false negatives are to increase the scale_pos_weight parameter and change the eval_metric parameter to AUC.
References:
* XGBoost Parameters
* XGBoost for Imbalanced Classification
NEW QUESTION # 254
A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting.
Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.
What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?
Answer: D
NEW QUESTION # 255
A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor, and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset.
Which tool should be used to improve the validation accuracy?
Answer: B
Explanation:
https://monkeylearn.com/sentiment-analysis/
NEW QUESTION # 256
A Data Scientist is training a multilayer perception (MLP) on a dataset with multiple classes. The target class of interest is unique compared to the other classes within the dataset, but it does not achieve and acceptable ecall metric. The Data Scientist has already tried varying the number and size of the MLP's hidden layers, which has not significantly improved the results. A solution to improve recall must be implemented as quickly as possible.
Which techniques should be used to meet these requirements?
Answer: A
Explanation:
Explanation
The best technique to improve the recall of the MLP for the target class of interest is to add class weights to the MLP's loss function and then retrain. Class weights are a way of assigning different importance to each class in the dataset, such that the model will pay more attention to the classes with higher weights. This can help mitigate the class imbalance problem, where the model tends to favor the majority class and ignore the minority class. By increasing the weight of the target class of interest, the model will try to reduce the false negatives and increase the true positives, which will improve the recall metric. Adding class weights to the loss function is also a quick and easy solution, as it does not require gathering more data, changing the model architecture, or switching to a different algorithm.
References:
AWS Machine Learning Specialty Exam Guide
AWS Machine Learning Training - Deep Learning with Amazon SageMaker
AWS Machine Learning Training - Class Imbalance and Weighted Loss Functions
NEW QUESTION # 257
A data scientist is training a large PyTorch model by using Amazon SageMaker. It takes 10 hours on average to train the model on GPU instances. The data scientist suspects that training is not converging and that resource utilization is not optimal.
What should the data scientist do to identify and address training issues with the LEAST development effort?
Answer: D
Explanation:
Explanation
The solution C is the best option to identify and address training issues with the least development effort. The solution C involves the following steps:
Use the SageMaker Debugger vanishing_gradient and LowGPUUtilization built-in rules to detect issues.
SageMaker Debugger is a feature of Amazon SageMaker that allows data scientists to monitor, analyze, and debug machine learning models during training. SageMaker Debugger provides a set of built-in rules that can automatically detect common issues and anomalies in model training, such as vanishing or exploding gradients, overfitting, underfitting, low GPU utilization, and more1. The data scientist can use the vanishing_gradient rule to check if the gradients are becoming too small and causing the training to not converge. The data scientist can also use the LowGPUUtilization rule to check if the GPU resources are underutilized and causing the training to be inefficient2.
Launch the StopTrainingJob action if issues are detected. SageMaker Debugger can also take actions based on the status of the rules. One of the actions is StopTrainingJob, which can terminate the training job if a rule is in an error state. This can help the data scientist to save time and money by stopping the training early if issues are detected3.
The other options are not suitable because:
Option A: Using CPU utilization metrics that are captured in Amazon CloudWatch and configuring a CloudWatch alarm to stop the training job early if low CPU utilization occurs will not identify and address training issues effectively. CPU utilization is not a good indicator of model training performance, especially for GPU instances. Moreover, CloudWatch alarms can only trigger actions based on simple thresholds, not complex rules or conditions4.
Option B: Using high-resolution custom metrics that are captured in Amazon CloudWatch and configuring an AWS Lambda function to analyze the metrics and to stop the training job early if issues are detected will incur more development effort than using SageMaker Debugger. The data scientist will have to write the code for capturing, sending, and analyzing the custom metrics, as well as for invoking the Lambda function and stopping the training job. Moreover, this solution may not be able to detect all the issues that SageMaker Debugger can5.
Option D: Using the SageMaker Debugger confusion and feature_importance_overweight built-in rules and launching the StopTrainingJob action if issues are detected will not identify and address training issues effectively. The confusion rule is used to monitor the confusion matrix of a classification model, which is not relevant for a regression model that predicts prices. The feature_importance_overweight rule is used to check if some features have too much weight in the model, which may not be related to the convergence or resource utilization issues2.
References:
1: Amazon SageMaker Debugger
2: Built-in Rules for Amazon SageMaker Debugger
3: Actions for Amazon SageMaker Debugger
4: Amazon CloudWatch Alarms
5: Amazon CloudWatch Custom Metrics
NEW QUESTION # 258
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