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To become an AWS Certified Machine Learning - Specialty, individuals must have prior experience in ML and have completed relevant training or have equivalent practical experience. AWS Certified Machine Learning - Specialty certification is intended for individuals who have a deep understanding of ML concepts and have hands-on experience building and deploying ML models. Candidates must also be able to demonstrate their ability to work with complex datasets, develop ML models, and deploy them at scale.

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Real AWS-Certified-Machine-Learning-Specialty Question | AWS-Certified-Machine-Learning-Specialty Training Tools

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q164-Q169):

NEW QUESTION # 164
For the given confusion matrix, what is the recall and precision of the model?

A. Recall = 0.84 Precision = 0.8
B. Recall = 0.92 Precision = 0.8
C. Recall = 0.92 Precision = 0.84
D. Recall = 0.8 Precision = 0.92

Answer: B

Explanation:
Recall and precision are two metrics that can be used to evaluate the performance of a classification model.
Recall is the ratio of true positives to the total number of actual positives, which measures how well the model can identify all the relevant cases. Precision is the ratio of true positives to the total number of predicted positives, which measures how accurate the model is when it makes a positive prediction. Based on the confusion matrix in the image, we can calculate the recall and precision as follows:
* Recall = TP / (TP + FN) = 12 / (12 + 1) = 0.92
* Precision = TP / (TP + FP) = 12 / (12 + 3) = 0.8
Where TP is the number of true positives, FN is the number of false negatives, and FP is the number of false positives. Therefore, the recall and precision of the model are 0.92 and 0.8, respectively.

NEW QUESTION # 165
A Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access notebooks, train models, and deploy endpoints. To ensure the best operational performance, the Specialist needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked.
Which services are integrated with Amazon SageMaker to track this information? (Choose two.)

A. AWS Config
B. AWS Trusted Advisor
C. AWS CloudTrail
D. AWS Health
E. Amazon CloudWatch

Answer: C,E

Explanation:
https://aws.amazon.com/sagemaker/faqs/

NEW QUESTION # 166
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. Increase the XGBoost scale_pos_weight parameter to adjust the balance of positive and negative weights.
B. Change the XGBoost eval_metric parameter to optimize based on Area Under the ROC Curve (AUC).
C. Change the XGBoost eval_metric parameter to optimize based on Root Mean Square Error (RMSE).
D. Increase the XGBoost max_depth parameter because the model is currently underfitting the data.
E. Decrease the XGBoost max_depth parameter because the model is currently overfitting the data.

Answer: A,B

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.
XGBoost Parameters - Amazon Machine Learning
Using XGBoost with Amazon SageMaker - AWS Machine Learning Blog

NEW QUESTION # 167
An office security agency conducted a successful pilot using 100 cameras installed at key locations within the main office. Images from the cameras were uploaded to Amazon S3 and tagged using Amazon Rekognition, and the results were stored in Amazon ES. The agency is now looking to expand the pilot into a full production system using thousands of video cameras in its office locations globally. The goal is to identify activities performed by non-employees in real time Which solution should the agency consider?

A. Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Image to detect faces from a collection of known employees and alert when non-employees are detected.
B. Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camera. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection on each stream, and alert when non-employees are detected.
C. Use a proxy server at each local office and for each camera, and stream the RTSP feed to a unique Amazon Kinesis Video Streams video stream. On each stream, use Amazon Rekognition Video and create a stream processor to detect faces from a collection of known employees, and alert when non-employees are detected.
D. Install AWS DeepLens cameras and use the DeepLens_Kinesis_Video module to stream video to Amazon Kinesis Video Streams for each camera. On each stream, run an AWS Lambda function to capture image fragments and then call Amazon Rekognition Image to detect faces from a collection of known employees, and alert when non-employees are detected.

Answer: B

Explanation:
https://aws.amazon.com/blogs/machine-learning/video-analytics-in-the-cloud-and-at-the-edge- with-aws-deeplens-and-kinesis-video-streams/

NEW QUESTION # 168
While working on a neural network project, a Machine Learning Specialist discovers thai some features in the data have very high magnitude resulting in this data being weighted more in the cost function What should the Specialist do to ensure better convergence during backpropagation?

A. Model regulanzation
B. Data augmentation for the minority class
C. Data normalization
D. Dimensionality reduction

Answer: C

Explanation:
Explanation
Data normalization is a data preprocessing technique that scales the features to a common range, such as [0, 1] or [-1, 1]. This helps reduce the impact of features with high magnitude on the cost function and improves the convergence during backpropagation. Data normalization can be done using different methods, such as min-max scaling, z-score standardization, or unit vector normalization. Data normalization is different from dimensionality reduction, which reduces the number of features; model regularization, which adds a penalty term to the cost function to prevent overfitting; and data augmentation, which increases the amount of data by creating synthetic samples. References:
Data processing options for AI/ML | AWS Machine Learning Blog
Data preprocessing - Machine Learning Lens
How to Normalize Data Using scikit-learn in Python
Normalization | Machine Learning | Google for Developers

NEW QUESTION # 169
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