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NEW QUESTION # 21
A company uses ML models to predict whether transactions are fraudulent. The company needs to identify as many fraudulent transactions as possible. Which evaluation metric should the company use to evaluate the models to meet this requirement?
Answer: A
Explanation:
Option D is correct because the company's primary goal is to identify as many fraudulent transactions as possible . In AWS documentation, recall is defined as TP / (TP + FN), where TP is true positives and FN is false negatives. Recall measures how well a model finds all actual positive cases. In a fraud-detection setting, the "positive" class is the fraudulent transaction, so maximizing recall means minimizing missed fraud cases.
AWS documentation also explains the business tradeoff clearly: a use case that needs to correctly predict as many positive examples as possible should prioritize high recall , even if that means accepting some additional false positives and therefore only moderate precision. That matches this question exactly, because the requirement is not to be most selective or most balanced overall; it is specifically to catch the largest possible number of fraudulent transactions.
The other metrics are less aligned to the stated goal. Precision focuses on how many predicted fraud cases are actually fraud, which is most important when false positives are very costly. F1 score balances precision and recall, but the question does not ask for balance; it asks for finding as many fraudulent transactions as possible. AUC is useful for overall ranking and threshold- independent model discrimination, but it is not the most direct metric for optimizing missed-fraud detection in this scenario. Based on AWS metric definitions, when the cost of missing true positives is the main concern, recall is the best evaluation metric. Therefore, the best verified answer is D .
NEW QUESTION # 22
A healthcare company wants to detect irregularities in patient vital signs that could indicate early signs of a medical condition. The company has an unlabeled dataset that includes patient health records, medication history, and lifestyle changes.
Which algorithm and hyperparameter should the company use to meet this requirement?
Answer: B
Explanation:
Option D is correct because Amazon SageMaker AI Random Cut Forest (RCF) is documented by AWS as an unsupervised algorithm for detecting anomalous data points within a data set . The question describes a healthcare use case where the company wants to detect irregularities in patient vital signs and only has an unlabeled dataset . That is a direct match for anomaly detection with an unsupervised method rather than a supervised classification or regression algorithm. AWS documentation also notes that anomalies can appear as unexpected spikes, breaks in patterns, or otherwise unusual observations.
RCF is a strong fit because it does not require labeled target outcomes to begin identifying unusual observations. AWS explains that RCF assigns an anomaly score to each data point, where higher scores indicate potentially anomalous records. This is especially suitable for early warning scenarios in healthcare, where the goal is to surface unusual combinations of vitals, medication history, and lifestyle changes for further review.
The hyperparameter in the answer, num_trees , is a valid RCF hyperparameter and controls the number of trees used in the forest. In AWS examples and documentation, this is one of the key parameters used when configuring the algorithm. By contrast, XGBoost is generally used for supervised tasks, k-means is for clustering rather than anomaly scoring, and DeepAR is for forecasting time series rather than general anomaly detection across an unlabeled multidimensional dataset.
Therefore, the fully verified AWS-docs answer is D , because Random Cut Forest is the AWS-native unsupervised anomaly detection algorithm best aligned to this healthcare irregularity-detection requirement.
NEW QUESTION # 23
A company has historical data that shows whether customers needed long-term support from company staff. The company needs to develop an ML model to predict whether new customers will require long-term support.
Which modeling approach should the company use to meet this requirement?
Answer: B
NEW QUESTION # 24
An ML engineer needs to use an ML model to predict the price of apartments in a specific location.
Which metric should the ML engineer use to evaluate the model's performance?
Answer: C
NEW QUESTION # 25
A company is building an Amazon SageMaker AI pipeline for an ML model. The pipeline uses distributed processing and training.
An ML engineer needs to encrypt network communication between instances that run distributed jobs. The ML engineer configures the distributed jobs to run in a private VPC.
What should the ML engineer do to meet the encryption requirement?
Answer: B
Explanation:
In distributed training and processing jobs, multiple instances and containers communicate with each other over the network to exchange gradients, parameters, and intermediate results. Even when jobs run inside a private VPC, network traffic between instances is not automatically encrypted at the application layer.
AWS documentation for Amazon SageMaker specifies that inter-container traffic encryption is the supported mechanism for encrypting data in transit between containers that participate in distributed training or processing jobs. When enabled, SageMaker uses TLS to encrypt all communication between containers across instances, ensuring confidentiality and compliance with security requirements.
Option A (network isolation) prevents containers from making outbound network calls but does not encrypt traffic between distributed instances. Option B is incorrect because security groups control traffic access, not encryption. Option D (VPC flow logs) is a monitoring feature and does not provide encryption.
Therefore, enabling inter-container traffic encryption is the correct and AWS-recommended solution.
NEW QUESTION # 26
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