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NEW QUESTION # 242
An ML engineer is building a model to predict house and apartment prices. The model uses three features:
Square Meters, Price, and Age of Building. The dataset has 10,000 data rows. The data includes data points for one large mansion and one extremely small apartment.
The ML engineer must perform preprocessing on the dataset to ensure that the model produces accurate predictions for the typical house or apartment.
Which solution will meet these requirements?
Answer: D
Explanation:
In regression problems such as house price prediction, extreme values can significantly distort model learning.
In this dataset, the presence of a large mansion and an extremely small apartment represents clear outliers in the Square Meters feature. According to AWS Machine Learning best practices, outliers can disproportionately influence loss functions (such as mean squared error), leading to poor predictions for the majority of typical data points.
Removing these outliers helps the model focus on learning patterns that apply to the majority of houses and apartments, which aligns with the requirement to produce accurate predictions for typical properties. After removing outliers, applying a log transformation to the Square Meters feature further improves model performance by reducing skewness and stabilizing variance. Log transformations are commonly recommended in AWS and general ML documentation when numerical features span multiple orders of magnitude.
Option B is incorrect because normalization alone does not address the undue influence of extreme outliers.
Option C and D are incorrect because one-hot encoding is intended for categorical variables, not continuous numerical features such as square meters.
Therefore, removing outliers and applying a log transformation is the most statistically sound preprocessing approach.
NEW QUESTION # 243
A company is creating an application that will recommend products for customers to purchase. The application will make API calls to Amazon Q Business. The company must ensure that responses from Amazon Q Business do not include the name of the company's main competitor.
Which solution will meet this requirement?
Answer: A
Explanation:
Amazon Q Business allows configuring blocked phrases to exclude specific terms or phrases from the responses. By adding the competitor's name as a blocked phrase, the company can ensure that it will not appear in the API responses, meeting the requirement efficiently with minimal configuration.
NEW QUESTION # 244
An airline company deploys ML models to one dozen Amazon SageMaker Al inference endpoints. The inference endpoints must be able to handle different types of workloads in a cost-effective way.
Select the correct inference option from the following list to handle each type of workload. Select each inference option one time. (Select FOUR.)
* Asynchronous inference
* Batch inference
* Real-time inference
* Serverless inference
Answer:
Explanation:
* Provide flight departure, arrival, and delay information, and provide updates for low-latency workloads# Real-time inference
* Advertise holiday travel promotional deals to millions of users in multiple markets before holiday seasons for spiky workloads# Serverless inference
* Generate quarterly and annual flight reports and insights for trend analysis of large datasets# Batch inference
* Generate online image and audio stories for passengers to watch or listen to while waiting at an airport# Asynchronous inference
* The correct mapping depends on latency requirement, traffic pattern, payload size, processing duration, and whether the workload needs a persistent endpoint.
* Real-time inference is the right choice for flight departure, arrival, and delay updates because this is an online user-facing workload that requires low latency. AWS states that SageMaker real-time inference is ideal for online inference workloads with low-latency or high-throughput requirements and uses a persistent fully managed endpoint. That fits flight status information because passengers and airline systems expect immediate responses.
* Serverless inference is the best choice for holiday promotional deals because this traffic is spiky, seasonal, and unpredictable. AWS describes SageMaker Serverless Inference as suitable for intermittent or unpredictable traffic patterns. It is cost-effective because SageMaker manages the infrastructure and scales down when there are no requests, so the company does not pay for idle endpoint capacity.
* Batch inference is correct for quarterly and annual flight reports because this workload analyzes large datasets offline and does not need an always-running endpoint. AWS says SageMaker batch transform is used to get inferences from large datasets and when a persistent endpoint is not required. Reports and trend analysis are scheduled, non-real-time analytics workloads, so batch inference is the most cost- effective option.
* Asynchronous inference is the right choice for generating online image and audio stories. These requests can have larger payloads and longer processing times than normal low-latency API calls. AWS states that SageMaker Asynchronous Inference queues incoming requests and is ideal for large payloads, long processing times, and near-real-time latency requirements. Image and audio generation can take seconds or minutes, so asynchronous inference is more appropriate than real-time inference.
NEW QUESTION # 245
A company has an ML model that needs to run one time each night to predict stock values. The model input is 3 MB of data that is collected during the current day. The model produces the predictions for the next day. The prediction process takes less than 1 minute to finish running.
How should the company deploy the model on Amazon SageMaker to meet these requirements?
Answer: D
NEW QUESTION # 246
A company needs to run a batch data-processing job on Amazon EC2 instances. The job will run during the weekend and will take 90 minutes to finish running. The processing can handle interruptions. The company will run the job every weekend for the next 6 months.
Which EC2 instance purchasing option will meet these requirements MOST cost-effectively?
Answer: C
NEW QUESTION # 247
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