A company is integrating AI into its employee recruitment and hiring solution. The company wants to mitigate bias risks and ensure responsible AI practices while prioritizing equitable hiring decisions.Which core dimensions of responsible AI should the company consider? (Choose two.)
Answer(s): A,E
Fairness - Ensuring equitable outcomes across all demographic groups helps mitigate bias risks in hiring decisions.Transparency - Providing clear explanations of how the AI makes decisions builds trust and allows auditing for bias.
A company needs to monitor the performance of its ML systems by using a highly scalable AWS service.Which AWS service meets these requirements?
Answer(s): A
Amazon CloudWatch is a highly scalable AWS service designed for monitoring and observability. It provides real-time monitoring of system metrics, including performance data for ML systems, such as resource utilization(CPU, memory, etc.), model inference latency, request counts, and errors. CloudWatch enables users to set up alarms, visualize metrics, and automate actions based on performance thresholds, making it ideal for monitoring the performance of ML systems.
An AI practitioner must fine-tune an open source large language model (LLM) for text categorization. The dataset is already prepared.Which solution will meet these requirements with the LEAST operational effort?
Answer(s): B
SageMaker JumpStart provides prebuilt solutions and workflows for fine-tuning open source models with minimal setup. It reduces operational effort compared to custom scripts, EC2 notebooks, or PartyRock, making it the most efficient choice.
Which statement describes a generative AI use case for multimodal models?
Answer(s): D
Multimodal generative AI models are designed to process and reason across multiple data types such as images, audio, video, and text within a single model.
A company wants to use large language models (LLMs) to produce code from natural language code comments.Which LLM feature meets these requirements?
Text generation is the feature of large language models (LLMs) that enables them to produce text based on a given prompt or input. In this scenario, the input would be natural language code comments, and the LLMwould generate code based on those comments. This process involves understanding the context of the comment and generating corresponding code, which is the core functionality of text generation.
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