|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/practice-exams-aws-certified-ai-practitioner/
课程评论：没有评论
课程名称：[模拟考试] AWS 认证 AI 从业者 - AIF-C01 课程概述：准备AWS认证AI从业者AIF-C01考试？本课程提供了模拟考试，帮助您提升通过考试的信心和能力。这些模拟考试由Stephane Maarek和Abhishek Singh联合创作，他们拥有丰富的AWS认证经验，共同通过了18个AWS认证。问题的风格和内容模拟真实考试，同时考试说明中提供了详细描述和“考试提醒”，并广泛引用了AWS文档，以帮助您全面了解AIF-C01考试的各个知识领域。我们希望您将本课程视为最终的“加油站”，帮助您自信满满地跨越胜利的终点，取得AWS认证！相信我们的过程，您在好手中。 您将获得四套高质量的完整模拟考试，以帮助您为认证做好准备。 课程特点： - 样题示例：题目涉及Amazon Bedrock的有效模型定制方法，提供详细解释与参考链接，帮助学员深入理解相关概念。 - 每道题目都有详细解释，帮助您理解错题，并提升知识水平。 - 可在移动设备上学习，方便随时随地复习。 - 如果您对课程不满意，提供30天退款保证。 讲师介绍：课程由Stephane Maarek教授主讲，专注于云计算和AWS认证，帮助学生提升AWS专业能力。与他的合讲师Abhishek Singh一同，为您提供最佳的模拟考试体验。 课程目标：该课程旨在通过原创的题库和持续的支持，帮助学员多次练习，并有效准备AWS认证AI从业者考试。祝您学习愉快，备考顺利！
Preparing for AWS Certified AI Practitioner AIF-C01? This is THE practice exams course to give you the winning edge.These practice exams have been co-authored by Stephane Maarek and Abhishek Singh who bring their collective experience of passing 18 AWS Certifications to the table.The tone and tenor of the questions mimic the real exam. Along with the detailed description and "exam alert" provided within the explanations, we have also extensively referenced AWS documentation to get you up to speed on all domain areas being tested for the AIF-C01 exam.We want you to think of this course as the final pit-stop so that you can cross the winning line with absolute confidence and get AWS Certified! Trust our process, you are in good hands.All questions have been written from scratch! You will get FOUR high-quality FULL-LENGTH practice exams to be ready for your certificationQuality speaks for itself:SAMPLE QUESTION:Which of the following are valid model customization methods for Amazon Bedrock? (Select two)1. Continued Pre-training2. Fine-tuning3. Retrieval Augmented Generation (RAG)4. Zero-shot prompting5. Chain-of-thought promptingWhat's your guess? Scroll below for the answer.Correct: 1,2Explanation:Correct options:Model customization involves further training and changing the weights of the model to enhance its performance. You can use continued pre-training or fine-tuning for model customization in Amazon Bedrock.Continued Pre-trainingIn the continued pre-training process, you provide unlabeled data to pre-train a foundation model by familiarizing it with certain types of inputs. You can provide data from specific topics to expose a model to those areas. The Continued Pre-training process will tweak the model parameters to accommodate the input data and improve its domain knowledge.For example, you can train a model with private data, such as business documents, that are not publicly available for training large language models. Additionally, you can continue to improve the model by retraining the model with more unlabeled data as it becomes available.Fine-tuningWhile fine-tuning a model, you provide labeled data to train a model to improve performance on specific tasks. By providing a training dataset of labeled examples, the model learns to associate what types of outputs should be generated for certain types of inputs. The model parameters are adjusted in the process and the model's performance is improved for the tasks represented by the training dataset.Model customization - reference imagevia - reference linkBenefits of model customization - reference imagevia - reference linkIncorrect options:Retrieval Augmented Generation (RAG)Retrieval Augmented Generation (RAG) allows you to customize a model's responses when you want the model to consider new knowledge or up-to-date information. When your data changes frequently, like inventory or pricing, it's not practical to fine-tune and update the model while it's serving user queries. To equip the FM with up-to-date proprietary information, organizations turn to RAG, a technique that involves fetching data from company data sources and enriching the prompt with that data to deliver more relevant and accurate responses. RAG is not a model customization method.Zero-shot promptingChain-of-thought promptingPrompt engineering is the practice of carefully designing prompts to efficiently tap into the capabilities of FMs. It involves the use of prompts, which are short pieces of text that guide the model to generate more accurate and relevant responses. With prompt engineering, you can improve the performance of FMs and make them more effective for a variety of applications. Prompt engineering has techniques such as zero-shot and few-shot prompting, which rapidly adapts FMs to new tasks with just a few examples, and chain-of-thought prompting, which breaks down complex reasoning into intermediate steps.Prompt engineering is not a model customization method. Therefore, both these options are incorrect.With multiple reference links from AWS documentationInstructorMy name is Stéphane Maarek, I am passionate about Cloud Computing, and I will be your instructor in this course. I teach about AWS certifications, focusing on helping my students improve their professional proficiencies in AWS.I have already taught 2,500,000+ students and gotten 800,000+ reviews throughout my career in designing and delivering these certifications and courses!I'm delighted to welcome Abhishek Singh as my co-instructor for these practice exams!Welcome to the best practice exams to help you prepare for your AWS Certified AI Practitioner exam.You can retake the exams as many times as you wantThis is a huge original question bankYou get support from instructors if you have questionsEach question has a detailed explanationMobile-compatible with the Udemy app30-days money-back guarantee if you're not satisfiedWe hope that by now you're convinced! And there are a lot more questions inside the course.Happy learning and best of luck for your AWS Certified AI Practitioner exam!
