Mastering the AWS Certified AI Practitioner Exam

Important Note: while official practice exams for the exam haven’t been published yet, we built these practice exams based on the same tone and difficulty we are used to in other AWS certifications. The topics tested are those we believe AWS will test you on at the exam. We will of course adjust the practice exams in the future to reflect any changes

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 by Oussama El Berhichi 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 a warm-up practice exam and TWO high-quality FULL-LENGTH practice exams to be ready for your certification

Quality speaks for itself:

SAMPLE QUESTION:

Which of the following are valid model customization methods for Amazon Bedrock? (Select two)

1. Continued Pre-training

2. Fine-tuning

3. Retrieval Augmented Generation (RAG)

4. Zero-shot prompting

5. Chain-of-thought prompting

What’s your guess? Scroll below for the answer.

Correct: 1,2

Explanation:

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-training

In 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-tuning

While 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.

Incorrect 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 prompting

Chain-of-thought prompting

Prompt 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 documentation