Amazon Web Services AIF-C01 Exam With Confidence Using Practice Dumps

Decreasing the number of tokens in the prompt reduces the cost associated with using an LLM model on Amazon Bedrock, as costs are often based on the number of tokens processed by the model.

Token Reduction Strategy:

By decreasing the number of tokens (words or characters) in each prompt, the company reduces the computational load and, therefore, the cost associated with invoking the model.

Since the model is performing well with few-shot prompting, reducing token usage without sacrificing performance can lower monthly costs.

Why Option B is Correct:

Cost Efficiency:Directly reduces the number of tokens processed, lowering costs without requiring additional adjustments.

Maintaining Performance:If the model is already performing well, a reduction in tokens should not significantly impact its performance.

Why Other Options are Incorrect:

A. Fine-tuning:Can be costly and time-consuming and is not needed if the current model is already performing well.

C. Increase the number of tokens:Would increase costs, not lower them.

D. Use Provisioned Throughput:Is unrelated to token costs and applies more to read/write capacity in databases.

Amazon Bedrock Agents provide the capability to automate repetitive tasks and orchestrate complex workflows using generative AI models. This is particularly beneficial for customer support inquiries, where quick and efficient processing is crucial.

Option B (Correct): "Automation of repetitive tasks and orchestration of complex workflows":This is the correct answer because Bedrock Agents can automate common customer service tasks and streamline complex processes, improving response times and efficiency.

Option A:"Generation of custom foundation models (FMs) to predict customer needs" is incorrect as Bedrock agents do not create custom models.

Option C:"Automatically calling multiple foundation models (FMs) and consolidating the results" is incorrect because Bedrock agents focus on task automation rather than combining model outputs.

Option D:"Selecting the foundation model (FM) based on predefined criteria and metrics" is incorrect as Bedrock agents are not designed for selecting models.

AWS AI Practitioner References:

Amazon Bedrock Documentation:AWS explains that Bedrock Agents automate tasks and manage complex workflows, making them ideal for customer support automation.

Sampling bias is the correct type of bias affecting the model output when it disproportionately flags people from a specific ethnic group.

Sampling Bias:

Occurs when the training data is not representative of the broader population, leading to skewed model outputs.

In this case, if the model disproportionately flags people from a specific ethnic group, it likely indicates that the training data was not adequately balanced or representative.

Why Option B is Correct:

Reflects Data Imbalance:A biased sample in the training data could result in unfair outcomes, such as disproportionately flagging a particular group.

Common Issue in ML Models:Sampling bias is a known problem that can lead to unfair or inaccurate model predictions.

Why Other Options are Incorrect:

A. Measurement bias:Involves errors in data collection or measurement, not sampling.

C. Observer bias:Refers to bias introduced by researchers or data collectors, not the model's output.

D. Confirmation bias:Involves favoring information that confirms existing beliefs, not relevant to model output bias.