Free Access to Amazon.MLA-C01.v2026-04-16.q120 with Valid Practice Test (Page 2)

Question 1

A company is developing a generative AI conversational interface to assist customers with payments. The company wants to use an ML solution to detect customer intent. The company does not have training data to train a model.
Which solution will meet these requirements?

A.Fine-tune a sequence-to-sequence (seq2seq) algorithm in Amazon SageMaker JumpStart.
B.Use an LLM from Amazon Bedrock with zero-shot learning.
C.Use the Amazon Comprehend DetectEntities API.
D.Run an LLM from Amazon Bedrock on Amazon EC2 instances.

Correct Answer: B

The key requirement in this scenario is detecting customer intent without having any training data. According to AWS Machine Learning and Generative AI documentation, zero-shot learning is specifically designed for situations where labeled training data is unavailable. Zero-shot learning allows a pre-trained large language model (LLM) to perform tasks it has not been explicitly trained on by leveraging its general knowledge and language understanding.
Amazon Bedrock provides fully managed access to foundation models (FMs) and LLMs that support zero- shot and few-shot learning. By using an LLM from Amazon Bedrock, the company can directly infer customer intent from natural language inputs without building, training, or fine-tuning a custom model. This approach is ideal for conversational interfaces where rapid deployment and scalability are required.
Option A is incorrect because fine-tuning a sequence-to-sequence (seq2seq) model in Amazon SageMaker JumpStart still requires labeled training data. Since the company explicitly does not have training data, this option does not meet the requirement.
Option C is also incorrect because the Amazon Comprehend DetectEntities API is designed for named entity recognition (NER), such as detecting names, dates, locations, or monetary values. It does not perform intent detection and is not suitable for conversational AI intent classification.
Option D is partially misleading. While it is technically possible to run an LLM on Amazon EC2, this does not inherently solve the problem of intent detection without training data. Additionally, Amazon Bedrock already abstracts infrastructure management, scaling, and model hosting, making direct EC2 deployment unnecessary and less efficient.
Therefore, using an LLM from Amazon Bedrock with zero-shot learning is the most appropriate, scalable, and AWS-recommended solution for intent detection without training data.

Question 2

An ML engineer has trained a neural network by using stochastic gradient descent (SGD). The neural network performs poorly on the test set. The values for training loss and validation loss remain high and show an oscillating pattern. The values decrease for a few epochs and then increase for a few epochs before repeating the same cycle.
What should the ML engineer do to improve the training process?

A.Introduce early stopping.
B.Increase the size of the test set.
C.Increase the learning rate.
D.Decrease the learning rate.

Question 3

An ML engineer receives datasets that contain missing values, duplicates, and extreme outliers.
The ML engineer must consolidate these datasets into a single data frame and must prepare the data for ML.
Which solution will meet these requirements?

A.Manually import and merge the datasets. Consolidate the datasets into a single data frame. Use Amazon Q Developer to generate code snippets that will prepare the data.
B.Manually import and merge the datasets. Consolidate the datasets into a single data frame. Use Amazon SageMaker data labeling to prepare the data.
C.Use Amazon SageMaker Data Wrangler to import the datasets and to consolidate them into a single data frame. Use the cleansing and enrichment functionalities to prepare the data.
D.Use Amazon SageMaker Ground Truth to import the datasets and to consolidate them into a single data frame. Use the human-in-the-loop capability to prepare the data.

Question 4

An ML engineer trained an ML model on Amazon SageMaker to detect automobile accidents from dosed-circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a training dataset of images of accidents and non-accidents.
The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras.
Which solution will improve the model's accuracy in the LEAST amount of time?

A.Recreate the training dataset by using the Data Wrangler resize image transform. Crop all images to the same size.
B.Recreate the training dataset by using the Data Wrangler enhance image contrast transform.
Specify the Gamma contrast option.
C.Recreate the training dataset by using the Data Wrangler corrupt image transform. Specify the impulse noise option.
D.Collect more images from all the cameras. Use Data Wrangler to prepare a new training dataset.

Question 5

A company is developing a customer support AI assistant by using an Amazon Bedrock Retrieval Augmented Generation (RAG) pipeline. The AI assistant retrieves articles from a knowledge base stored in Amazon S3.
The company uses Amazon OpenSearch Service to index the knowledge base. The AI assistant uses an Amazon Bedrock Titan Embeddings model for vector search.
The company wants to improve the relevance of the retrieved articles to improve the quality of the AI assistant's answers.
Which solution will meet these requirements?

A.Use auto-summarization on the retrieved articles by using Amazon SageMaker JumpStart.
B.Use a reranker model before passing the articles to the foundation model (FM).
C.Use Amazon Athena to pre-filter the articles based on metadata before retrieval.
D.Use Amazon Bedrock Provisioned Throughput to process queries more efficiently.

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