Important Amazon MLA-C01 Exam Questions

Answer: A

Explanation:

This scenario clearly describes an overfitting problem. The neural network performs well on training data but fails to generalize to evaluation data. According to AWS Machine Learning and deep learning best practices, overfitting occurs when a model learns noise or overly specific patterns in the training data instead of generalizable relationships.

L1 and L2 regularization are well-established techniques to combat overfitting. They work by penalizing large weight values during optimization, effectively constraining the model's complexity. L1 regularization encourages sparsity, while L2 regularization (weight decay) smooths the weight distribution. AWS documentation recommends regularization as a primary method for improving generalization when a model shows a large gap between training and evaluation performance.

Option B is incorrect because removing dropout layers would increase overfitting, not reduce it. Dropout is itself a regularization technique.

Option C would likely worsen overfitting, as training for more epochs allows the model to memorize the training data even further.

Option D increases model complexity, which again exacerbates overfitting rather than resolving it.

Therefore, penalizing large weights using L1 or L2 regularization is the correct solution.

Answer: A, B

Explanation:

The described behavior indicates overfitting, where the model starts to memorize training data instead of generalizing.

Early stopping halts training when validation performance stops improving, preventing the model from overfitting further. AWS documentation recommends early stopping as a primary regularization technique.

Dropout randomly disables neurons during training, forcing the model to learn robust representations and reducing reliance on specific neurons. Increasing dropout is a well-established method for improving generalization.

Increasing layers or neurons increases model capacity and worsens overfitting. Model bias is unrelated to epoch-based degradation.

Therefore, Options A and B are correct.

Answer: A

Explanation:

In regression problems such as house price prediction, extreme values can significantly distort model learning. In this dataset, the presence of a large mansion and an extremely small apartment represents clear outliers in the Square Meters feature. According to AWS Machine Learning best practices, outliers can disproportionately influence loss functions (such as mean squared error), leading to poor predictions for the majority of typical data points.

Removing these outliers helps the model focus on learning patterns that apply to the majority of houses and apartments, which aligns with the requirement to produce accurate predictions for typical properties. After removing outliers, applying a log transformation to the Square Meters feature further improves model performance by reducing skewness and stabilizing variance. Log transformations are commonly recommended in AWS and general ML documentation when numerical features span multiple orders of magnitude.

Option B is incorrect because normalization alone does not address the undue influence of extreme outliers. Option C and D are incorrect because one-hot encoding is intended for categorical variables, not continuous numerical features such as square meters.

Therefore, removing outliers and applying a log transformation is the most statistically sound preprocessing approach.

Answer: A

Explanation:

Amazon Kendra Experience Builder provides a fully managed, low-code solution for building conversational search and question-answering applications. AWS documentation states that Kendra natively supports semantic search, vector embeddings, and vector-based retrieval, making it well suited for RAG-style applications with minimal development effort.

When integrated with Amazon Bedrock, Kendra can act as the retrieval layer, handling document ingestion, indexing, embedding generation, and relevance ranking automatically. This eliminates the need to manually manage embedding models, vector databases, and search logic.

Options B and C require custom schema design, vector indexing, query logic, and operational management of PostgreSQL instances. Although pgvector supports vector search, it significantly increases development and maintenance effort. Option D is unrelated to vector search and is used only for metadata cataloging.

AWS explicitly positions Amazon Kendra as the fastest way to build enterprise-grade conversational assistants that integrate with foundation models.

Therefore, Option A is the correct and most AWS-aligned solution.

Answer: A

Explanation:

Amazon SageMaker Asynchronous Inference is specifically designed for long-running inference requests and large payloads. It supports payload sizes up to 1 GB and processing times of up to 1 hour, while automatically queuing requests.

Asynchronous inference stores results in Amazon S3 and allows clients to retrieve inference outputs after processing completes. It also supports auto scaling down to zero when there are no incoming requests, reducing cost.

Batch transform is intended for offline, bulk inference and does not return per-request results in an asynchronous request--response pattern. Serverless and real-time inference have strict payload size and timeout limits that do not support 1-hour processing.

Therefore, asynchronous inference is the only SageMaker inference option that meets all stated requirements.