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Amazon MLA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Data Preparation for Machine Learning (ML)	28%	- Data ingestion and collection 1. Data acquisition from structured and unstructured sources 2. Batch and streaming ingestion using AWS services (e.g., S3, Kinesis, Glue) - Data preprocessing and transformation 1. Data cleaning, normalization, and feature engineering 2. Data validation and quality checks
Deployment and Orchestration of ML Workflows	22%	- ML pipeline orchestration 1. Workflow automation using AWS services 2. CI/CD pipelines for ML workflows - Model deployment 1. Real-time and batch inference endpoints 2. Auto scaling and infrastructure configuration
ML Model Development	26%	- Model tuning and evaluation 1. Hyperparameter tuning 2. Performance evaluation and metrics interpretation - Model selection and training 1. Choosing appropriate ML algorithms 2. Training models using Amazon SageMaker
ML Solution Monitoring, Maintenance, and Security	24%	- Monitoring and observability 1. Model drift detection and performance monitoring 2. Infrastructure and data monitoring - Security and governance 1. IAM policies and access control 2. Compliance and secure ML system design

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q147-Q152):

NEW QUESTION # 147
A company has an ML model that generates text descriptions based on images that customers upload to the company's website. The images can be up to 50 MB in total size.
An ML engineer decides to store the images in an Amazon S3 bucket. The ML engineer must implement a processing solution that can scale to accommodate changes in demand.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create an Amazon Elastic Kubernetes Service (Amazon EKS) cluster that uses Karpenter for auto scaling. Host the model on the EKS cluster. Run a script to make an inference request for each image.
B. Create an Amazon SageMaker Asynchronous Inference endpoint and a scaling policy. Run a script to make an inference request for each image.
C. Create an AWS Batch job that uses an Amazon Elastic Container Service (Amazon ECS) cluster.Specify a list of images to process for each AWS Batch job.
D. Create an Amazon SageMaker batch transform job to process all the images in the S3 bucket.

Answer: B

Explanation:
SageMaker Asynchronous Inference is designed for processing large payloads, such as images up to 50 MB, and can handle requests that do not require an immediate response.
It scales automatically based on the demand, minimizing operational overhead while ensuring cost-efficiency.
A script can be used to send inference requests for each image, and the results can be retrieved asynchronously. This approach is ideal for accommodating varying levels of traffic with minimal manual intervention.

NEW QUESTION # 148
An ML engineer is training an ML model to identify people's health risk based on 20 features and
1 target. The target class has two values:
- Likely to have health risk (positive class)
- Unlikely to have health risk (negative class)
The age range of people in the dataset is 30 years old to 60 years old. Age is one of the features.
The ML engineer analyzes the features. For the positive class, the difference in proportions of labels (DPL) value is (+0.9) for the age range of 40 to 45 compared with all other age ranges.
What should the ML engineer do to correct this data imbalance?

A. Oversample the positive class for the age range of 40 to 45.
B. Oversample the negative class for all age ranges except 40 to 45.
C. Undersample the positive class for all age ranges except 40 to 45.
D. Undersample the positive class for the age range of 40 to 45.

Answer: D

Explanation:
A DPL of +0.9 indicates that the positive class is heavily overrepresented in the 40-45 age range compared to other age ranges. To correct this imbalance, the solution is to undersample the positive class within the 40-45 range, reducing its dominance and improving fairness in the dataset.

NEW QUESTION # 149
An ML engineer at a credit card company built and deployed an ML model by using Amazon SageMaker AI.
The model was trained on transaction data that contained very few fraudulent transactions. After deployment, the model is underperforming.
What should the ML engineer do to improve the model's performance?

A. Use Synthetic Minority Oversampling Technique (SMOTE) to generate synthetic minority samples and retrain the model.
B. Use random undersampling to reduce the majority class and retrain the model.
C. Use random oversampling to duplicate minority samples and retrain the model.
D. Retrain the model with a different SageMaker built-in algorithm.

Answer: A

Explanation:
This is a classic class imbalance problem, where fraudulent transactions (minority class) are severely underrepresented. AWS documentation for SageMaker Data Wrangler recommends SMOTE (Synthetic Minority Oversampling Technique) as an effective approach for improving model performance in such scenarios.
SMOTE generates synthetic minority samples by interpolating between existing minority class examples.
This improves the model's ability to learn decision boundaries without simply duplicating data, which can cause overfitting.
Random undersampling removes valuable majority class data, reducing overall model robustness. Random oversampling duplicates data and increases overfitting risk. Changing algorithms does not address the root cause.
AWS best practices highlight SMOTE as the preferred technique for fraud detection and other highly imbalanced datasets.
Therefore, Option C is the correct and AWS-verified answer.

NEW QUESTION # 150
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. 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.
B. 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.
C. 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.
D. Manually import and merge the datasets. Consolidate the datasets into a single data frame. Use Amazon SageMaker data labeling to prepare the data.

Answer: B

NEW QUESTION # 151
A medical company needs to store clinical data. The data includes personally identifiable information (PII) and protected health information (PHI).
An ML engineer needs to implement a solution to ensure that the PII and PHI are not used to train ML models.
Which solution will meet these requirements?

A. Create an AWS Lambda function to encrypt the PII and PHI. Program the Lambda function to save the encrypted data to an Amazon S3 bucket for model training.
B. Upload the clinical data to an Amazon Redshift database. Use built-in SQL stored procedures to automatically classify and mask the PII and PHI before the data is used for model training.
C. Store the clinical data in Amazon S3 buckets. Use AWS Glue DataBrew to mask the PII and PHI before the data is used for model training.
D. Use Amazon Comprehend to detect and mask the PII before the data is used for model training.
Use Amazon Comprehend Medical to detect and mask the PHI before the data is used for model training.

Answer: D

NEW QUESTION # 152
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