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Amazon MLA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • ML Model Development: This section of the exam measures skills of Fraud Examiners and covers choosing and training machine learning models to solve business problems such as fraud detection. It includes selecting algorithms, using built-in or custom models, tuning parameters, and evaluating performance with standard metrics. The domain emphasizes refining models to avoid overfitting and maintaining version control to support ongoing investigations and audit trails.
Topic 2
  • ML Solution Monitoring, Maintenance, and Security: This section of the exam measures skills of Fraud Examiners and assesses the ability to monitor machine learning models, manage infrastructure costs, and apply security best practices. It includes setting up model performance tracking, detecting drift, and using AWS tools for logging and alerts. Candidates are also tested on configuring access controls, auditing environments, and maintaining compliance in sensitive data environments like financial fraud detection.
Topic 3
  • Deployment and Orchestration of ML Workflows: This section of the exam measures skills of Forensic Data Analysts and focuses on deploying machine learning models into production environments. It covers choosing the right infrastructure, managing containers, automating scaling, and orchestrating workflows through CI
  • CD pipelines. Candidates must be able to build and script environments that support consistent deployment and efficient retraining cycles in real-world fraud detection systems.
Topic 4
  • Data Preparation for Machine Learning (ML): This section of the exam measures skills of Forensic Data Analysts and covers collecting, storing, and preparing data for machine learning. It focuses on understanding different data formats, ingestion methods, and AWS tools used to process and transform data. Candidates are expected to clean and engineer features, ensure data integrity, and address biases or compliance issues, which are crucial for preparing high-quality datasets in fraud analysis contexts.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q40-Q45):

NEW QUESTION # 40
A company has an Amazon S3 bucket that contains 1 TB of files from different sources. The S3 bucket contains the following file types in the same S3 folder: CSV, JSON, XLSX, and Apache Parquet.
An ML engineer must implement a solution that uses AWS Glue DataBrew to process the data.
The ML engineer also must store the final output in Amazon S3 so that AWS Glue can consume the output in the future.
Which solution will meet these requirements?

Answer: C


NEW QUESTION # 41
An ML engineer needs to use AWS services to identify and extract meaningful unique keywords from documents.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: B


NEW QUESTION # 42
A company wants to improve the sustainability of its ML operations.
Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

Answer: B,C


NEW QUESTION # 43
An ML engineer is deploying a trained model to an Amazon SageMaker endpoint. The ML engineer needs to receive alerts when data quality issues occur in production. Which solution will meet this requirement?

Answer: C

Explanation:
SageMaker Model Monitor is designed to continuously monitor deployed models for data quality issues such as data drift or violations of data constraints. By integrating Model Monitor with Amazon CloudWatch, alerts can be automatically triggered when data quality issues occur in production.


NEW QUESTION # 44
An ML engineer is building a model to predict house and apartment prices. The model uses three features:
Square Meters, Price, and Age of Building. The dataset has 10,000 data rows. The data includes data points for one large mansion and one extremely small apartment.
The ML engineer must perform preprocessing on the dataset to ensure that the model produces accurate predictions for the typical house or apartment.
Which solution will meet these requirements?

Answer: C

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.


NEW QUESTION # 45
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