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Amazon MLA-C01 Exam Overview:

Certification Vendor:Amazon Web Services (AWS)
Exam Name:AWS Certified Machine Learning Engineer – Associate (MLA-C01)
Exam Number:MLA-C01
Related Certifications:AWS Certified AI Practitioner
AWS Certified Data Engineer – Associate
AWS Certified DevOps Engineer – Professional
AWS Certified Solutions Architect – Associate
Exam Duration:130 minutes
Passing Score:720/1000
Real Exam Qty:65 scored questions + 15 unscored questions
Available Languages:English, Japanese, Simplified Chinese, Korean
Exam Format:Multiple response, Multiple choice, Matching, Ordering
Exam Price:USD 150
Certificate Validity Period:3 years
Recommended Training:AWS Skill Builder - ML Engineer Associate Exam Prep
Amazon SageMaker Documentation
Exam Registration:AWS Certification Registration
Sample Questions:Amazon MLA-C01 Sample Questions
Exam Way:Online proctored or test center exam
Pre Condition:Recommended: ~1 year experience with Amazon SageMaker and AWS-based ML or data engineering roles
Official Syllabus URL:https://aws.amazon.com/certification/certified-machine-learning-engineer-associate/

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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
  • 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.
Topic 4
  • 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.

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q49-Q54):

NEW QUESTION # 49
An ML engineer is using Amazon SageMaker to train a deep learning model that requires distributed training.
After some training attempts, the ML engineer observes that the instances are not performing as expected. The ML engineer identifies communication overhead between the training instances.
What should the ML engineer do to MINIMIZE the communication overhead between the instances?

Answer: A

Explanation:
To minimize communication overhead during distributed training:
1. Same VPC Subnet: Ensures low-latency communication between training instances by keeping the network traffic within a single subnet.
2. Same AWS Region and Availability Zone: Reduces network latency further because cross-AZ communication incurs additional latency and costs.
3. Data in the Same Region and AZ: Ensures that the training data is accessed with minimal latency, improving performance during training.
This configuration optimizes communication efficiency and minimizes overhead.


NEW QUESTION # 50
A company needs to create a central catalog for all the company's ML models. The models are in AWS accounts where the company developed the models initially. The models are hosted in Amazon Elastic Container Registry (Amazon ECR) repositories.
Which solution will meet these requirements?

Answer: D


NEW QUESTION # 51
A company is setting up a system to manage all of the datasets it stores in Amazon S3. The company would like to automate running transformation jobs on the data and maintaining a catalog of the metadata concerning the datasets. The solution should require the least amount of setup and maintenance.
Which solution will allow the company to achieve its goals?

Answer: B

Explanation:
AWS Glue is the correct answer because this option requires the least amount of setup and maintenance since it is serverless, and it does not require management of the infrastructure.


NEW QUESTION # 52
An ML engineer is training a simple neural network model. The ML engineer tracks the performance of the model over time on a validation dataset. The model's performance improves substantially at first and then degrades after a specific number of epochs.
Which solutions will mitigate this problem? (Choose two.)

Answer: A,B

Explanation:
Early stopping halts training once the performance on the validation dataset stops improving. This prevents the model from overfitting, which is likely the cause of performance degradation after a certain number of epochs.
Dropout is a regularization technique that randomly deactivates neurons during training, reducing overfitting by forcing the model to generalize better. Increasing dropout can help mitigate the problem of performance degradation due to overfitting.


NEW QUESTION # 53
An ML engineer is building a logistic regression model to predict customer churn for subscription services.
The dataset contains two string variables: location and job_seniority_level.
The location variable has 3 distinct values, and the job_seniority_level variable has over 10 distinct values.
The ML engineer must perform preprocessing on the variables.
Which solution will meet this requirement?

Answer: B

Explanation:
Logistic regression requires numeric input features and is sensitive to how categorical variables are encoded.
AWS feature engineering best practices recommend one-hot encoding for low-cardinality categorical variables with no inherent order and ordinal encoding for categorical variables with a meaningful order.
The location feature has only three distinct values and no ordinal relationship, making one-hot encoding the most appropriate method. This prevents the model from inferring a false numerical relationship between locations.
The job_seniority_level feature typically has an inherent order (for example: junior, mid-level, senior, lead).
Even with more than 10 categories, ordinal encoding preserves this natural hierarchy while keeping the feature dimensionality manageable.
Tokenization is used for unstructured text, not structured categorical variables. Standard scaling applies only to continuous numeric features and is not suitable for categorical string variables.
AWS documentation explicitly highlights using one-hot encoding for nominal features and ordinal encoding for ordered categorical features when preparing data for linear models such as logistic regression.
Therefore, Option B is the correct and AWS-aligned solution.


NEW QUESTION # 54
......

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