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Amazon MLA-C01 Exam Overview:

Certification Vendor:	Amazon Web Services (AWS)
Exam Name:	AWS Certified Machine Learning Engineer - Associate
Exam Number:	MLA-C01
Passing Score:	720 (scaled score 100–1000)
Available Languages:	Simplified Chinese, Korean, Japanese, English
Related Certifications:	AWS Certified AI Practitioner AWS Certified Machine Learning - Specialty
Exam Duration:	130 minutes
Exam Price:	150 USD
Real Exam Qty:	65 (50 scored, 15 unscored)
Exam Format:	Matching, Multiple response, Case study, Ordering, Multiple choice
Certificate Validity Period:	3 years
Recommended Training:	AWS Training and Certification AWS Certified Machine Learning Engineer - Associate Official Exam Guide
Exam Registration:	AWS Certification Portal Pearson VUE Registration
Sample Questions:	Amazon MLA-C01 Sample Questions
Exam Way:	Online proctored or onsite at Pearson VUE testing centers
Pre Condition:	Recommended: 1+ year hands-on experience with AWS services and machine learning engineering; familiarity with Amazon SageMaker and related ML services. No mandatory prerequisite exams.
Official Syllabus URL:	https://docs.aws.amazon.com/aws-certification/latest/machine-learning-engineer-associate-01/machine-learning-engineer-associate-01.html

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Amazon MLA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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.

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q86-Q91):

NEW QUESTION # 86
A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.
Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

A. Use Amazon Rekognition to analyze sentiments of the chat conversations.
B. Train a Naive Bayes classifier to analyze sentiments of the chat conversations.
C. Use random forests to classify sentiments of the chat conversations.
D. Use Amazon Comprehend to analyze sentiments of the chat conversations.

Answer: D

Explanation:
Amazon Comprehend is a fully managed natural language processing (NLP) service that includes a built-in sentiment analysis feature. It can quickly and efficiently analyze text data to determine whether the sentiment is positive, negative, neutral, or mixed. Using Amazon Comprehend requires minimal setup and provides accurate results without the need to train and deploy custom models, making it the fastest and most efficient solution for this task.

NEW QUESTION # 87
A company is using Amazon SageMaker to create ML models. The company's data scientists need fine-grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications.
Which solution will meet these requirements?

A. Use SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
B. Use AWS CodePipeline and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
C. Use SageMaker Pipelines and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.
D. Use AWS CodePipeline and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.

Answer: A

NEW QUESTION # 88
An ML engineer needs to ensure that a dataset complies with regulations for personally identifiable information (PII). The ML engineer will use the data to train an ML model on Amazon SageMaker instances. SageMaker must not use any of the PII.
Which solution will meet these requirements in the MOST operationally efficient way?

A. Use Amazon Macie for automatic discovery of PII in the data. Remove the PII. Store the data in an Amazon S3 bucket. Mount the S3 bucket to the SageMaker instances for model training.
B. Use the Amazon Comprehend DetectPiiEntities API call to redact the PII from the data. Store the data in an Amazon Elastic File System (Amazon EFS) file system. Mount the EFS file system to the SageMaker instances for model training.
C. Use AWS Glue DataBrew to cleanse the dataset of PII. Store the data in an Amazon Elastic File System (Amazon EFS) file system. Mount the EFS file system to the SageMaker instances for model training.
D. Use the Amazon Comprehend DetectPiiEntities API call to redact the PII from the data. Store the data in an Amazon S3 bucket. Access the S3 bucket from the SageMaker instances for model training.

Answer: D

NEW QUESTION # 89
A company needs to develop an ML model. The model must identify an item in an image and must provide the location of the item.
Which Amazon SageMaker algorithm will meet these requirements?

A. XGBoost
B. Object detection
C. K-nearest neighbors (k-NN)
D. Image classification

Answer: B

NEW QUESTION # 90
A company is building an Amazon SageMaker AI pipeline for an ML model. The pipeline uses distributed processing and training.
An ML engineer needs to encrypt network communication between instances that run distributed jobs. The ML engineer configures the distributed jobs to run in a private VPC.
What should the ML engineer do to meet the encryption requirement?

A. Enable inter-container traffic encryption.
B. Enable network isolation.
C. Configure traffic encryption by using security groups.
D. Enable VPC flow logs.

Answer: A

Explanation:
In distributed training and processing jobs, multiple instances and containers communicate with each other over the network to exchange gradients, parameters, and intermediate results. Even when jobs run inside a private VPC, network traffic between instances is not automatically encrypted at the application layer.
AWS documentation for Amazon SageMaker specifies that inter-container traffic encryption is the supported mechanism for encrypting data in transit between containers that participate in distributed training or processing jobs. When enabled, SageMaker uses TLS to encrypt all communication between containers across instances, ensuring confidentiality and compliance with security requirements.
Option A (network isolation) prevents containers from making outbound network calls but does not encrypt traffic between distributed instances. Option B is incorrect because security groups control traffic access, not encryption. Option D (VPC flow logs) is a monitoring feature and does not provide encryption.
Therefore, enabling inter-container traffic encryption is the correct and AWS-recommended solution.

NEW QUESTION # 91
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