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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
Passing Score:720/1000
Exam Format:Matching, Ordering, Multiple response, Multiple choice
Exam Price:USD 150
Exam Duration:130 minutes
Certificate Validity Period:3 years
Related Certifications:AWS Certified DevOps Engineer – Professional
AWS Certified Solutions Architect – Associate
AWS Certified AI Practitioner
AWS Certified Data Engineer – Associate
Real Exam Qty:65 scored questions + 15 unscored questions
Available Languages:English, Japanese, Korean, Simplified Chinese
Recommended Training:Amazon SageMaker Documentation
AWS Skill Builder - ML Engineer Associate Exam Prep
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
  • 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
  • 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 3
  • 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 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 (Q85-Q90):

NEW QUESTION # 85
A company needs to use Retrieval Augmented Generation (RAG) to supplement an open source large language model (LLM) that runs on Amazon Bedrock. The company's data for RAG is a set of documents in an Amazon S3 bucket. The documents consist of .csv files and .docx files.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: C


NEW QUESTION # 86
A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon 53 to provide customers with a live conversational engine.
The model is using sensitive data. An ML engineer needs to implement a solution to identify and remove the sensitive data.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: C

Explanation:
Amazon Macie is a fully managed data security and privacy service that uses machine learning to discover and classify sensitive data in Amazon S3. It is purpose-built to identify sensitive data with minimal operational overhead. After identifying the sensitive data, you can use AWS Lambda functions to automate the process of removing or redacting the sensitive data, ensuring efficiency and integration with the hybrid cloud environment. This solution requires the least development effort and aligns with the requirement to handle sensitive data effectively.


NEW QUESTION # 87
An ML engineer must choose the appropriate Amazon SageMaker algorithm to solve specific AI problems.
Select the correct SageMaker built-in algorithm from the following list for each use case. Each algorithm should be selected one time.
* Random Cut Forest (RCF) algorithm
* Semantic segmentation algorithm
* Sequence-to-Sequence (seq2seq) algorithm

Answer:

Explanation:

Explanation:
Use case 1:
Summarize the text of a research paper
## Sequence-to-Sequence (seq2seq) algorithm
Why:
Seq2seq models are designed for natural language generation tasks such as text summarization, translation, and paraphrasing. AWS documentation explicitly lists text summarization as a primary use case for the SageMaker seq2seq algorithm.
Use case 2:
Scan every pixel of an image to help self-driving cars identify objects in their path
## Semantic segmentation algorithm
Why:
Semantic segmentation performs pixel-level classification, assigning a class label to every pixel in an image.
This is exactly what is required for applications such as autonomous driving, road scene understanding, and object boundary detection.
Use case 3:
Identify abnormal data points in a dataset
## Random Cut Forest (RCF) algorithm
Why:
Random Cut Forest is an unsupervised anomaly detection algorithm. AWS SageMaker RCF is purpose-built to identify outliers, unusual patterns, and anomalies in numerical datasets, making it ideal for fraud detection, monitoring, and abnormal data point detection.


NEW QUESTION # 88
A gaming company needs to deploy a natural language processing (NLP) model to moderate a chat forum in a game. The workload experiences heavy usage during evenings and weekends but minimal activity during other hours.
Which solution will meet these requirements MOST cost-effectively?

Answer: B

Explanation:
The key requirements in this scenario are variable traffic patterns and cost efficiency. The workload has unpredictable spikes during evenings and weekends, followed by long periods of low or no usage. According to AWS Machine Learning documentation, Amazon SageMaker Serverless Inference is specifically designed for such use cases.
SageMaker Serverless Inference automatically provisions, scales, and shuts down compute resources based on incoming inference requests. Customers are billed only for the compute time used during inference, not for idle resources. This makes it highly cost-effective for workloads with intermittent or spiky traffic, such as real- time chat moderation in gaming environments.
Option A is incorrect because batch transform jobs are intended for offline, large-scale inference and require fixed capacity during job execution. They are not suitable for real-time NLP moderation.
Option C is also incorrect because reserving an EC2 GPU instance incurs continuous costs regardless of utilization. This would be inefficient given the long idle periods described in the scenario.
Option D, SageMaker Asynchronous Inference, is designed for workloads with long processing times or large payloads and still requires endpoint provisioning. While it can handle traffic spikes, it does not scale down to zero in the same cost-efficient manner as Serverless Inference.
Therefore, Amazon SageMaker Serverless Inference is the most cost-effective and operationally efficient solution for deploying an NLP moderation model with highly variable usage patterns.


NEW QUESTION # 89
A company has used Amazon SageMaker to deploy a predictive ML model in production. The company is using SageMaker Model Monitor on the model. After a model update, an ML engineer notices data quality issues in the Model Monitor checks.
What should the ML engineer do to mitigate the data quality issues that Model Monitor has identified?

Answer: B


NEW QUESTION # 90
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