Amazon MLA-C01 Exam Dumps - Latest Preparation Material [2026]

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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 Solutions Architect – Associate AWS Certified AI Practitioner AWS Certified DevOps Engineer – Professional AWS Certified Data Engineer – Associate
Passing Score:	720/1000
Exam Format:	Ordering, Matching, Multiple response, Multiple choice
Real Exam Qty:	65 scored questions + 15 unscored questions
Exam Price:	USD 150
Available Languages:	Korean, Simplified Chinese, Japanese, English
Exam Duration:	130 minutes
Certificate Validity Period:	3 years
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:

Topic	Details
Topic 1	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 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	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 (Q74-Q79):

NEW QUESTION # 74
A company is interested in building a fraud detection model. Currently, the data scientist does not have a sufficient amount of information due to the low number of fraud cases.
Which method is MOST likely to detect the GREATEST number of valid fraud cases?

A. Oversampling using SMOTE
B. Oversampling using bootstrapping
C. Undersampling
D. Class weight adjustment

Answer: A

Explanation:
With datasets that are not fully populated, the Synthetic Minority Over-sampling Technique (SMOTE. adds new information by adding synthetic data points to the minority class. This technique would be the most effective in this scenario.

NEW QUESTION # 75
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?

A. Use Amazon Macie to identify the sensitive data. Create a set of AWS Lambda functions to remove the sensitive data.
B. Use Amazon Comprehend to identify the sensitive data. Launch Amazon EC2 instances to remove the sensitive data.
C. Deploy the model on Amazon SageMaker. Create a set of AWS Lambda functions to identify and remove the sensitive data.
D. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Create an AWS Batch job to identify and remove the sensitive data.

Answer: A

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 # 76
A company is training a large language model (LLM) by using on-premises infrastructure. A live conversational engine uses the LLM to help customers find real-time insights in credit card data.
An ML engineer must implement a solution to train and deploy the LLM on Amazon SageMaker.
Which solution will meet these requirements?

A. Use SageMaker Notebook Jobs to train the LLM. Deploy the LLM by using SageMaker Asynchronous Inference.
B. Use SageMaker with deep learning containers for large model inference to train the LLM. Deploy the LLM by using SageMaker real-time inference.
C. Use SageMaker Studio to train the LLM. Deploy the LLM by using SageMaker batch transform.
D. Use SageMaker Training Compiler to train the LLM. Deploy the LLM by using SageMaker real- time inference.

Answer: D

Explanation:
SageMaker Training Compiler accelerates training of large models like LLMs by optimizing GPU utilization, making it suitable for efficient large-scale training. For deployment of a live conversational engine that requires real-time responses, the correct choice is a SageMaker real- time inference endpoint. This combination meets both training and deployment requirements effectively.

NEW QUESTION # 77
A company has a custom extract, transform, and load (ETL) process that runs on premises. The ETL process is written in the R language and runs for an average of 6 hours. The company wants to migrate the process to run on AWS.
Which solution will meet these requirements?

A. Use an AWS Lambda function created from a container image to run the ETL jobs.
B. Use Amazon SageMaker AI processing jobs with a custom Docker image stored in Amazon Elastic Container Registry (Amazon ECR).
C. Use AWS Glue to prepare and run the ETL jobs.
D. Use Amazon SageMaker AI script mode to build a Docker image. Run the ETL jobs by using SageMaker Notebook Jobs.

Answer: B

Explanation:
The ETL process has two critical characteristics: it is long-running (6 hours) and written in R. AWS Lambda is unsuitable because it has a maximum execution time of 15 minutes. AWS Glue primarily supports Spark- based ETL and does not natively support custom R-based workloads.
AWS documentation recommends using Amazon SageMaker Processing Jobs for long-running, custom data processing workloads. Processing jobs allow users to run arbitrary code in custom Docker containers, making them ideal for migrating on-premises ETL jobs written in R.
By building a custom Docker image that includes the R runtime and required libraries and storing it in Amazon ECR, the company can run the ETL job at scale on managed infrastructure without rewriting the code.
SageMaker script mode is intended for training, not ETL. Therefore, SageMaker processing jobs with a custom container are the correct solution.

NEW QUESTION # 78
A company wants to provide services to help other businesses label images. The company wants its labeling specialists to complete human labeling tasks on AWS. How should the company register the labeling specialists to receive tasks on AWS?

A. Use the Amazon Mechanical Turk website.
B. Create and use Amazon Mechanical Turk entities in an Amazon SageMaker human loop.
C. Create and use an internal workforce in Amazon SageMaker Ground Truth.
D. Use AWS Data Exchange.

Answer: C

Explanation:
To enable labeling specialists within the company to perform tasks, the correct solution is to create and use an internal workforce in Amazon SageMaker Ground Truth. This allows the company to securely register and manage its own labeling team to receive and complete human labeling tasks.

NEW QUESTION # 79
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