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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
Certificate Validity Period:	3 years
Available Languages:	Japanese, English, Simplified Chinese, Korean
Real Exam Qty:	65 (50 scored, 15 unscored)
Exam Duration:	130 minutes
Exam Price:	150 USD
Passing Score:	720 (scaled score 100–1000)
Related Certifications:	AWS Certified Machine Learning - Specialty AWS Certified AI Practitioner
Exam Format:	Matching, Multiple response, Multiple choice, Ordering, Case study
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	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 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 4	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.

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q126-Q131):

NEW QUESTION # 126
An ML engineer notices class imbalance in an image classification training job.
What should the ML engineer do to resolve this issue?

A. Apply random oversampling on the dataset.
B. Transform some of the images in the dataset.
C. Apply random data splitting on the dataset.
D. Reduce the size of the dataset.

Answer: A

NEW QUESTION # 127
A company has an ML model that generates text descriptions based on images that customers upload to the company's website. The images can be up to 50 MB in total size.
An ML engineer decides to store the images in an Amazon S3 bucket. The ML engineer must implement a processing solution that can scale to accommodate changes in demand.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create an AWS Batch job that uses an Amazon Elastic Container Service (Amazon ECS) cluster.Specify a list of images to process for each AWS Batch job.
B. Create an Amazon SageMaker batch transform job to process all the images in the S3 bucket.
C. Create an Amazon Elastic Kubernetes Service (Amazon EKS) cluster that uses Karpenter for auto scaling. Host the model on the EKS cluster. Run a script to make an inference request for each image.
D. Create an Amazon SageMaker Asynchronous Inference endpoint and a scaling policy. Run a script to make an inference request for each image.

Answer: D

NEW QUESTION # 128
A company uses Amazon SageMaker for its ML workloads. The company's ML engineer receives a 50 MB Apache Parquet data file to build a fraud detection model. The file includes several correlated columns that are not required.
What should the ML engineer do to drop the unnecessary columns in the file with the LEAST effort?

A. Create a data flow in SageMaker Data Wrangler. Configure a transform step.
B. Create an Apache Spark job that uses a custom processing script on Amazon EMR.
C. Create a SageMaker processing job by calling the SageMaker Python SDK.
D. Download the file to a local workstation. Perform one-hot encoding by using a custom Python script.

Answer: A

NEW QUESTION # 129
A company has built more than 50 models and deployed the models on Amazon SageMaker Al as real-time inference endpoints. The company needs to reduce the costs of the SageMaker Al inference endpoints. The company used the same ML framework to build the models. The company ' s customers require low-latency access to the models.
Select and order the correct steps from the following list to reduce the cost of inference and keep latency low.
Select each
step one time or not at all. (Select and order FIVE.)
Create an endpoint configuration that references a multi-model container.
. Create a SageMaker Al model with multi-model endpoints enabled.
. Deploy a real-time inference endpoint by using the endpoint configuration.
. Deploy a serverless inference endpoint configuration by using the endpoint configuration.
Spread the existing models to multiple different Amazon S3 bucket paths.
. Upload the existing models to the same Amazon S3 bucket path.
. Update the models to use the new endpoint ID. Pass the model IDs to the new endpoint.

Answer:

Explanation:

Explanation:
Step 1
Upload the existing models to the same Amazon S3 bucket path.
Multi-model endpoints require all models to be stored under a single S3 prefix so SageMaker can dynamically load them on demand.
Step 2
Create a SageMaker AI model with multi-model endpoints enabled.
This creates a SageMaker model resource that uses a multi-model-capable container (for example, XGBoost, PyTorch, or TensorFlow MME-compatible containers).
Step 3
Create an endpoint configuration that references a multi-model container.
The endpoint configuration defines:
Instance type
Initial instance count
The multi-model container reference
Step 4
Deploy a real-time inference endpoint by using the endpoint configuration.
Real-time endpoints ensure low-latency inference, which is a strict customer requirement.
Step 5
Update the models to use the new endpoint ID. Pass the model IDs to the new endpoint.
Each inference request specifies a model ID so SageMaker knows which model to load from S3.

NEW QUESTION # 130
A construction company is using Amazon SageMaker AI to train specialized custom object detection models to identify road damage. The company uses images from multiple cameras. The images are stored as JPEG objects in an Amazon S3 bucket.
The images need to be pre-processed by using computationally intensive computer vision techniques before the images can be used in the training job. The company needs to optimize data loading and pre-processing in the training job. The solution cannot affect model performance or increase compute or storage resources.
Which solution will meet these requirements?

A. Use SageMaker AI file mode to load and process the images in batches.
B. Reduce the batch size of the model and increase the number of pre-processing threads.
C. Reduce the quality of the training images in the S3 bucket.
D. Convert the images into RecordIO format and use the lazy loading pattern.

Answer: D

Explanation:
AWS documentation recommends using RecordIO format with lazy loading to optimize data input pipelines for image-based training workloads. RecordIO is a binary data format that enables sequential reads, reducing I
/O overhead and improving throughput during training.
By converting JPEG images into RecordIO format, the training job can read data more efficiently from Amazon S3. Lazy loading ensures that only the required data is loaded into memory when needed, which optimizes CPU utilization during computationally intensive preprocessing steps.
Option A (file mode) results in many small S3 GET requests, which can become a bottleneck for large image datasets. Option B changes training behavior and can negatively affect convergence and performance. Option C reduces image quality, which directly impacts model accuracy and violates the requirement.
AWS SageMaker documentation explicitly highlights RecordIO and lazy loading as best practices for high- performance image training pipelines, especially when preprocessing is CPU-intensive.
Therefore, Option D is the correct and AWS-aligned solution.

NEW QUESTION # 131
......

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