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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
Exam Price:	USD 150
Exam Format:	Matching, Ordering, Multiple choice, Multiple response
Certificate Validity Period:	3 years
Passing Score:	720/1000
Available Languages:	English, Japanese, Korean, Simplified Chinese
Exam Duration:	130 minutes
Related Certifications:	AWS Certified Data Engineer – Associate AWS Certified Solutions Architect – Associate AWS Certified AI Practitioner AWS Certified DevOps Engineer – Professional
Real Exam Qty:	65 scored questions + 15 unscored questions
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	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 (Q41-Q46):

NEW QUESTION # 41
An ML engineer needs to use an ML model to predict the price of apartments in a specific location.
Which metric should the ML engineer use to evaluate the model's performance?

A. Accuracy
B. Area Under the ROC Curve (AUC)
C. Mean absolute error (MAE)
D. F1 score

Answer: C

NEW QUESTION # 42
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 # 43
A company runs an Amazon SageMaker domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker domain.
Recently, the company discovered suspicious traffic to the domain from a specific IP address.
The company needs to block traffic from the specific IP address.
Which update to the network configuration will meet this requirement?

A. Create a security group inbound rule to deny traffic from the specific IP address. Assign the security group to the domain.
B. Create a network ACL inbound rule to deny traffic from the specific IP address. Assign the rule to the default network Ad for the subnet where the domain is located.
C. Create a VPC route table to deny inbound traffic from the specific IP address. Assign the route table to the domain.
D. Create a shadow variant for the domain. Configure SageMaker Inference Recommender to send traffic from the specific IP address to the shadow endpoint.

Answer: B

NEW QUESTION # 44
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. Reduce the quality of the training images in the S3 bucket.
B. Convert the images into RecordIO format and use the lazy loading pattern.
C. Use SageMaker AI file mode to load and process the images in batches.
D. Reduce the batch size of the model and increase the number of pre-processing threads.

Answer: B

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 # 45
A hospital wants to predict patient outcomes for the coming year An ML engineer must improve several existing ML models that currently perform poorly.
Select the correct regularization method from the following list to improve each model Select each regularization method one time, more than one time, or not at all. (Select THREE.)
* L1 regularization
* L2 regularization
* Early stopping

Answer:

Explanation:

Explanation:
Linear regression model whose coefficients should shrink but not become zero The answer: L2 regularization AWS says L2 produces smaller overall weight values and is the right fit when coefficients should be reduced without being forced to zero.
Polynomial regression model with irrelevant polynomial terms that should be eliminated The answer: L1 regularization AWS says L1 reduces the number of features used by pushing small weights to zero, which matches elimination of irrelevant terms.
Logistic regression model that has highly correlated features to eliminate highly redundant predictors The answer: L1 regularization This is the nuanced one. AWS says L2 stabilizes weights when there is high correlation between features, but because the question explicitly says eliminate highly redundant predictors, L1 is the better match since it creates sparsity and removes predictors by zeroing coefficients.

NEW QUESTION # 46
......

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