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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
Real Exam Qty:	65 (50 scored, 15 unscored)
Exam Duration:	130 minutes
Related Certifications:	AWS Certified Machine Learning - Specialty AWS Certified AI Practitioner
Exam Price:	150 USD
Available Languages:	Korean, English, Japanese, Simplified Chinese
Exam Format:	Multiple choice, Case study, Matching, Multiple response, Ordering
Passing Score:	720 (scaled score 100–1000)
Certificate Validity Period:	3 years
Recommended Training:	AWS Training and Certification AWS Certified Machine Learning Engineer - Associate Official Exam Guide
Exam Registration:	Pearson VUE Registration AWS Certification Portal
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 Prüfungsplan:

Thema	Einzelheiten
Thema 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.
Thema 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.
Thema 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.
Thema 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 MLA-C01 Prüfungsfragen mit Lösungen (Q189-Q194):

189. Frage
A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_dassifier for the predictorjype hyperparameter.
What should the company do to MINIMIZE false positives?

A. Set the value of the weight decay hyperparameter to zero.
B. Increase the value of the target_precision hyperparameter.
C. Change the value of the predictorjype hyperparameter to regressor.
D. Increase the number of training epochs.

Antwort: B

190. Frage
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?

A. Use Amazon SageMaker Asynchronous Inference.
B. Use Amazon SageMaker Serverless Inference.
C. Use an Amazon SageMaker AI batch transform job with fixed capacity.
D. Use a single Amazon EC2 GPU instance with reserved capacity.

Antwort: B

Begründung:
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.

191. Frage
An ML engineer has trained a neural network by using stochastic gradient descent (SGD). The neural network performs poorly on the test set. The values for training loss and validation loss remain high and show an oscillating pattern. The values decrease for a few epochs and then increase for a few epochs before repeating the same cycle.
What should the ML engineer do to improve the training process?

A. Increase the learning rate.
B. Decrease the learning rate.
C. Increase the size of the test set.
D. Introduce early stopping.

Antwort: B

192. Frage
A company uses an ML model to recommend videos to users. The model is deployed on Amazon SageMaker AI. The model performed well initially after deployment, but the model's performance has degraded over time.
Which solution can the company use to identify model drift in the future?

A. Create a monitoring job in SageMaker Model Monitor. Then create a baseline from the training dataset.
B. Create a baseline from the training dataset. Then create a monitoring job in SageMaker Model Monitor.
C. Retrain the model on new data. Compare the retrained model's performance to the original model's performance.
D. Create a baseline by using a built-in rule in SageMaker Clarify. Monitor the drift in Amazon CloudWatch.

Antwort: B

Begründung:
AWS recommends Amazon SageMaker Model Monitor for detecting data drift and model drift in deployed models. Model Monitor works by comparing live inference data against a baseline, which must first be created from the training dataset.
AWS documentation clearly specifies the required order:
* Create a baseline using training data statistics
* Create a monitoring schedule to compare incoming data against the baseline Option A reverses this order and is therefore incorrect. Option C is incorrect because SageMaker Clarify focuses on bias and explainability, not ongoing drift detection. Option D is reactive and does not provide continuous monitoring.
Model Monitor integrates with Amazon CloudWatch, enabling automated alerts and downstream retraining workflows. This proactive approach allows companies to detect degradation early and maintain model quality.
Therefore, Option B is the correct and AWS-verified answer.

193. Frage
A company has trained and deployed an ML model by using Amazon SageMaker. The company needs to implement a solution to record and monitor all the API call events for the SageMaker endpoint. The solution also must provide a notification when the number of API call events breaches a threshold.
Use SageMaker Debugger to track the inferences and to report metrics. Create a custom rule to provide a notification when the threshold is breached.
Which solution will meet these requirements?

A. Log all the endpoint invocation API events by using AWS CloudTrail. Use an Amazon CloudWatch dashboard for monitoring. Set up a CloudWatch alarm to provide notification when the threshold is breached.
B. Add the Invocations metric to an Amazon CloudWatch dashboard for monitoring. Set up a CloudWatch alarm to provide notification when the threshold is breached.
C. Use SageMaker Debugger to track the inferences and to report metrics. Create a custom rule to provide a notification when the threshold is breached.
D. Use SageMaker Debugger to track the inferences and to report metrics. Use the tensor_variance built-in rule to provide a notification when the threshold is breached.

Antwort: B

Begründung:
Amazon SageMaker automatically tracks theInvocationsmetric, which represents the number of API calls made to the endpoint, inAmazon CloudWatch. By adding this metric to a CloudWatch dashboard, you can monitor the endpoint's activity in real-time. Setting up aCloudWatch alarmallows the system to send notifications whenever the API call events exceed the defined threshold, meeting both the monitoring and notification requirements efficiently.

194. Frage
......

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