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| 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 DevOps Engineer – Professional AWS Certified Data Engineer – Associate AWS Certified AI Practitioner |
| Exam Format: | Ordering, Multiple response, Multiple choice, Matching |
| Certificate Validity Period: | 3 years |
| Exam Price: | USD 150 |
| Exam Duration: | 130 minutes |
| Available Languages: | Korean, English, Japanese, Simplified Chinese |
| Passing Score: | 720/1000 |
| Real Exam Qty: | 65 scored questions + 15 unscored questions |
| Recommended Training: | AWS Skill Builder - ML Engineer Associate Exam Prep Amazon SageMaker Documentation |
| 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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NEW QUESTION # 54
A company has trained an ML model that is packaged in a container. The company will integrate the model with an existing Python web application. The company needs to host the model on AWS by using Kubernetes.
The company does not want to manage the control plane and must provision the resources in a repeatable manner. The infrastructure must be provisioned by using Python.
Which solution will meet these requirements?
Answer: B
Explanation:
Option C is correct because the company needs Kubernetes hosting, does not want to manage the control plane, wants repeatable infrastructure provisioning, and requires that provisioning be done by using Python.
Amazon EKS is AWS's managed Kubernetes service, so it satisfies the requirement to avoid managing the Kubernetes control plane directly. The AWS CDK documentation also confirms that Python is a fully supported client language for defining infrastructure as code.
AWS CDK is the best fit because it lets engineers define cloud infrastructure programmatically in Python and deploy it in a repeatable way. The AWS CDK EKS construct library specifically supports defining Amazon EKS clusters and related Kubernetes resources. This makes it a strong match for infrastructure that must be reproducible and expressed in code rather than provisioned manually. Since the model is already packaged in a container, storing the image in Amazon ECR and then deploying it to Amazon EKS follows the normal AWS container workflow.
The other options are less suitable. Option A requires setting up and managing a Kubernetes cluster on EC2, which violates the requirement to avoid control-plane management. Option B uses the AWS CLI, but the question specifically requires infrastructure provisioning by using Python, not command-line provisioning.
Option D uses CloudFormation, which is repeatable infrastructure as code, but the question explicitly says the infrastructure must be provisioned by using Python. AWS CDK uniquely satisfies both the IaC and Python requirements while using managed Kubernetes with EKS.
Therefore, the best verified AWS-docs answer is C.
NEW QUESTION # 55
An ML engineer is using Amazon SageMaker AI to train an ML model. The ML engineer needs to use SageMaker AI automatic model tuning (AMT) features to tune the model hyperparameters over a large parameter space.
The model has 20 categorical hyperparameters and 7 continuous hyperparameters that can be tuned. The ML engineer needs to run the tuning job a maximum of 1,000 times. The ML engineer must ensure that each parameter trial is built based on the performance of the previous trial.
Which solution will meet these requirements?
Answer: A
Explanation:
The requirement that each parameter trial is built based on the performance of the previous trial is the defining characteristic of Bayesian optimization. In Amazon SageMaker Automatic Model Tuning, Bayesian optimization uses prior trial results to intelligently select the next set of hyperparameters, making it far more efficient than grid or random search-especially in large, mixed search spaces.
This scenario includes both categorical (20) and continuous (7) hyperparameters and allows up to 1,000 training jobs, which is well within the supported limits of SageMaker AMT. Bayesian optimization natively supports mixed parameter types and is explicitly recommended by AWS for large, high-dimensional search spaces where exhaustive grid search is impractical.
Option A and D (grid search) do not meet the requirement because grid search evaluates combinations independently and does not learn from previous trials. Additionally, grid search becomes computationally infeasible as dimensionality increases.
Option B (random search) also evaluates trials independently and does not leverage previous results, violating the core requirement.
Therefore, defining both categorical and continuous parameters and using Bayesian optimization with a maximum of 1,000 jobs is the correct and AWS-recommended solution.
NEW QUESTION # 56
A company is creating an ML model to identify defects in a product. The company has gathered a dataset and has stored the dataset in TIFF format in Amazon S3. The dataset contains 200 images in which the most common defects are visible. The dataset also contains 1,800 images in which there is no defect visible.
An ML engineer trains the model and notices poor performance in some classes. The ML engineer identifies a class imbalance problem in the dataset.
What should the ML engineer do to solve this problem?
Answer: C
Explanation:
Class imbalance occurs when one class significantly outnumbers another, causing models to bias predictions toward the majority class. In this case, images without defects (1,800) vastly outnumber images with defects (200). AWS ML best practices recommend oversampling the minority class to improve class representation without discarding valuable data.
Oversampling techniques-such as duplicating minority samples or applying data augmentation-help the model better learn defect-related features. This approach preserves all available data and improves recall and precision for underrepresented defect classes.
Option B is incorrect because undersampling the minority class would further worsen imbalance. Option A unnecessarily reduces dataset size. Option D does not address the imbalance problem.
Thus, oversampling defect images is the correct solution.
NEW QUESTION # 57
An ML engineer needs to create data ingestion pipelines and ML model deployment pipelines on AWS. All the raw data is stored in Amazon S3 buckets.
Which solution will meet these requirements?
Answer: A
NEW QUESTION # 58
A company that has hundreds of data scientists is using Amazon SageMaker to create ML models. The models are in model groups in the SageMaker Model Registry.
The data scientists are grouped into three categories: computer vision, natural language processing (NLP), and speech recognition. An ML engineer needs to implement a solution to organize the existing models into these groups to improve model discoverability at scale. The solution must not affect the integrity of the model artifacts and their existing groupings.
Which solution will meet these requirements?
Answer: C
Explanation:
Using custom tags allows you to organize and categorize models in the SageMaker Model Registry without altering their existing groupings or affecting the integrity of the model artifacts. Tags are a lightweight and scalable way to improve model discoverability at scale, enabling the data scientists to filter and identify models by category (e.g., computer vision, NLP, speech recognition). This approach meets the requirements efficiently without introducing structural changes to the existing model registry setup.
NEW QUESTION # 59
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