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| 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 Format: | Multiple response, Ordering, Matching, Multiple choice, Case study |
| Related Certifications: | AWS Certified AI Practitioner AWS Certified Machine Learning - Specialty |
| Exam Duration: | 130 minutes |
| Exam Price: | 150 USD |
| Passing Score: | 720 (scaled score 100–1000) |
| 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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NEW QUESTION # 33
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: A
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 # 34
A company uses Amazon SageMakerAI to support ML workflows such as model training and deployment.
Select the correct registry from the following list to meet the requirements for each use case with the LEAST operational overhead. Each registry should be selected one or more times. (Select FOUR.)
* Amazon Elastic Container Registry (Amazon ECR)
* SageMaker Model Registry
Answer:
Explanation:
Explanation:
* Tag model packages and use model package groups that include container images for training and deployment # SageMaker Model Registry
* Store predefined language packages, kernels, and relevant dependencies # Amazon Elastic Container Registry (Amazon ECR)
* Organize models and their images into model groups for better discoverability # SageMaker Model Registry
* Pull built-in SageMaker AI images for model training # Amazon Elastic Container Registry (Amazon ECR) The correct hotspot mapping is based on the different purposes of SageMaker Model Registry and Amazon ECR.
For model packages, model package groups, and model discoverability, the correct choice is SageMaker Model Registry. AWS documentation states that a model package group is a collection of versioned model packages, and each version can contain the model artifacts, metadata, and container information used for deployment. AWS also documents that registered models can be organized into groups to support governance, discoverability, and lifecycle management. That is why both "tag model packages and use model package groups" and "organize models and their images into model groups" map to SageMaker Model Registry.
For storing software environments and pulling training images, the correct choice is Amazon ECR. AWS documentation says SageMaker uses Docker container images, and these images contain the software stack such as frameworks, language packages, kernels, and dependencies. AWS also states that SageMaker provides prebuilt Docker images for training and inference, and these are referenced through Amazon ECR image URIs. Therefore, both "store predefined language packages, kernels, and relevant dependencies" and
"pull built-in SageMaker AI images for model training" map to Amazon ECR.
NEW QUESTION # 35
An ML engineer is working on an ML model to predict the prices of similarly sized homes. The model will base predictions on several features The ML engineer will use the following feature engineering techniques to estimate the prices of the homes:
* Feature splitting
* Logarithmic transformation
* One-hot encoding
* Standardized distribution
Select the correct feature engineering techniques for the following list of features. Each feature engineering technique should be selected one time or not at all (Select three.)
Answer:
Explanation:
Explanation:
City (name): One-hot encoding
Type_year (type of home and year the home was built): Feature splitting Size of the building (square feet or square meters): Standardized distribution City (name): One-hot encoding Why? The " City " is a categorical feature (non-numeric), so one-hot encoding is used to transform it into a numeric format. This encoding creates binary columns for each unique category (e.g., cities like " New York " or " Los Angeles " ), which the model can interpret.
Type_year (type of home and year the home was built): Feature splitting Why? " Type_year " combines two pieces of information into one column, which could confuse the model.
Feature splitting separates this column into two distinct features: " Type of home " and " Year built, " enabling the model to process each feature independently.
Size of the building (square feet or square meters): Standardized distribution Why? Size is a continuous numerical variable, and standardization (scaling the feature to have a mean of 0 and a standard deviation of 1) ensures that the model treats it fairly compared to other features, avoiding bias from differences in feature scale.
By applying these feature engineering techniques, the ML engineer can ensure that the input data is correctly formatted and optimized for the model to make accurate predictions.
NEW QUESTION # 36
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?
Answer: C
Explanation:
Thetarget_precisionhyperparameter in the Amazon SageMaker linear learner controls the trade-off between precision and recall for the model. Increasing the target_precision prioritizes minimizing false positives by making the model more cautious in its predictions. This approach is effective for use cases where false positives have higher consequences than false negatives.
NEW QUESTION # 37
A company has deployed a model to predict the churn rate for its games by using Amazon SageMaker Studio.
After the model is deployed, the company must monitor the model performance for data drift and inspect the report. Select and order the correct steps from the following list to model monitor actions. Select each step one time. (Select and order THREE.) .
Check the analysis results on the SageMaker Studio console. .
Create a Shapley Additive Explanations (SHAP) baseline for the model by using Amazon SageMaker Clarify.
Schedule an hourly model explainability monitor.
Answer:
Explanation:
Explanation:
Step 1:
Create a Shapley Additive Explanations (SHAP) baseline for the model by using Amazon SageMaker Clarify.
Step 2:
Schedule an hourly model explainability monitor.
Step 3:
Check the analysis results on the SageMaker Studio console.
When monitoring a deployed model for data drift and explainability, AWS prescribes a specific workflow using SageMaker Clarify and SageMaker Model Monitor:
Create a SHAP baseline (Step 1)Before any monitoring can occur, SageMaker Clarify must establish a baseline explainability configuration. This baseline captures the reference SHAP values for feature importance using training or baseline data. Model Monitor uses this baseline to compare future inferences and detect drift in feature attributions.
Schedule the model explainability monitor (Step 2)After the baseline is created, an explainability monitoring schedule must be configured (hourly in this case). The monitor periodically analyzes inference data, compares it against the SHAP baseline, and generates reports that highlight drift or anomalies in feature contributions.
Inspect results in SageMaker Studio (Step 3)Once monitoring jobs run, SageMaker stores the analysis results in Amazon S3 and surfaces them in the SageMaker Studio console, where engineers can review metrics, violations, and visual reports.
This sequence is mandatory because:
A monitor cannot run without a baseline
Results cannot be reviewed until the monitor executes
NEW QUESTION # 38
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