MLA-C01 Valid Test Syllabus & Certification MLA-C01 Dump

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>> MLA-C01 Valid Test Syllabus <<

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Amazon MLA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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 (Q23-Q28):

NEW QUESTION # 23
An ML engineer is using Amazon SageMaker Canvas to build a custom ML model from an imported dataset.
The model must make continuous numeric predictions based on 10 years of data.
Which metric should the ML engineer use to evaluate the model's performance?

Answer: D

Explanation:
This is a regression problem, where the target variable is continuous and numeric. AWS documentation clearly states that classification metrics such as accuracy and AUC are not appropriate for regression models.
Root Mean Square Error (RMSE) measures the square root of the average squared differences between predicted and actual values. RMSE penalizes larger errors more heavily, making it especially useful when large prediction errors are costly or undesirable.
SageMaker Canvas automatically selects regression metrics such as RMSE and MAE when building regression models. RMSE is widely used for time-based and numeric prediction problems, especially when evaluating long historical datasets.
Inference latency measures system performance, not model accuracy.
Therefore, Option D is the correct and AWS-verified answer.


NEW QUESTION # 24
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: C

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 # 25
An ML engineer has developed a binary classification model outside of Amazon SageMaker. The ML engineer needs to make the model accessible to a SageMaker Canvas user for additional tuning.
The model artifacts are stored in an Amazon S3 bucket. The ML engineer and the Canvas user are part of the same SageMaker domain.
Which combination of requirements must be met so that the ML engineer can share the model with the Canvas user? (Choose two.)

Answer: A,B

Explanation:
The SageMaker Canvas user needs permissions to access the Amazon S3 bucket where the model artifacts are stored to retrieve the model for use in Canvas.
Registering the model in the SageMaker Model Registry allows the model to be tracked and managed within the SageMaker ecosystem. This makes it accessible for tuning and deployment through SageMaker Canvas.
This combination ensures proper access control and integration within SageMaker, enabling the Canvas user to work with the model.


NEW QUESTION # 26
A company uses Amazon Athena to query a dataset in Amazon S3. The dataset has a target variable that the company wants to predict.
The company needs to use the dataset in a solution to determine if a model can predict the target variable.
Which solution will provide this information with the LEAST development effort?

Answer: D


NEW QUESTION # 27
An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account # in the same Region.
Which solution will meet this requirement with the LEAST development effort?

Answer: C

Explanation:
Amazon Comprehend provides the ImportModel API operation, which allows you to copy a custom model between AWS accounts. By creating a resource-based IAM policy on the model in Account A, you can grant Account B the necessary permissions to access and import the model. This approach requires minimal development effort and is the AWS-recommended method for sharing custom models across accounts.


NEW QUESTION # 28
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