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Amazon MLA-C01 Exam Syllabus Topics:

Topic	Details
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	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	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 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.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q215-Q220):

NEW QUESTION # 215
A company is using Amazon SageMaker to create ML models. The company's data scientists need fine-grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications.
Which solution will meet these requirements?

A. Use AWS CodePipeline and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.
B. Use AWS CodePipeline and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.
C. Use SageMaker Pipelines and its integration with SageMaker Experiments to manage the entire ML workflows. Use SageMaker Experiments for the running history of experiments and for auditing and compliance verifications.
D. Use SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications.

Answer: D

NEW QUESTION # 216
A company is using Amazon SageMaker to develop ML models. The company stores sensitive training data in an Amazon S3 bucket. The model training must have network isolation from the internet.
Which solution will meet this requirement?

A. Run the SageMaker training jobs in public subnets that have an attached security group. In the security group, use inbound rules to limit traffic from the internet. Encrypt SageMaker instance storage by using server-side encryption with AWS KMS keys (SSE-KMS).
B. Run the SageMaker training jobs in private subnets. Create a NAT gateway. Route traffic for training through the NAT gateway.
C. Encrypt traffic to Amazon S3 by using a bucket policy that includes a value of True for the aws:SecureTransport condition key. Use default at-rest encryption for Amazon S3. Encrypt SageMaker instance storage by using server-side encryption with AWS KMS keys (SSE-KMS).
D. Run the SageMaker training jobs in private subnets. Create an S3 gateway VPC endpoint. Route traffic for training through the S3 gateway VPC endpoint.

Answer: D

NEW QUESTION # 217
An ML engineer needs to train a supervised deep learning model. The available dataset is a large number of unlabeled images that only employees should access. The ML engineer needs to implement a solution that labels the dataset with the highest possible accuracy. Which combination of steps should the ML engineer take to meet these requirements? (Choose two.)

A. Use Amazon Mechanical Turk to complete the annotation job created by Amazon SageMaker Ground Truth.
B. Use Amazon Rekognition to automatically label the dataset.
C. Train the deep learning model directly on the raw data. Let the model infer the labels by itself.
D. Use Amazon SageMaker Ground Truth to create an annotation job that specifies the labeling task and requirements.
E. Set up workforce teams to access a private workforce to run and review the annotation job created by Amazon SageMaker Ground Truth.

Answer: D,E

Explanation:
To achieve the highest labeling accuracy with controlled employee-only access, the ML engineer should use Amazon SageMaker Ground Truth to define the annotation job and then assign it to a private workforce of employees for labeling and review. This ensures high-quality, secure labeling restricted to authorized personnel.

NEW QUESTION # 218
A company needs to update the model definition of an existing Amazon SageMaker Al endpoint.
Select and order the correct steps from the following list to update the model definition settings with the LEAST interruption of inferences. Select each step one time or not at all. (Select and order THREE.)
* Create a new endpoint configuration that uses the new model definition.
* Create a new model definition with updated settings by using the CreateModel action in the SageMaker AI API.
* Delete the endpoint that needs to be updated and recreate the endpoint with the new endpoint configuration.
* Delete the IAM role and permissions for the ExecutionRoleArn parameter.
* Update the endpoint with the new endpoint configuration.

Answer:

Explanation:

Explanation:
Step 1: Create a new model definition with updated settings by using the CreateModel action in the SageMaker AI API.
Step 2: Create a new endpoint configuration that uses the new model definition.
Step 3: Update the endpoint with the new endpoint configuration.
Do not delete and recreate the endpoint. That causes unnecessary inference interruption. SageMaker endpoint updates are designed to use a new EndpointConfig; AWS explicitly states that to update an endpoint, you must create a new endpoint configuration, then call UpdateEndpoint on the existing endpoint. During the update, SageMaker changes the endpoint to Updating and then back to InService.

NEW QUESTION # 219
An ML engineer is training an XGBoost regression model in Amazon SageMaker AI. The ML engineer conducts several rounds of hyperparameter tuning with random grid search. After these rounds of tuning, the error rate on the test hold-out dataset is much larger than the error rate on the training dataset.
The ML engineer needs to make changes before running the hyperparameter grid search again.
Which changes will improve the model's performance? (Select TWO.)

A. Decrease the model complexity by reducing the number of features in the dataset.
B. Increase the model complexity by increasing the number of features in the dataset.
C. Decrease the model complexity by reducing the number of samples in the dataset.
D. Increase the value of the L2 regularization parameter.
E. Decrease the value of the L2 regularization parameter.

Answer: A,D

Explanation:
The scenario describes a classic overfitting problem: the XGBoost model performs well on the training dataset but poorly on the test hold-out dataset. According to AWS Machine Learning and XGBoost documentation, overfitting occurs when a model is too complex and learns noise and patterns specific to the training data rather than generalizable relationships.
One effective way to address overfitting is to reduce model complexity. Option B, reducing the number of features, simplifies the hypothesis space and lowers the risk of fitting spurious correlations. Feature reduction is a recommended best practice when the model shows a large generalization gap between training and test error.
Another effective method is to increase regularization. Option D, increasing the L2 regularization parameter (lambda in XGBoost), penalizes large weights and discourages overly complex trees. AWS documentation explicitly notes that L2 regularization helps improve generalization by smoothing model parameters and reducing variance.
Option A would worsen overfitting by increasing complexity. Option C is incorrect because reducing the number of training samples generally increases overfitting risk. Option E would decrease regularization strength and further degrade test performance.
Therefore, reducing feature complexity and increasing L2 regularization are the correct changes.

NEW QUESTION # 220
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