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Amazon MLA-C01 Exam Syllabus Topics:

TopicDetails
Topic 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.
Topic 2
  • 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 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.
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.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q227-Q232):

NEW QUESTION # 227
A customer call center uses Amazon Transcribe to convert hundreds of audio recordings of conversations between customers and support agents to text files. The call center wants to use the text files to train an ML model. To comply with industry regulations, the call center must remove customer names, addresses, and phone numbers from the training text files.
Which solution will meet these requirements with the LEAST development effort?

Answer: A

Explanation:
Option B is correct because AWS Glue provides a built-in Detect PII transform that can detect, mask, or remove personally identifiable information with minimal custom development. AWS documentation says the Detect PII transform can process predefined AWS-managed PII entity types and supports actions such as removing or masking values. The examples in AWS docs explicitly mention sensitive entities such as phone numbers and addresses , which directly match the problem statement.
The question specifically asks for the least development effort . That wording makes AWS Glue Detect PII the strongest answer because it is a native transformation capability rather than a custom code-heavy workflow. AWS also documents fine-grained sensitive data detection features that let you apply actions per entity type, improving usability and reducing the need to build custom parsing and redaction logic yourself.
This is much easier than creating Lambda-based transformation code or custom text-cleaning logic inside another ML preprocessing tool.
The other options are less suitable. Amazon Bedrock Guardrails is not the standard AWS service documented for bulk ETL-style redaction of training text files in this context. S3 Object Lambda would require more custom engineering to inspect and redact each object. SageMaker Data Wrangler custom transformation would also involve extra implementation work compared with using a purpose-built Glue transform. Because the call center already has text output and simply needs regulated fields like names, addresses, and phone numbers removed before training, the AWS-native low-effort solution is AWS Glue Detect PII . Therefore, the best verified answer is B .


NEW QUESTION # 228
An ML engineer needs to use an ML model to predict the price of apartments in a specific location.
Which metric should the ML engineer use to evaluate the model ' s performance?

Answer: B

Explanation:
When predicting continuous variables, such as apartment prices, it ' s essential to evaluate the model ' s performance using appropriate regression metrics. The Mean Absolute Error (MAE) is a widely used metric for this purpose.
Understanding Mean Absolute Error (MAE):
MAE measures the average magnitude of errors in a set of predictions, without considering their direction. It calculates the average absolute difference between predicted values and actual values, providing a straightforward interpretation of prediction accuracy.

Advantages of MAE:
Interpretability: MAE is expressed in the same units as the target variable, making it easy to understand.
Robustness to Outliers: Unlike metrics that square the errors (e.g., Mean Squared Error), MAE does not disproportionately penalize larger errors, making it more robust to outliers.
Comparison with Other Metrics:
Accuracy, AUC, F1 Score: These metrics are designed for classification tasks, where the goal is to predict discrete labels. They are not suitable for regression problems involving continuous target variables.
Mean Squared Error (MSE): While MSE also measures prediction errors, it squares the differences, giving more weight to larger errors. This can be useful in certain contexts but may be sensitive to outliers.
Conclusion:
For evaluating the performance of a model predicting apartment prices-a continuous variable-MAE is an appropriate and effective metric. It provides a clear indication of the average prediction error in the same units as the target variable, facilitating straightforward interpretation and comparison.
References:
Regression Metrics - GeeksforGeeks
Evaluation Metrics for Your Regression Model - Analytics Vidhya
Regression Metrics for Machine Learning - Machine Learning Mastery


NEW QUESTION # 229
An ML engineer must choose the appropriate Amazon SageMaker algorithm to solve specific AI problems.
Select the correct SageMaker built-in algorithm from the following list for each use case. Each algorithm should be selected one time.
* Random Cut Forest (RCF) algorithm
* Semantic segmentation algorithm
* Sequence-to-Sequence (seq2seq) algorithm

Answer:

Explanation:

Explanation:
Use case 1:
Summarize the text of a research paper
## Sequence-to-Sequence (seq2seq) algorithm
Why:
Seq2seq models are designed for natural language generation tasks such as text summarization, translation, and paraphrasing. AWS documentation explicitly lists text summarization as a primary use case for the SageMaker seq2seq algorithm.
Use case 2:
Scan every pixel of an image to help self-driving cars identify objects in their path
## Semantic segmentation algorithm
Why:
Semantic segmentation performs pixel-level classification, assigning a class label to every pixel in an image.
This is exactly what is required for applications such as autonomous driving, road scene understanding, and object boundary detection.
Use case 3:
Identify abnormal data points in a dataset
## Random Cut Forest (RCF) algorithm
Why:
Random Cut Forest is an unsupervised anomaly detection algorithm. AWS SageMaker RCF is purpose-built to identify outliers, unusual patterns, and anomalies in numerical datasets, making it ideal for fraud detection, monitoring, and abnormal data point detection.


NEW QUESTION # 230
A company wants to deploy an Amazon SageMaker AI model that can queue requests. The model needs to handle payloads of up to 1 GB that take up to 1 hour to process. The model must return an inference for each request. The model also must scale down when no requests are available to process.
Which inference option will meet these requirements?

Answer: A

Explanation:
Amazon SageMaker Asynchronous Inference is specifically designed for long-running inference requests and large payloads. It supports payload sizes up to 1 GB and processing times of up to 1 hour, while automatically queuing requests.
Asynchronous inference stores results in Amazon S3 and allows clients to retrieve inference outputs after processing completes. It also supports auto scaling down to zero when there are no incoming requests, reducing cost.
Batch transform is intended for offline, bulk inference and does not return per-request results in an asynchronous request-response pattern. Serverless and real-time inference have strict payload size and timeout limits that do not support 1-hour processing.
Therefore, asynchronous inference is the only SageMaker inference option that meets all stated requirements.


NEW QUESTION # 231
A company runs an Amazon SageMaker domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker domain.
Recently, the company discovered suspicious traffic to the domain from a specific IP address.
The company needs to block traffic from the specific IP address.
Which update to the network configuration will meet this requirement?

Answer: C


NEW QUESTION # 232
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