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Amazon MLA-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 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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AWS Certified Machine Learning Engineer - Associate study guide & MLA-C01 reliable questions & AWS Certified Machine Learning Engineer - Associate pdf dumps

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q145-Q150):

NEW QUESTION # 145
An ML engineer is training an ML model to identify people's health risk based on 20 features and
1 target. The target class has two values:
- Likely to have health risk (positive class)
- Unlikely to have health risk (negative class)
The age range of people in the dataset is 30 years old to 60 years old. Age is one of the features.
The ML engineer analyzes the features. For the positive class, the difference in proportions of labels (DPL) value is (+0.9) for the age range of 40 to 45 compared with all other age ranges.
What should the ML engineer do to correct this data imbalance?

Answer: D

Explanation:
A DPL of +0.9 indicates that the positive class is heavily overrepresented in the 40-45 age range compared to other age ranges. To correct this imbalance, the solution is to undersample the positive class within the 40-45 range, reducing its dominance and improving fairness in the dataset.


NEW QUESTION # 146
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.)

Answer: A,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 # 147
An ML model is deployed in production. The model has performed well and has met its metric thresholds for months.
An ML engineer who is monitoring the model observes a sudden degradation. The performance metrics of the model are now below the thresholds.
What could be the cause of the performance degradation?

Answer: B

Explanation:
A sudden drop in model performance after a long period of stability is a classic indicator of data drift.
According to AWS ML documentation, production data distribution drift occurs when the statistical properties of incoming data change over time compared to the training dataset.
This drift can be caused by changes in user behavior, market conditions, seasonal trends, or upstream data pipelines. When drift occurs, the model's learned patterns no longer align with real-world data, leading to degraded accuracy and other performance metrics.
Lack of training data (Option A) and overfitting (Option D) are issues that typically manifest during initial training, not after months of stable production performance. Compute constraints (Option C) may affect latency but do not usually cause sustained accuracy degradation.
AWS recommends using SageMaker Model Monitor to detect such drift and trigger retraining workflows.
Therefore, drift in the production data distribution is the most likely cause of the observed performance degradation.


NEW QUESTION # 148
A company uses ML models to predict whether transactions are fraudulent. The company needs to identify as many fraudulent transactions as possible. Which evaluation metric should the company use to evaluate the models to meet this requirement?

Answer: D

Explanation:
Option D is correct because the company's primary goal is to identify as many fraudulent transactions as possible . In AWS documentation, recall is defined as TP / (TP + FN), where TP is true positives and FN is false negatives. Recall measures how well a model finds all actual positive cases. In a fraud-detection setting, the "positive" class is the fraudulent transaction, so maximizing recall means minimizing missed fraud cases.
AWS documentation also explains the business tradeoff clearly: a use case that needs to correctly predict as many positive examples as possible should prioritize high recall , even if that means accepting some additional false positives and therefore only moderate precision. That matches this question exactly, because the requirement is not to be most selective or most balanced overall; it is specifically to catch the largest possible number of fraudulent transactions.
The other metrics are less aligned to the stated goal. Precision focuses on how many predicted fraud cases are actually fraud, which is most important when false positives are very costly. F1 score balances precision and recall, but the question does not ask for balance; it asks for finding as many fraudulent transactions as possible. AUC is useful for overall ranking and threshold- independent model discrimination, but it is not the most direct metric for optimizing missed-fraud detection in this scenario. Based on AWS metric definitions, when the cost of missing true positives is the main concern, recall is the best evaluation metric. Therefore, the best verified answer is D .


NEW QUESTION # 149
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 # 150
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