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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 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	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 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 (Q80-Q85):

NEW QUESTION # 80
A company wants to use Amazon SageMaker to host an ML model that runs on CPU for real-time predictions. The model will have intermittent traffic during business hours and will have periods of no traffic after business hours. The company needs a solution that will serve inference requests in the most cost-effective manner. Which hosting option will meet these requirements?

A. Deploy the model to a SageMaker Serverless Inference endpoint. Configure increased provisioned concurrency during business hours.
B. Deploy the model to a SageMaker Asynchronous Inference endpoint. Configure an auto scaling policy that scales in to zero outside business hours.
C. Deploy the model to a SageMaker real-time endpoint. Create a scheduled AWS Lambda function that activates the endpoint during business hours only.
D. Deploy the model to a SageMaker real-time endpoint. Add a schedule-based auto scaling policy to handle traffic surges during business hours.

Answer: A

Explanation:
SageMaker Serverless Inference is the most cost-effective option for models with intermittent traffic. It automatically scales down to zero when idle, so no cost is incurred outside business hours. Configuring provisioned concurrency during business hours ensures low-latency responses when traffic is expected.

NEW QUESTION # 81
A company is using Amazon SageMaker AI to develop a credit risk assessment model. During model validation, the company finds that the model achieves 82% accuracy on the validation data. However, the model achieved 99% accuracy on the training data. The company needs to address the model accuracy issue before deployment.
Which solution will meet this requirement?

A. Implement dropout layers. Use L1 or L2 regularization. Perform k-fold cross-validation.
B. Use principal component analysis (PCA) to reduce the feature dimensionality. Decrease model layers.
Implement cross-entropy loss functions.
C. Augment the training dataset. Remove duplicate records from the training dataset. Implement stratified sampling.
D. Add more dense layers to increase model complexity. Implement batch normalization. Use early stopping during training.

Answer: A

Explanation:
The large gap between training accuracy (99%) and validation accuracy (82%) is a textbook case of overfitting. The model has learned patterns that fit the training data extremely well but do not generalize to unseen data.
AWS ML best practices recommend regularization techniques to address overfitting. Dropout layers randomly deactivate neurons during training, preventing the network from relying too heavily on specific paths. L1 and L2 regularization penalize large weights, reducing model complexity and improving generalization. k-fold cross-validation provides a more robust evaluation by training and validating the model across multiple data splits.
Option A increases complexity, which would worsen overfitting. Option C mixes valid ideas (dimensionality reduction) with unrelated changes (loss function choice) and is less targeted. Option D focuses on data quality but does not directly address model variance.
Therefore, implementing dropout, regularization, and k-fold cross-validation is the correct solution.

NEW QUESTION # 82
A healthcare company wants to detect irregularities in patient vital signs that could indicate early signs of a medical condition. The company has an unlabeled dataset that includes patient health records, medication history, and lifestyle changes.
Which algorithm and hyperparameter should the company use to meet this requirement?

A. Use the Amazon SageMaker AI Random Cut Forest (RCF) algorithm. Set num_trees to greater than
100.
B. Use the Amazon SageMaker AI k-means clustering algorithm. Set k to determine the number of clusters.
C. Use the Amazon SageMaker AI DeepAR algorithm. Set epochs to the number of training iterations.
D. Use the Amazon SageMaker AI XGBoost algorithm. Set max_depth to greater than 100 to regulate tree complexity.

Answer: A

Explanation:
Option D is correct because Amazon SageMaker AI Random Cut Forest (RCF) is documented by AWS as an unsupervised algorithm for detecting anomalous data points within a data set . The question describes a healthcare use case where the company wants to detect irregularities in patient vital signs and only has an unlabeled dataset . That is a direct match for anomaly detection with an unsupervised method rather than a supervised classification or regression algorithm. AWS documentation also notes that anomalies can appear as unexpected spikes, breaks in patterns, or otherwise unusual observations.
RCF is a strong fit because it does not require labeled target outcomes to begin identifying unusual observations. AWS explains that RCF assigns an anomaly score to each data point, where higher scores indicate potentially anomalous records. This is especially suitable for early warning scenarios in healthcare, where the goal is to surface unusual combinations of vitals, medication history, and lifestyle changes for further review.
The hyperparameter in the answer, num_trees , is a valid RCF hyperparameter and controls the number of trees used in the forest. In AWS examples and documentation, this is one of the key parameters used when configuring the algorithm. By contrast, XGBoost is generally used for supervised tasks, k-means is for clustering rather than anomaly scoring, and DeepAR is for forecasting time series rather than general anomaly detection across an unlabeled multidimensional dataset.
Therefore, the fully verified AWS-docs answer is D , because Random Cut Forest is the AWS-native unsupervised anomaly detection algorithm best aligned to this healthcare irregularity-detection requirement.

NEW QUESTION # 83
An ML engineer is using Amazon SageMaker AI to train an ML model. The ML engineer needs to use SageMaker AI automatic model tuning (AMT) features to tune the model hyperparameters over a large parameter space.
The model has 20 categorical hyperparameters and 7 continuous hyperparameters that can be tuned. The ML engineer needs to run the tuning job a maximum of 1,000 times. The ML engineer must ensure that each parameter trial is built based on the performance of the previous trial.
Which solution will meet these requirements?

A. Define the search space as categorical parameters of 1,000 possible combinations. Use grid search.
B. Define the search space as continuous parameters. Use random search. Set the maximum number of tuning jobs to 1,000.
C. Define the search space as categorical parameters and continuous parameters. Use Bayesian optimization. Set the maximum number of training jobs to 1,000.
D. Define the search space as categorical parameters and continuous parameters. Use grid search. Set the maximum number of tuning jobs to 1,000.

Answer: C

Explanation:
The requirement that each parameter trial is built based on the performance of the previous trial is the defining characteristic of Bayesian optimization. In Amazon SageMaker Automatic Model Tuning, Bayesian optimization uses prior trial results to intelligently select the next set of hyperparameters, making it far more efficient than grid or random search-especially in large, mixed search spaces.
This scenario includes both categorical (20) and continuous (7) hyperparameters and allows up to 1,000 training jobs, which is well within the supported limits of SageMaker AMT. Bayesian optimization natively supports mixed parameter types and is explicitly recommended by AWS for large, high-dimensional search spaces where exhaustive grid search is impractical.
Option A and D (grid search) do not meet the requirement because grid search evaluates combinations independently and does not learn from previous trials. Additionally, grid search becomes computationally infeasible as dimensionality increases.
Option B (random search) also evaluates trials independently and does not leverage previous results, violating the core requirement.
Therefore, defining both categorical and continuous parameters and using Bayesian optimization with a maximum of 1,000 jobs is the correct and AWS-recommended solution.

NEW QUESTION # 84
An ML engineer is building a logistic regression model to predict customer churn for subscription services.
The dataset contains two string variables: location and job_seniority_level.
The location variable has 3 distinct values, and the job_seniority_level variable has over 10 distinct values.
The ML engineer must perform preprocessing on the variables.
Which solution will meet this requirement?

A. Apply binning to location. Apply standard scaling to job_seniority_level.
B. Apply one-hot encoding to location. Apply standard scaling to job_seniority_level.
C. Apply one-hot encoding to location. Apply ordinal encoding to job_seniority_level.
D. Apply tokenization to location. Apply ordinal encoding to job_seniority_level.

Answer: C

Explanation:
Logistic regression requires numeric input features and is sensitive to how categorical variables are encoded.
AWS feature engineering best practices recommend one-hot encoding for low-cardinality categorical variables with no inherent order and ordinal encoding for categorical variables with a meaningful order.
The location feature has only three distinct values and no ordinal relationship, making one-hot encoding the most appropriate method. This prevents the model from inferring a false numerical relationship between locations.
The job_seniority_level feature typically has an inherent order (for example: junior, mid-level, senior, lead).
Even with more than 10 categories, ordinal encoding preserves this natural hierarchy while keeping the feature dimensionality manageable.
Tokenization is used for unstructured text, not structured categorical variables. Standard scaling applies only to continuous numeric features and is not suitable for categorical string variables.
AWS documentation explicitly highlights using one-hot encoding for nominal features and ordinal encoding for ordered categorical features when preparing data for linear models such as logistic regression.
Therefore, Option B is the correct and AWS-aligned solution.

NEW QUESTION # 85
......

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