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Amazon MLA-C01 Exam Overview:

Certification Vendor:Amazon Web Services (AWS)
Exam Name:AWS Certified Machine Learning Engineer - Associate
Exam Number:MLA-C01
Certificate Validity Period:3 years
Exam Price:150 USD
Passing Score:1000-2000 scaled score (passing score approximately 720)
Related Certifications:AWS Certified Solutions Architect - Associate
AWS Certified Developer - Associate
AWS Certified Data Engineer - Associate
Exam Duration:170 minutes
Real Exam Qty:85
Exam Format:Multiple response, Multiple choice
Available Languages:Japanese, Korean, Traditional Chinese, English, Simplified Chinese
Sample Questions:Amazon MLA-C01 Sample Questions
Exam Way:Online proctored (Pearson VUE) or in-person testing center
Pre Condition:Recommended: 2+ years of ML engineering experience, familiarity with AWS ML services
Official Syllabus URL:https://aws.amazon.com/certification/certified-machine-learning-engineer-associate/

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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
  • 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
  • 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
  • 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.

Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q45-Q50):

NEW QUESTION # 45
A company has AWS Glue data processing jobs that are orchestrated by an AWS Glue workflow.
The AWS Glue jobs can run on a schedule or can be launched manually.
The company is developing pipelines in Amazon SageMaker Pipelines for ML model development. The pipelines will use the output of the AWS Glue jobs during the data processing phase of model development. An ML engineer needs to implement a solution that integrates the AWS Glue jobs with the pipelines.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: B


NEW QUESTION # 46
A company plans to use Amazon SageMaker AI to build image classification models. The company has 6 TB of training data stored on Amazon FSx for NetApp ONTAP. The file system is in the same VPC as SageMaker AI.
An ML engineer must make the training data accessible to SageMaker AI training jobs.
Which solution will meet these requirements?

Answer: C

Explanation:
Amazon SageMaker supports direct file system access for training jobs through Amazon FSx. AWS documentation states that FSx for NetApp ONTAP file systems can be mounted directly to SageMaker training instances when they are located in the same VPC.
Mounting the FSx file system allows SageMaker to stream large datasets efficiently without copying data into Amazon S3. This is ideal for very large datasets such as 6 TB of image data and avoids unnecessary storage duplication.
Options involving SageMaker Data Wrangler are intended for data preparation and exploration, not large- scale training data access. Mountpoint for Amazon S3 is not required and introduces additional complexity.
Therefore, Option A is the correct and AWS-aligned solution.


NEW QUESTION # 47
A company has an ML model in Amazon SageMaker AI. An ML engineer needs to implement a monitoring solution to automatically detect changes in the input data distribution of model features.
Which solution will meet this requirement with the LEAST operational overhead?

Answer: A

Explanation:
Option A is correct because the requirement is to detect changes in the input data distribution of model features, which is a data quality / data drift monitoring problem. AWS documentation states that Amazon SageMaker Model Monitor uses rules to detect data drift and alerts you when it happens. The documented workflow is to enable data capture, create a baseline from training data, and then run monitoring jobs that compare incoming inference data against that baseline. That directly matches the need to automatically detect changes in feature distributions.
AWS also documents that Model Monitor can emit metrics to Amazon CloudWatch, and those metrics can be used with CloudWatch alarms to notify teams when data quality drifts beyond acceptable thresholds. That makes Option A the lowest-operational-overhead solution because it uses SageMaker's built-in monitoring capability plus managed alerting, rather than requiring custom drift logic. The inclusion of emit_metrics and CloudWatch alarming is consistent with the SageMaker monitoring pattern for automated notification.
The other options are weaker. Option B is for model quality monitoring, which focuses on prediction performance against ground truth, not shifts in the input feature distribution. Option C uses SageMaker Debugger, which is aimed at training-time debugging and custom rule analysis rather than managed production data drift monitoring. Option D relies on manual log analysis and endpoint performance metrics, which does not directly solve feature-distribution drift detection and adds more operational effort. Therefore, the best AWS-documented answer is A.


NEW QUESTION # 48
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: C


NEW QUESTION # 49
An ML engineer decides to use Amazon SageMaker AI automated model tuning (AMT) for hyperparameter optimization (HPO). The ML engineer requires a tuning strategy that uses regression to slowly and sequentially select the next set of hyperparameters based on previous runs. The strategy must work across small hyperparameter ranges.
Which solution will meet these requirements?

Answer: D

Explanation:
Amazon SageMaker Automated Model Tuning supports several hyperparameter search strategies. Bayesian optimization is explicitly designed to model the relationship between hyperparameters and objective metrics using regression techniques. Based on results from previous training jobs, Bayesian optimization predicts which hyperparameter combinations are most likely to improve model performance and evaluates those next.
AWS documentation highlights Bayesian optimization as the preferred strategy when the hyperparameter search space is small to medium and when training jobs are expensive. Because the algorithm learns from prior runs, it avoids wasting resources on unpromising configurations and converges efficiently.
Grid search exhaustively evaluates all combinations and becomes inefficient even with moderately sized search spaces. Random search does not use information from prior runs and is less efficient. Hyperband focuses on aggressive early stopping and resource allocation, not regression-based sequential selection.
Therefore, Option C is the correct and AWS-verified solution.


NEW QUESTION # 50
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