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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
Exam Format:	Matching, Multiple choice, Multiple response, Ordering, Case study
Available Languages:	English, Japanese, Simplified Chinese, Korean
Real Exam Qty:	65 (50 scored, 15 unscored)
Exam Price:	150 USD
Certificate Validity Period:	3 years
Exam Duration:	130 minutes
Related Certifications:	AWS Certified Machine Learning - Specialty AWS Certified AI Practitioner
Passing Score:	720 (scaled score 100–1000)
Recommended Training:	AWS Training and Certification AWS Certified Machine Learning Engineer - Associate Official Exam Guide
Exam Registration:	AWS Certification Portal Pearson VUE Registration
Sample Questions:	Amazon MLA-C01 Sample Questions
Exam Way:	Online proctored or onsite at Pearson VUE testing centers
Pre Condition:	Recommended: 1+ year hands-on experience with AWS services and machine learning engineering; familiarity with Amazon SageMaker and related ML services. No mandatory prerequisite exams.
Official Syllabus URL:	https://docs.aws.amazon.com/aws-certification/latest/machine-learning-engineer-associate-01/machine-learning-engineer-associate-01.html

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Amazon MLA-C01 Exam Syllabus Topics:

Topic	Details
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 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	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.

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

NEW QUESTION # 215
An insurance company needs to automate claim compliance reviews because human reviews are expensive and error-prone. The company has a large set of claims and a compliance label for each. Each claim consists of a few sentences in English, many of which contain complex related information. Management would like to use Amazon SageMaker built-in algorithms to design a machine learning supervised model that can be trained to read each claim and predict if the claim is compliant or not.
Which approach should be used to extract features from the claims to be used as inputs for the downstream supervised task?

A. Apply Amazon SageMaker BlazingText in Word2Vec mode to claims in the training set. Send the derived features space as inputs for the downstream supervised task.
B. Derive a dictionary of tokens from claims in the entire dataset. Apply one-hot encoding to tokens found in each claim of the training set. Send the derived features space as inputs to an Amazon SageMaker built- in supervised learning algorithm.
C. Apply Amazon SageMaker BlazingText in classification mode to labeled claims in the training set to derive features for the claims that correspond to the compliant and non-compliant labels, respectively.
D. Apply Amazon SageMaker Object2Vec to claims in the training set. Send the derived features space as inputs for the downstream supervised task.

Answer: D

Explanation:
Amazon SageMaker Object2Vec generalizes the Word2Vec embedding technique for words to more complex objects, such as sentences and paragraphs. Since the supervised learning task is at the level of whole claims, for which there are labels, and no labels are available at the word level, Object2Vec needs be used instead of Word2Vec.

NEW QUESTION # 216
A logistics company has installed in-vehicle cameras for basic monitoring of its drivers. The company wants to improve driver safety by identifying distractions that could lead to accidents.
Which solution will meet this requirement with the LEAST operational effort?

A. Integrate a third-party driver monitoring system with Amazon Rekognition to monitor driver behavior and identify distractions.
B. Use Amazon SageMaker AI to customize an AI model to monitor driver behavior and identify distractions.
C. Use Amazon Rekognition eye gaze direction detection to monitor driver behavior and identify distractions.
D. Use Amazon Comprehend to analyze text-based driver feedback and identify distractions.

Answer: C

Explanation:
Option A is correct because Amazon Rekognition provides a built-in EyeDirection attribute that indicates the direction a person's eyes are gazing, expressed through pitch and yaw. AWS documentation describes EyeDirection as showing where the eyes are looking independently of head pose. For a driver-monitoring use case, this is directly useful for identifying whether a driver is looking away from the road and may be distracted.
AWS also announced that Rekognition's eye gaze direction detection is intended to support safety use cases and to help identify where users focus their attention. That makes it a strong match for monitoring in-vehicle cameras for distraction detection. Because Rekognition is a fully managed service with ready-to-use computer vision capabilities, it requires far less operational effort than collecting data, labeling it, training a custom model in SageMaker, and maintaining that model over time.
The other options are less appropriate. Option B could work technically, but building and maintaining a custom SageMaker model would require significantly more engineering and operational work than using a managed Rekognition feature. Option C introduces unnecessary complexity by adding a third-party system when AWS already provides a directly relevant managed capability. Option D is unrelated because Amazon Comprehend analyzes text, while the input here is camera footage. Since the question asks for the solution with the least operational effort , the best AWS-aligned answer is to use the managed Rekognition eye gaze detection capability. Therefore, the fully verified answer is A .

NEW QUESTION # 217
A company is using Amazon EMR. The company has a large dataset in Amazon S3 that needs to be ingested into Amazon SageMaker Feature Store. The dataset contains historical data and real-time streaming data.
The company must ensure that the Feature Store online store is updated with the most recent data as soon as the data becomes available. The company also must maintain a complete Feature Store offline store for batch processing.
Which solution will meet these requirements?

A. Use the Feature Store Spark connector to ingest the data as Spark DataFrames with the online store and offline store enabled.
B. Use the Feature Store Spark connector to ingest the data as Spark DataFrames with only the online store enabled.
C. Use the PutRecord API in Feature Store Runtime to ingest all the data into the online store.
D. Use the PutRecord API in Feature Store Runtime to ingest all the data into the offline store.

Answer: A

Explanation:
The SageMaker Feature Store Spark connector allows ingestion of large-scale data from Amazon EMR into Feature Store as Spark DataFrames. Enabling both the online store ensures real-time updates for the latest data, while the offline store maintains the full historical dataset for batch analytics. This setup meets both low-latency and historical processing requirements.

NEW QUESTION # 218
An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account B in the same Region.
Which solution will meet this requirement with the LEAST development effort?

A. Create an AWS Site-to-Site VPN connection between Account A and Account B to transfer the model.
B. Create a resource-based IAM policy. Use the Amazon Comprehend ImportModel API operation to copy the model to Account B.
C. Use Amazon S3 to make a copy of the model. Transfer the copy to Account B.
D. Use AWS DataSync to replicate the model from Account A to Account B.

Answer: B

NEW QUESTION # 219
A company is developing a new online application to gather information from customers. An ML engineer has developed a new ML model that will determine a score for each customer. The model will use the score to determine which product to display to the customer. The ML engineer needs to minimize response-time latency for the model. How should the ML engineer deploy the application in Amazon SageMaker to meet these requirements?

A. Configure a real-time inference endpoint.
B. Configure batch transform.
C. Configure a serverless inference endpoint.
D. Configure an asynchronous inference endpoint.

Answer: A

Explanation:
To minimize response-time latency, the ML model should be deployed to a real-time inference endpoint in Amazon SageMaker. This provides low-latency predictions by keeping the model loaded and ready to handle incoming requests, which is critical for an online application serving customers in real time.

NEW QUESTION # 220
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