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Amazon MLA-C01 Exam Overview:

Certification Vendor:	Amazon Web Services (AWS)
Exam Name:	AWS Certified Machine Learning Engineer – Associate (MLA-C01)
Exam Number:	MLA-C01
Exam Format:	Ordering, Multiple choice, Matching, Multiple response
Exam Price:	USD 150
Real Exam Qty:	65 scored questions + 15 unscored questions
Exam Duration:	130 minutes
Related Certifications:	AWS Certified DevOps Engineer – Professional AWS Certified Solutions Architect – Associate AWS Certified AI Practitioner AWS Certified Data Engineer – Associate
Available Languages:	Simplified Chinese, Korean, Japanese, English
Passing Score:	720/1000
Certificate Validity Period:	3 years
Recommended Training:	AWS Skill Builder - ML Engineer Associate Exam Prep Amazon SageMaker Documentation
Exam Registration:	AWS Certification Registration
Sample Questions:	Amazon MLA-C01 Sample Questions
Exam Way:	Online proctored or test center exam
Pre Condition:	Recommended: ~1 year experience with Amazon SageMaker and AWS-based ML or data engineering roles
Official Syllabus URL:	https://aws.amazon.com/certification/certified-machine-learning-engineer-associate/

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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 (Q53-Q58):

NEW QUESTION # 53
A company needs to combine data from multiple sources. The company must use Amazon Redshift Serverless to query an AWS Glue Data Catalog database and underlying data that is stored in an Amazon S3 bucket.
Select and order the correct steps from the following list to meet these requirements. Select each step one time or not at all. (Select and order three.)
* Attach the IAM role to the Redshift cluster.
* Attach the IAM role to the Redshift namespace.
* Create an external database in Amazon Redshift to point to the Data Catalog schema.
* Create an external schema in Amazon Redshift to point to the Data Catalog database.
* Create an IAM role for Amazon Redshift to use to access only the S3 bucket that contains underlying data.
* Create an IAM role for Amazon Redshift to use to access the Data Catalog and the S3 bucket that contains underlying data.

Answer:

Explanation:

Explanation:
Step 1
Create an IAM role for Amazon Redshift to use to access the Data Catalog and the S3 bucket that contains underlying data.
This role must include:
Permissions for AWS Glue Data Catalog (e.g., glue:GetDatabase, glue:GetTables) Permissions for the Amazon S3 bucket that stores the underlying data Step 2 Attach the IAM role to the Redshift namespace.
Redshift Serverless uses a namespace, not a cluster, so the role must be associated with the namespace to allow Redshift to assume it when querying external data.
Step 3
Create an external schema in Amazon Redshift to point to the Data Catalog database.
The external schema maps Redshift to the Glue Data Catalog database so Redshift can query the tables stored in S3.

NEW QUESTION # 54
An ML engineer is developing a fraud detection model by using the Amazon SageMaker XGBoost algorithm.
The model classifies transactions as either fraudulent or legitimate.
During testing, the model excels at identifying fraud in the training dataset. However, the model is inefficient at identifying fraud in new and unseen transactions.
What should the ML engineer do to improve the fraud detection for new transactions?

A. Remove some irrelevant features from the training dataset.
B. Decrease the value of the max_depth hyperparameter.
C. Increase the learning rate.
D. Increase the value of the max_depth hyperparameter.

Answer: B

Explanation:
A high max_depth value in XGBoost can lead to overfitting, where the model learns the training dataset too well but fails to generalize to new and unseen data. By decreasing the max_depth, the model becomes less complex, reducing overfitting and improving its ability to detect fraud in new transactions. This adjustment helps the model focus on general patterns rather than memorizing specific details in the training data.

NEW QUESTION # 55
A company wants to improve the sustainability of its ML operations.
Which actions will reduce the energy usage and computational resources that are associated with the company's training jobs? (Choose two.)

A. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected.
B. Use Amazon SageMaker Ground Truth for data labeling.
C. Use AWS Trainium instances for training.
D. Use PyTorch or TensorFlow with the distributed training option.
E. Deploy models by using AWS Lambda functions.

Answer: A,C

Explanation:
SageMaker Debuggercan identify when a training job is not converging or is stuck in a non-productive state.
By stopping these jobs early, unnecessary energy and computational resources are conserved, improving sustainability.
AWS Trainiuminstances are purpose-built for ML training and are optimized for energy efficiency and cost- effectiveness. They use less energy per training task compared to general-purpose instances, making them a sustainable choice.

NEW QUESTION # 56
A company has an ML model that needs to run one time each night to predict stock values. The model input is
3 MB of data that is collected during the current day. The model produces the predictions for the next day.
The prediction process takes less than 1 minute to finish running.
How should the company deploy the model on Amazon SageMaker to meet these requirements?

A. Use an asynchronous inference endpoint. Set the InitialInstanceCount parameter to 0.
B. Use a serverless inference endpoint. Set the MaxConcurrency parameter to 1.
C. Use a multi-model serverless endpoint. Enable caching.
D. Use a real-time endpoint. Configure an auto scaling policy to scale the model to 0 when the model is not in use.

Answer: B

Explanation:
A serverless inference endpoint in Amazon SageMaker is ideal for use cases where the model is invoked infrequently, such as running one time each night. It eliminates the cost of idle resources when the model is not in use. Setting the MaxConcurrency parameter to 1 ensures cost-efficiency while supporting the required single nightly invocation. This solution minimizes costs and matches the requirement to process a small amount of data quickly.

NEW QUESTION # 57
A company is building an Amazon SageMaker AI pipeline for an ML model. The pipeline uses distributed processing and distributed training.
An ML engineer needs to encrypt network communication between instances that run distributed jobs. The ML engineer configures the distributed jobs to run in a private VPC.
What should the ML engineer do to meet the encryption requirement?

A. Enable VPC flow logs.
B. Enable inter-container traffic encryption.
C. Configure traffic encryption by using security groups.
D. Enable network isolation.

Answer: B

Explanation:
In Amazon SageMaker, distributed training and distributed processing jobs often involve multiple instances exchanging data over the network. By default, when these jobs run inside a VPC, network traffic remains private but is not automatically encrypted between instances. When compliance or security requirements mandate encryption of in-transit data, additional configuration is required.
The correct solution is to enable inter-container traffic encryption, which ensures that all network communication between containers running on different instances is encrypted using TLS. Amazon SageMaker provides a built-in feature for this purpose. When inter-container traffic encryption is enabled, SageMaker automatically configures secure communication channels between all nodes participating in a distributed job, including training clusters and processing jobs.
Option A (Network isolation) is incorrect because network isolation prevents containers from making outbound network calls and accessing the internet. It does not encrypt traffic between instances.
Option B (Security groups) is incorrect because security groups control network access and traffic flow, not encryption. They can restrict which instances can communicate, but they do not provide data-in-transit encryption.
Option D (VPC flow logs) is incorrect because VPC flow logs are used for monitoring and auditing network traffic, not for encrypting it.
AWS documentation explicitly states that enabling inter-container traffic encryption is the recommended and supported approach for encrypting data exchanged between instances during distributed SageMaker jobs. This feature aligns with enterprise security best practices and regulatory requirements for protecting sensitive ML training data in transit.
Therefore, Option C is the only solution that directly fulfills the encryption requirement for distributed SageMaker workloads.

NEW QUESTION # 58
......

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