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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.

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q146-Q151):

NEW QUESTION # 146
An ML engineer notices class imbalance in an image classification training job.
What should the ML engineer do to resolve this issue?

A. Transform some of the images in the dataset.
B. Apply random oversampling on the dataset.
C. Reduce the size of the dataset.
D. Apply random data splitting on the dataset.

Answer: B

NEW QUESTION # 147
An ML engineer is using AWS CodeDeploy to deploy new container versions for inference on Amazon ECS.
The deployment must shift 10% of traffic initially, and the remaining 90% must shift within 10-15 minutes.
Which deployment configuration meets these requirements?

A. CodeDeployDefault.ECSCanary10Percent15Minutes
B. CodeDeployDefault.ECSAllAtOnce
C. CodeDeployDefault.LambdaCanary10Percent15Minutes
D. CodeDeployDefault.LambdaLinear10PercentEvery10Minutes

Answer: A

Explanation:
AWS CodeDeploy provides predefined deployment configurations for ECS that support canary and linear traffic shifting. The ECSCanary10Percent15Minutes configuration shifts 10% of traffic initially, waits 15 minutes, and then shifts the remaining traffic.
This matches the exact requirement: a 10% initial shift followed by the remaining 90% within the specified time window.
Lambda deployment configurations are not applicable to ECS. ECSAllAtOnce does not perform gradual traffic shifting.
AWS documentation explicitly defines ECSCanary10Percent15Minutes for controlled, low-risk ECS deployments.
Therefore, Option C is the correct and AWS-verified answer.

NEW QUESTION # 148
A company stores historical data in .csv files in Amazon S3. Only some of the rows and columns in the .csv files are populated. The columns are not labeled. An ML engineer needs to prepare and store the data so that the company can use the data to train ML models.
Select and order the correct steps from the following list to perform this task. Each step should be selected one time or not at all. (Select and order three.)
* Create an Amazon SageMaker batch transform job for data cleaning and feature engineering.
* Store the resulting data back in Amazon S3.
* Use Amazon Athena to infer the schemas and available columns.
* Use AWS Glue crawlers to infer the schemas and available columns.
* Use AWS Glue DataBrew for data cleaning and feature engineering.

Answer:

Explanation:

Explanation:
Step 1: Use AWS Glue crawlers to infer the schemas and available columns.
Step 2: Use AWS Glue DataBrew for data cleaning and feature engineering.
Step 3: Store the resulting data back in Amazon S3.
* Step 1: Use AWS Glue Crawlers to Infer Schemas and Available Columns
* Why? The data is stored in .csv files with unlabeled columns, and Glue Crawlers can scan the raw data in Amazon S3 to automatically infer the schema, including available columns, data types, and any missing or incomplete entries.
* How? Configure AWS Glue Crawlers to point to the S3 bucket containing the .csv files, and run the crawler to extract metadata. The crawler creates a schema in the AWS Glue Data Catalog, which can then be used for subsequent transformations.
* Step 2: Use AWS Glue DataBrew for Data Cleaning and Feature Engineering
* Why? Glue DataBrew is a visual data preparation tool that allows for comprehensive cleaning and transformation of data. It supports imputation of missing values, renaming columns, feature engineering, and more without requiring extensive coding.
* How? Use Glue DataBrew to connect to the inferred schema from Step 1 and perform data cleaning and feature engineering tasks like filling in missing rows/columns, renaming unlabeled columns, and creating derived features.
* Step 3: Store the Resulting Data Back in Amazon S3
* Why? After cleaning and preparing the data, it needs to be saved back to Amazon S3 so that it can be used for training machine learning models.
* How? Configure Glue DataBrew to export the cleaned data to a specific S3 bucket location. This ensures the processed data is readily accessible for ML workflows.
Order Summary:
* Use AWS Glue crawlers to infer schemas and available columns.
* Use AWS Glue DataBrew for data cleaning and feature engineering.
* Store the resulting data back in Amazon S3.
This workflow ensures that the data is prepared efficiently for ML model training while leveraging AWS services for automation and scalability.

NEW QUESTION # 149
A company wants to migrate ML models from an on-premises environment to Amazon SageMaker AI. The models are based on the PyTorch algorithm. The company needs to reuse its existing custom scripts as much as possible.
Which SageMaker AI feature should the company use?

A. SageMaker AI built-in algorithms
B. SageMaker AI script mode
C. SageMaker JumpStart
D. SageMaker Canvas

Answer: B

Explanation:
SageMaker script mode allows ML engineers to bring existing training scripts written for frameworks such as PyTorch and TensorFlow directly into SageMaker with minimal changes. AWS documentation explicitly states that script mode is designed to support migration of existing ML workloads.
With script mode, the user provides a custom training script, and SageMaker handles infrastructure provisioning, distributed training, logging, and model artifact storage. This makes script mode ideal for companies that want to reuse established codebases without rewriting them.
Built-in algorithms require adopting AWS-provided implementations. SageMaker Canvas is a no-code tool, and JumpStart provides pretrained models and templates but does not focus on custom script reuse.
Therefore, Option D is the correct and AWS-recommended choice.

NEW QUESTION # 150
An ML engineer is using an Amazon SageMaker Studio notebook to train a neural network by creating an estimator. The estimator runs a Python training script that uses Distributed Data Parallel (DDP) on a single instance that has more than one GPU.
The ML engineer discovers that the training script is underutilizing GPU resources. The ML engineer must identify the point in the training script where resource utilization can be optimized.
Which solution will meet this requirement?

A. Use Amazon CloudWatch metrics to create a report that describes GPU utilization over time.
B. Use AWS CloudTrail to create a report that describes GPU utilization and GPU memory utilization over time.
C. Add SageMaker Profiler annotations to the training script. Run the script and generate a report from the results.
D. Create a default monitor in Amazon SageMaker Model Monitor and suggest a baseline. Generate a report based on the constraints and statistics the monitor generates.

Answer: C

Explanation:
To pinpoint inefficiencies inside a training script, AWS recommends using Amazon SageMaker Profiler.
SageMaker Profiler provides fine-grained visibility into CPU, GPU, memory, I/O usage, and framework-level operations during training.
By adding profiler annotations directly to the training script, the ML engineer can identify bottlenecks such as inefficient data loading, synchronization delays in DDP, or idle GPU time between training steps.
CloudWatch metrics provide high-level utilization trends but cannot identify exact code-level inefficiencies.
CloudTrail is an auditing service and is irrelevant to performance profiling. Model Monitor focuses on data and model quality, not training resource utilization.
Therefore, SageMaker Profiler is the correct tool.

NEW QUESTION # 151
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