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Amazon MLA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
ML Solution Monitoring, Maintenance, and Security	24%	- Optimize and maintain workloads 1. Cost optimization and resource management 2. Logging, auditing, and troubleshooting 3. Model retraining and update strategies - Monitor model and data quality 1. Model drift detection: data drift, concept drift 2. Performance monitoring and alerting 3. Amazon SageMaker Model Monitor - Secure ML solutions and resources 1. Data encryption: at rest and in transit 2. Access control: IAM roles, policies, permissions 3. Compliance, governance, and data privacy
Data Preparation for Machine Learning	28%	- Ingest and store data 1. Data formats: Parquet, JSON, CSV, ORC, Avro, RecordIO 2. Data storage options: object storage, data lakes, databases 3. Data ingestion services: Amazon Kinesis, AWS Glue, Amazon S3, Amazon Athena - Transform data and perform feature engineering 1. Tools: Amazon SageMaker Processing, AWS Glue DataBrew, Pandas, PySpark 2. Data cleaning, normalization, and encoding 3. Feature selection, transformation, and scaling - Ensure data integrity and prepare for modeling 1. Data splitting: train/validation/test sets 2. Data validation, quality checks, and profiling 3. Feature store usage: Amazon SageMaker Feature Store
ML Model Development	26%	- Evaluate and analyze model performance 1. Model explainability: Amazon SageMaker Clarify 2. Metrics: accuracy, precision, recall, F1, RMSE, MAE, confusion matrix 3. Bias detection and mitigation - Train, tune, and refine models 1. Training options: built-in algorithms, custom containers, frameworks 2. Distributed training and managed services 3. Hyperparameter optimization: Amazon SageMaker Automatic Model Tuning - Select appropriate modeling approach 1. Algorithm selection: traditional ML, deep learning, pre-built models 2. Use cases and service recommendations 3. Problem type: classification, regression, clustering, forecasting, NLP, computer vision
Deployment and Orchestration of ML Workflows	22%	- Automate and orchestrate ML pipelines 1. CI/CD integration: AWS CodePipeline, AWS CodeBuild 2. Workflow automation and event-driven processing 3. ML pipelines: Amazon SageMaker Pipelines - Choose deployment infrastructure and pattern 1. Real-time inference, batch transform, serverless, edge deployment 2. Model packaging and versioning: Amazon SageMaker Model Registry 3. Infrastructure: Amazon SageMaker endpoints, AWS Lambda, Amazon ECS, Amazon EKS - Configure deployment for scalability and availability 1. Infrastructure as code: AWS CloudFormation, Terraform 2. Auto-scaling, load balancing, and high availability 3. A/B testing and canary deployment

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q205-Q210):

NEW QUESTION # 205
A company has an existing Amazon SageMaker model (v1) on a production endpoint. The company develops a new model version (v2) and needs to test v2 in production before substituting v2 for v1.
The company needs to implement a solution to minimize the risk of v2 generating incorrect output in production. The solution must prevent any disruption of production traffic during the change to v2.
Which solution will meet these requirements?

A. Deploy v2 into a shadow variant that samples 100% of the inference requests. Collect all the output in an Amazon S3 bucket. If v2 performs as expected, promote v2 to production.
B. Create a second production variant for v2. Assign 10% of the traffic to v2 and 90% of the traffic to v1. Collect all the output of v2 in an Amazon S3 bucket. If v2 performs as expected, switch all the traffic to v2.
C. Deploy v2 to a new endpoint. Turn on data capturing for the production endpoint. Write a script to pass 100% of input data to v2. If v2 performs as expected, deactivate the v1 endpoint and direct the traffic to v2.
D. Create a second production variant for v2. Assign 1% of the traffic to v2 and 99% of the traffic to v1. Collect all the output of v2 in an Amazon S3 bucket. If v2 performs as expected, switch all the traffic to v2.

Answer: A

Explanation:
A shadow variant allows the new model (v2) to receive a copy of 100% of production traffic while only v1's outputs are returned to users. This enables safe side-by-side evaluation of v2 without impacting production responses, minimizing risk and ensuring no disruption of live traffic until v2 is validated and promoted.

NEW QUESTION # 206
An ML engineer notices class imbalance in an image classification training job.
What should the ML engineer do to resolve this issue?

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

Answer: C

NEW QUESTION # 207
A company is developing an ML model for a customer. The training data is stored in an Amazon S3 bucket in the customer's AWS account (Account A). The company runs Amazon SageMaker AI training jobs in a separate AWS account (Account B).
The company defines an S3 bucket policy and an IAM policy to allow reads to the S3 bucket.
Which additional steps will meet the cross-account access requirement?

A. Create the S3 bucket policy in Account B. Attach the IAM policy to an IAM role that SageMaker AI uses in Account B.
B. Create the S3 bucket policy in Account A. Attach the IAM policy to an IAM role that SageMaker AI uses in Account B.
C. Create the S3 bucket policy in Account B. Attach the IAM policy to an IAM role that SageMaker AI uses in Account A.
D. Create the S3 bucket policy in Account A. Attach the IAM policy to an IAM role that SageMaker AI uses in Account A.

Answer: B

Explanation:
For cross-account Amazon S3 access, AWS requires two components:
* An S3 bucket policy in the owning account (Account A) that grants access to a principal in another account
* An IAM role policy in the consuming account (Account B) that allows the service to access the bucket Amazon SageMaker training jobs assume an IAM role in the account where the job runs-in this case, Account B. Therefore, the IAM policy must be attached to the SageMaker execution role in Account B.
The S3 bucket policy must reside in Account A because bucket policies are owned and enforced by the bucket owner. This policy explicitly allows the IAM role from Account B to read the training data.
Any other combination fails either because the policy is in the wrong account or because the role is not the one used by SageMaker.
AWS documentation clearly describes this pattern as the correct way to grant cross-account access for SageMaker training jobs.
Therefore, Option B is the correct and AWS-aligned solution.

NEW QUESTION # 208
An ML engineer develops a neural network model to predict whether customers will continue to subscribe to a service. The model performs well on training data. However, the accuracy of the model decreases significantly on evaluation data.
The ML engineer must resolve the model performance issue.
Which solution will meet this requirement?

A. Penalize large weights by using L1 or L2 regularization.
B. Remove dropout layers from the neural network.
C. Capture complex patterns by increasing the number of layers.
D. Train the model for longer by increasing the number of epochs.

Answer: A

Explanation:
This scenario clearly describes an overfitting problem. The neural network performs well on training data but fails to generalize to evaluation data. According to AWS Machine Learning and deep learning best practices, overfitting occurs when a model learns noise or overly specific patterns in the training data instead of generalizable relationships.
L1 and L2 regularization are well-established techniques to combat overfitting. They work by penalizing large weight values during optimization, effectively constraining the model's complexity. L1 regularization encourages sparsity, while L2 regularization (weight decay) smooths the weight distribution. AWS documentation recommends regularization as a primary method for improving generalization when a model shows a large gap between training and evaluation performance.
Option B is incorrect because removing dropout layers would increase overfitting, not reduce it. Dropout is itself a regularization technique.
Option C would likely worsen overfitting, as training for more epochs allows the model to memorize the training data even further.
Option D increases model complexity, which again exacerbates overfitting rather than resolving it.
Therefore, penalizing large weights using L1 or L2 regularization is the correct solution.

NEW QUESTION # 209
A company has several teams that have developed separate prediction models on their own laptops. The teams developed the models by using Python with scikit-learn and TensorFlow frameworks.
The company must rebuild the models and must integrate the models into an ML infrastructure that the company manages by using Amazon SageMaker. The company also must incorporate the models into a model registry.
Which solution will meet these requirements with the LEAST operational overhead?

A. Import the Python-based models into SageMaker. Rebuild the scikit-learn and TensorFlow models in SageMaker. Register all the models in the SageMaker Model Registry.
B. Use code from the laptops to create containers for the models. Use the bring your own container (BYOC) functionality of SageMaker to import and use the models. Register the models in the SageMaker Model Registry.
C. Export the models from the laptops to an Amazon S3 bucket. Use an Amazon API Gateway REST API and AWS Lambda functions with SageMaker endpoints to access the models. Register the models in the SageMaker Model Registry.
D. Import the models into the SageMaker Model Registry. Use SageMaker to run the imported models.

Answer: A

Explanation:
The least operational overhead comes from directly importing the scikit-learn and TensorFlow models into SageMaker, rebuilding them using the respective prebuilt SageMaker frameworks, and then registering them in the SageMaker Model Registry. This leverages managed framework containers provided by SageMaker, avoids custom container management, and integrates seamlessly with the registry.

NEW QUESTION # 210
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