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Amazon MLA-C01 Exam Syllabus Topics:

SectionWeightObjectives
ML Model Development26%- Select appropriate modeling approach
  • 1. Use cases and service recommendations
  • 2. Problem type: classification, regression, clustering, forecasting, NLP, computer vision
  • 3. Algorithm selection: traditional ML, deep learning, pre-built models
- Train, tune, and refine models
  • 1. Distributed training and managed services
  • 2. Training options: built-in algorithms, custom containers, frameworks
  • 3. Hyperparameter optimization: Amazon SageMaker Automatic Model Tuning
- Evaluate and analyze model performance
  • 1. Bias detection and mitigation
  • 2. Model explainability: Amazon SageMaker Clarify
  • 3. Metrics: accuracy, precision, recall, F1, RMSE, MAE, confusion matrix
Deployment and Orchestration of ML Workflows22%- 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
- Configure deployment for scalability and availability
  • 1. Infrastructure as code: AWS CloudFormation, Terraform
  • 2. A/B testing and canary deployment
  • 3. Auto-scaling, load balancing, and high availability
- Choose deployment infrastructure and pattern
  • 1. Model packaging and versioning: Amazon SageMaker Model Registry
  • 2. Real-time inference, batch transform, serverless, edge deployment
  • 3. Infrastructure: Amazon SageMaker endpoints, AWS Lambda, Amazon ECS, Amazon EKS
ML Solution Monitoring, Maintenance, and Security24%- Secure ML solutions and resources
  • 1. Access control: IAM roles, policies, permissions
  • 2. Data encryption: at rest and in transit
  • 3. Compliance, governance, and data privacy
- Optimize and maintain workloads
  • 1. Logging, auditing, and troubleshooting
  • 2. Model retraining and update strategies
  • 3. Cost optimization and resource management
- Monitor model and data quality
  • 1. Amazon SageMaker Model Monitor
  • 2. Model drift detection: data drift, concept drift
  • 3. Performance monitoring and alerting
Data Preparation for Machine Learning28%- Ensure data integrity and prepare for modeling
  • 1. Feature store usage: Amazon SageMaker Feature Store
  • 2. Data validation, quality checks, and profiling
  • 3. Data splitting: train/validation/test sets
- Ingest and store data
  • 1. Data formats: Parquet, JSON, CSV, ORC, Avro, RecordIO
  • 2. Data ingestion services: Amazon Kinesis, AWS Glue, Amazon S3, Amazon Athena
  • 3. Data storage options: object storage, data lakes, databases
- Transform data and perform feature engineering
  • 1. Tools: Amazon SageMaker Processing, AWS Glue DataBrew, Pandas, PySpark
  • 2. Feature selection, transformation, and scaling
  • 3. Data cleaning, normalization, and encoding

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q88-Q93):

NEW QUESTION # 88
An ML engineer needs to deploy a trained model that is based on a genetic algorithm. The algorithm solves a complex problem and can take several minutes to generate predictions.
When the model is deployed, the model needs to access large amounts of data to process requests. The requests can involve as much as 100 MB of data.
Which deployment solution will meet these requirements with the LEAST operational overhead?

Answer: C

Explanation:
SageMaker Asynchronous Inference is designed for models with long processing times and large payloads. It can handle input data up to 1 GB and avoids holding open connections during long inference runs, reducing operational overhead compared to managing EC2 or ECS infrastructure.
This makes it the best fit for the genetic algorithm model that takes minutes and processes large requests.


NEW QUESTION # 89
A music streaming company constantly streams song ratings from an application to an Amazon S3 bucket.
The company wants to use the ratings as an input for training and inference of an Amazon SageMaker AI model.
The company has an AWS Glue Data Catalog that is configured with the S3 bucket as the source. An ML engineer needs to implement a solution to create a repository for this data. The solution must ensure that the data stays synchronized during batch training and real-time inference.
Which solution will meet these requirements?

Answer: C


NEW QUESTION # 90
An ML engineer needs to deploy four ML models in an Amazon SageMaker inference pipeline.
The models were built with different frameworks. The ML engineer also needs to give clients the ability to use the invoke_endpoint call to perform inference for each model. Which solution will meet these requirements MOST cost-effectively?

Answer: A

Explanation:
A SageMaker multi-container endpoint allows deployment of multiple models built with different frameworks in a single endpoint. Each container can host a model with its required framework, and clients can use the same invoke_endpoint call while specifying the target container. This meets the requirement for framework diversity and is more cost-effective than running separate single-model endpoints.


NEW QUESTION # 91
An ML engineer wants to run a training job on Amazon SageMaker AI by using multiple GPUs. The training dataset is stored in Apache Parquet format.
The Parquet files are too large to fit into the memory of the SageMaker AI training instances.
Which solution will fix the memory problem?

Answer: B

Explanation:
Large Parquet files can cause out-of-memory (OOM) issues during training if individual files exceed the memory capacity of the training instances. AWS documentation recommends repartitioning large datasets into smaller files to enable efficient streaming and parallel loading.
By using Apache Spark on Amazon EMR to repartition the Parquet files, the ML engineer can split the dataset into multiple smaller files that can be read incrementally during training. This approach avoids loading a single large file into memory and improves data parallelism.
Attaching larger EBS volumes increases storage capacity but does not solve memory constraints. Switching to memory-optimized instances increases cost and is not necessary when the dataset can be restructured.
SageMaker distributed data parallelism focuses on model parameter synchronization across GPUs, not dataset file size.
AWS best practices explicitly recommend partitioning large Parquet datasets to improve memory efficiency during training.
Therefore, Option B is the correct and AWS-aligned solution.


NEW QUESTION # 92
Hotspot Question
An ecommerce company is using Amazon SageMaker Clarify Foundation Model Evaluations (FMEval) to evaluate ML models.
Select the correct model evaluation task from the following list for each ecommerce use case.
Each model evaluation task should be selected one time.
- Classification evaluation
- Open-ended generation
- Question answering
- Text summarization

Answer:

Explanation:


NEW QUESTION # 93
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