MLA-C01 Exam Actual Tests - MLA-C01 Certification Materials

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Amazon MLA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Model Development	30%	- ML frameworks (SageMaker, built-in algorithms) - Distributed training - Training and validation strategies - Transfer learning and fine-tuning - Hyperparameter optimization - Algorithm selection and model architecture
Topic 2: Data Processing	22%	- Data preprocessing and feature engineering - Data pipelining with AWS services ( Glue, Data Brew, etc.) - Handling imbalanced data - Data validation and quality assessment - Data ingestion and transformation
Topic 3: MLOps and Monitoring	28%	- Model monitoring and drift detection - CI/CD pipelines for ML - Incident response and remediation - Model lineage and reproducibility - Security and access management for ML - Cost optimization for ML workloads
Topic 4: Model Deployment and Inference	20%	- Inference optimization (latency, throughput) - Model versioning and rollback - A/B testing and shadow mode deployment - SageMaker endpoints configuration - Model deployment strategies (real-time, batch)

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q182-Q187):

NEW QUESTION # 182
Hotspot Question
An ML engineer is working on an ML model to predict the prices of similarly sized homes. The model will base predictions on several features The ML engineer will use the following feature engineering techniques to estimate the prices of the homes:
- Feature splitting
- Logarithmic transformation
- One-hot encoding
- Standardized distribution
Select the correct feature engineering techniques for the following list of features. Each feature engineering technique should be selected one time or not at all (Select three.)

Answer:

Explanation:

NEW QUESTION # 183
An IoT company uses Amazon SageMaker to train and test an XGBoost model for object detection. ML engineers need to monitor performance metrics when they train the model with variants in hyperparameters. The ML engineers also need to send Short Message Service (SMS) text messages after training is complete.
Which solution will meet these requirements?

A. Use AWS CloudTrail to monitor performance metrics. Use Amazon Simple Queue Service (Amazon SQS) for message delivery.
B. Use AWS CloudTrail to monitor performance metrics. Use Amazon Simple Notification Service (Amazon SNS) for message delivery.
C. Use Amazon CloudWatch to monitor performance metrics. Use Amazon Simple Queue Service (Amazon SQS) for message delivery.
D. Use Amazon CloudWatch to monitor performance metrics. Use Amazon Simple Notification Service (Amazon SNS) for message delivery.

Answer: D

NEW QUESTION # 184
An ML engineer wants to use a set of survey responses as training data for an ML classifier. All the survey responses are either "yes" or "no." The ML engineer needs to convert the responses into a feature that will produce better model training results. The ML engineer must not increase the dimensionality of the dataset.
Which methods will meet these requirements? (Choose two.)

A. Label encoding
B. Tokenization
C. Statistical imputation
D. Binary encoding
E. One-hot encoding

Answer: A,D

Explanation:
Both binary encoding and label encoding convert categorical yes/no responses into numerical values without increasing dimensionality. For example, mapping yes → 1 and no → 0. Unlike one-hot encoding, which would add extra dimensions, these methods keep the dataset compact and effective for training.

NEW QUESTION # 185
Case Study
An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.
Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data.
Which solution will meet this requirement with the LEAST operational effort?

A. Use AWS Glue DataBrew built-in features to oversample the minority class.
B. Use Amazon Athena to identify patterns that contribute to the imbalance. Adjust the dataset accordingly.
C. Use the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.
D. Use Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset.

Answer: C

NEW QUESTION # 186
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 Ground Truth for data labeling.
B. Use AWS Trainium instances for training.
C. Deploy models by using AWS Lambda functions.
D. Use PyTorch or TensorFlow with the distributed training option.
E. Use Amazon SageMaker Debugger to stop training jobs when non-converging conditions are detected.

Answer: B,E

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 # 187
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