信頼できる-権威のあるMLA-C01試験資料試験-試験の準備方法MLA-C01サンプル問題集

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Amazon MLA-C01 Exam Syllabus Topics:

SectionWeightObjectives
Model Deployment and Inference20%- A/B testing and shadow mode deployment
- Model deployment strategies (real-time, batch)
- Model versioning and rollback
- Inference optimization (latency, throughput)
- SageMaker endpoints configuration
Data Processing22%- Data ingestion and transformation
- Handling imbalanced data
- Data validation and quality assessment
- Data pipelining with AWS services ( Glue, Data Brew, etc.)
- Data preprocessing and feature engineering
Model Development30%- Hyperparameter optimization
- Transfer learning and fine-tuning
- Training and validation strategies
- Algorithm selection and model architecture
- ML frameworks (SageMaker, built-in algorithms)
- Distributed training
MLOps and Monitoring28%- Incident response and remediation
- CI/CD pipelines for ML
- Security and access management for ML
- Model monitoring and drift detection
- Cost optimization for ML workloads
- Model lineage and reproducibility

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Amazon AWS Certified Machine Learning Engineer - Associate 認定 MLA-C01 試験問題 (Q129-Q134):

質問 # 129
An ML engineer is setting up an Amazon SageMaker AI pipeline for an ML model. The pipeline must automatically initiate a re-training job if any data drift is detected.
How should the ML engineer set up the pipeline to meet this requirement?

正解：B

解説：
AWS provides Amazon SageMaker Model Monitor as a native solution for detecting data drift and model quality issues in production ML pipelines. Model Monitor continuously analyzes incoming inference data and compares it with baseline training data to identify schema drift, feature distribution drift, and data quality anomalies.
When drift thresholds are violated, Model Monitor generates CloudWatch metrics and alerts. These alerts can directly trigger an AWS Lambda function, which can then programmatically initiate a SageMaker retraining job or start a SageMaker Pipeline execution. This design is explicitly documented by AWS as the recommended architecture for automated retraining workflows.
Option A is incorrect because AWS Glue is a data integration service and does not provide ML-specific drift detection capabilities.
Option B is incorrect because Apache Flink is designed for stream processing, not ML data drift detection.
Option D is incorrect because Amazon QuickSight anomaly detection is intended for business intelligence metrics, not ML feature drift.
Therefore, using SageMaker Model Monitor with AWS Lambda automation is the correct, AWS-native solution for drift-driven retraining.


質問 # 130
A company needs to analyze a large dataset that is stored in Amazon S3 in Apache Parquet format. The company wants to use one-hot encoding for some of the columns.
The company needs a no-code solution to transform the data. The solution must store the transformed data back to the same S3 bucket for model training.
Which solution will meet these requirements?

正解：D

解説：
AWS Glue DataBrew is specifically designed to provide no-code and low-code data preparation for analytics and machine learning. It supports common file formats such as Apache Parquet and integrates directly with Amazon S3.
Using DataBrew, users can visually create recipes that apply transformations such as one-hot encoding without writing any code. Once the recipe is defined, a DataBrew job can be run to process the dataset and store the transformed output back into Amazon S3.
Options B, C, and D all require writing SQL or code, which violates the no-code requirement. AWS documentation clearly identifies DataBrew as the correct service for interactive, visual data transformation at scale.
Therefore, Option A is the correct solution.


質問 # 131
An ML engineer is using Amazon SageMaker to train a deep learning model that requires distributed training. After some training attempts, the ML engineer observes that the instances are not performing as expected. The ML engineer identifies communication overhead between the training instances.
What should the ML engineer do to MINIMIZE the communication overhead between the instances?

正解：A


質問 # 132
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.)

正解：B、C

解説：
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.


質問 # 133
A company wants to predict the success of advertising campaigns by considering the color scheme of each advertisement. An ML engineer is preparing data for a neural network model.
The dataset includes color information as categorical data.
Which technique for feature engineering should the ML engineer use for the model?

正解：A


質問 # 134
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