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Amazon MLA-C01 認定試験の出題範囲：

トピック	出題範囲
トピック 1	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.
トピック 2	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.
トピック 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.
トピック 4	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.

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Amazon AWS Certified Machine Learning Engineer - Associate 認定 MLA-C01 試験問題 (Q59-Q64):

質問 # 59
A company has implemented a data ingestion pipeline for sales transactions from its ecommerce website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch Service. The buffer interval of the Firehose stream is set for 60 seconds. An OpenSearch linear model generates real-time sales forecasts based on the data and presents the data in an OpenSearch dashboard.
The company needs to optimize the data ingestion pipeline to support sub-second latency for the real-time dashboard.
Which change to the architecture will meet these requirements?

A. Replace the Firehose stream with an Amazon Simple Queue Service (Amazon SQS) queue.
B. Use zero buffering in the Firehose stream. Tune the batch size that is used in the PutRecordBatch operation.
C. Replace the Firehose stream with an AWS DataSync task. Configure the task with enhanced fan-out consumers.
D. Increase the buffer interval of the Firehose stream from 60 seconds to 120 seconds.

正解：B

解説：
The primary requirement in this scenario is achieving sub-second latency for a real-time analytics dashboard powered by Amazon OpenSearch Service. The current architecture uses Amazon Data Firehose, which buffers incoming records based on time or size before delivering them to the destination. A buffer interval of
60 seconds introduces unavoidable latency, making it unsuitable for near-real-time or sub-second use cases.
According to AWS documentation, reducing or eliminating buffering in Firehose is the correct approach when low-latency ingestion is required. Setting the Firehose buffer interval to zero seconds forces Firehose to deliver records as soon as they are received. Additionally, tuning the PutRecordBatch batch size allows efficient ingestion while minimizing delivery delay. This configuration is explicitly recommended for latency- sensitive analytics pipelines.
Option B is incorrect because AWS DataSync is designed for batch-oriented data transfers between storage systems, not real-time streaming. Enhanced fan-out consumers are a feature of Amazon Kinesis Data Streams, not DataSync, making this option invalid.
Option C directly contradicts the requirement. Increasing the buffer interval from 60 seconds to 120 seconds would further increase latency and degrade real-time performance.
Option D is also incorrect because Amazon SQS is a message queueing service, not a streaming ingestion service optimized for indexing data into OpenSearch with minimal latency. Using SQS would add additional processing layers and would not inherently provide sub-second ingestion into OpenSearch.
Therefore, using zero buffering in the Firehose stream and tuning the PutRecordBatch batch size is the only change that aligns with AWS best practices for achieving sub-second latency in real-time analytics pipelines.

質問 # 60
A company plans to deploy an ML model for production inference on an Amazon SageMaker endpoint. The average inference payload size will vary from 100 MB to 300 MB. Inference requests must be processed in 60 minutes or less.
Which SageMaker inference option will meet these requirements?

A. Real-time inference
B. Asynchronous inference
C. Batch transform
D. Serverless inference

正解：B

質問 # 61
A company wants to use Amazon SageMaker AI to host an ML model that runs on CPU for real-time predictions. The model has intermittent traffic during business hours and periods of no traffic after business hours.
Which hosting option will serve inference requests in the MOST cost-effective manner?

A. Deploy the model to a real-time endpoint and activate it only during business hours using AWS Lambda.
B. Deploy the model to a SageMaker AI Serverless Inference endpoint with provisioned concurrency during business hours.
C. Deploy the model to an asynchronous inference endpoint with auto scaling to zero.
D. Deploy the model to a real-time endpoint with scheduled auto scaling.

正解：B

解説：
AWS recommends SageMaker Serverless Inference for workloads with intermittent or unpredictable traffic.
Serverless inference automatically scales compute resources to zero when idle, eliminating costs during periods with no traffic.
For business-hour traffic spikes, provisioned concurrency ensures low-latency responses while still avoiding the cost of continuously running instances. This model is especially cost-effective for CPU-based inference workloads.
Real-time endpoints incur costs even when idle, and asynchronous inference is designed for long-running jobs rather than low-latency predictions.
AWS documentation explicitly states that Serverless Inference is the most cost-effective option for intermittent real-time workloads.
Therefore, Option B is the correct choice.

質問 # 62
An ML engineer wants to run a training job on Amazon SageMaker AI. The training job will train a neural network by using multiple GPUs. The training dataset is stored in Parquet format.
The ML engineer discovered that the Parquet dataset contains files too large to fit into the memory of the SageMaker AI training instances.
Which solution will fix the memory problem?

A. Use the SageMaker AI distributed data parallelism (SMDDP) library with multiple instances to split the memory usage.
B. Attach an Amazon Elastic Block Store (Amazon EBS) Provisioned IOPS SSD volume to the instance.
Store the files in the EBS volume.
C. Repartition the Parquet files by using Apache Spark on Amazon EMR. Use the repartitioned files for the training job.
D. Change the instance type to Memory Optimized instances with sufficient memory for the training job.

正解：C

解説：
The issue is caused by oversized Parquet files that cannot be efficiently read into memory during training. The most effective and scalable solution is to repartition the dataset into smaller Parquet files.
AWS best practices for large-scale ML training recommend optimizing data layout, not simply increasing memory. By using Apache Spark on Amazon EMR, the ML engineer can repartition the Parquet files into smaller chunks that can be streamed and processed efficiently by SageMaker training jobs.
Attaching EBS volumes (Option A) increases storage capacity but does not solve in-memory constraints.
Changing to memory-optimized instances (Option C) increases cost and does not address long-term scalability. SMDDP (Option D) distributes gradients and computation, not dataset file sizes.
Therefore, repartitioning the Parquet files is the correct solution.

質問 # 63
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?

A. Ingest data into SageMaker Feature Store from the S3 bucket. Apply tags and indexes.
B. Use Amazon Athena. Create tables by using CREATE TABLE AS SELECT (CTAS) queries to group data.
C. Use the Generate Data Insights function in SageMaker Data Wrangler.
D. Use AWS Lake Formation. Apply tag-based control on the data.

正解：A

解説：
Option A is correct because Amazon SageMaker Feature Store is the AWS service designed to act as a centralized repository for ML features that are used consistently across training and inference . AWS documentation states that SageMaker Feature Store simplifies how you create, store, share, and manage features for data exploration, model training, and model inference. This directly matches the requirement to create a repository for streamed song ratings that will be used in both batch training and real-time inference.
The most important requirement in the question is that the data must stay synchronized between batch training and real-time inference . AWS documents explain that Feature Store provides both an offline store and an online store . The offline store is used for historical data, model training, and batch inference, while the online store is a low-latency, high-availability store intended for real-time lookup during inference. This dual-store design is exactly why Feature Store is used to maintain feature consistency across training and serving workflows. AWS Well-Architected guidance also explicitly says Feature Store provides online storage for real-time inference and offline storage for model training and batch inference.
The other options do not solve the full problem. Athena CTAS can organize query results but does not provide a synchronized feature repository for online and offline ML use. Lake Formation governs access to data lakes but is not a feature repository for training and inference consistency. Data Wrangler Generate Data Insights is for analysis and preparation, not synchronized feature serving. Therefore, the best AWS- documented answer is A .

質問 # 64
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2026年MogiExamの最新MLA-C01 PDFダンプおよびMLA-C01試験エンジンの無料共有：https://drive.google.com/open?id=1uyjwU05jJnQqOCTlAyy9lLxElbW5WjQB