MLA-C01最新テスト & MLA-C01問題例JpexamのMLA-C01問題集は実際のMLA-C01認定試験と同じです。この問題集は実際試験の問題をすべて含めることができるだけでなく、問題集のソフト版はMLA-C01試験の雰囲気を完全にシミュレートすることもできます。Jpexamの問題集を利用してから、試験を受けるときに簡単に対処し、楽に高い点数を取ることができます。 Amazon AWS Certified Machine Learning Engineer - Associate 認定 MLA-C01 試験問題 (Q88-Q93):質問 # 88
An ML engineer normalized training data by using min-max normalization in AWS Glue DataBrew. The ML engineer must normalize the production inference data in the same way as the training data before passing the production inference data to the model for predictions.
Which solution will meet this requirement?
A. Apply statistics from a well-known dataset to normalize the production samples.
B. Calculate a new set of min-max normalization statistics from a batch of production samples. Use these values to normalize all the production samples.
C. Keep the min-max normalization statistics from the training set. Use these values to normalize the production samples.
D. Calculate a new set of min-max normalization statistics from each production sample. Use these values to normalize all the production samples.
正解：C
解説：
To ensure consistency between training and inference, themin-max normalization statistics (min and max values)calculated during training must be retained and applied to normalize production inference data. Using the same statistics ensures that the model receives data in the same scale and distribution as it did during training, avoiding discrepancies that could degrade model performance. Calculating new statistics from production data would lead to inconsistent normalization and affect predictions.
質問 # 89
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. Change the instance type to Memory Optimized instances with sufficient memory for the training job.
B. Repartition the Parquet files by using Apache Spark on Amazon EMR. Use the repartitioned files for the training job.
C. Attach an Amazon Elastic Block Store (Amazon EBS) Provisioned IOPS SSD volume to the instance.
Store the files in the EBS volume.
D. Use the SageMaker AI distributed data parallelism (SMDDP) library with multiple instances to split the memory usage.
正解：B
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 # 90
A government agency is conducting a national census to assess program needs by area and city. The census form collects approximately 500 responses from each citizen. The agency needs to analyze the data to extract meaningful insights. The agency wants to reduce the dimensions of the high-dimensional data to uncover hidden patterns.
Which solution will meet these requirements?
A. Use the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm in Amazon SageMaker AI.
B. Use the principal component analysis (PCA) algorithm in Amazon SageMaker AI.
C. Use the k-means algorithm in Amazon SageMaker AI.
D. Use the Random Cut Forest (RCF) algorithm in Amazon SageMaker AI.
正解：B
解説：
The primary requirement is dimensionality reduction on high-dimensional structured data to uncover hidden patterns. Principal Component Analysis (PCA) is a linear dimensionality reduction technique specifically designed for this purpose and is available as a built-in algorithm in Amazon SageMaker.
PCA transforms the original features into a smaller set of orthogonal components that preserve the maximum possible variance. This makes PCA ideal for large tabular datasets such as census data, where hundreds of correlated variables are common.
t-SNE (Option B) is mainly used for visualization in very low dimensions (2D or 3D) and does not scale well for large datasets or production analysis. k-means (Option C) is a clustering algorithm, not a dimensionality reduction method. Random Cut Forest (Option D) is used for anomaly detection.
Therefore, PCA is the correct and AWS-recommended solution.
質問 # 91
An ML engineer needs to use Amazon SageMaker to fine-tune a large language model (LLM) for text summarization. The ML engineer must follow a low-code no-code (LCNC) approach.
Which solution will meet these requirements?
A. Use SageMaker Autopilot to fine-tune an LLM that is deployed on Amazon EC2 instances.
B. Use SageMaker Studio to fine-tune an LLM that is deployed on Amazon EC2 instances.
C. Use SageMaker Autopilot to fine-tune an LLM that is deployed by a custom API endpoint.
D. Use SageMaker Autopilot to fine-tune an LLM that is deployed by SageMaker JumpStart.
正解：D
質問 # 92
An ML engineer needs to deploy ML models to get inferences from large datasets in an asynchronous manner. The ML engineer also needs to implement scheduled monitoring of the data quality of the models. The ML engineer must receive alerts when changes in data quality occur.
Which solution will meet these requirements?
A. Deploy the models by using Amazon Elastic Container Service (Amazon ECS) on AWS Fargate.
Use Amazon EventBridge to monitor the data quality and to send alerts.
B. Deploy the models by using Amazon SageMaker batch transform. Use SageMaker Model Monitor to monitor the data quality and to send alerts.
C. Deploy the models by using scheduled AWS Batch jobs. Use AWS CloudTrail to monitor the data quality and to send alerts.
D. Deploy the models by using scheduled AWS Glue jobs. Use Amazon CloudWatch alarms to monitor the data quality and to send alerts.