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我們的Amazon MLA-C01題庫是由專業的IT團隊以最好的技術水準制作而得到的學習資料，其中整合最新的MLA-C01考試問題得到而來，以確保您購買我們的題庫資料是真實有效的，即使是新手也可以快速輕松獲得Amazon MLA-C01認證。對于如此有效的考古題，趕快加入購物車吧！付款之后您就可以立即下載所購買的MLA-C01題庫，這將會讓您在您的考試中獲得高分，并順利的通過MLA-C01考試。
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MLA-C01 是一個占有一定比重的認證科目。由於人數太少，加上需求太大，導致擁有 MLA-C01 認證的人成為薪酬最高的資訊技術專業認證人員。由於技能是本身擁有的，加上在全球範圍內的幾乎所有國家都有類似需求。所以，Amazon 的 MLA-C01 認證為網路工程師打開了通往全球各地的大門。如果您通過了MLA-C01 的考試，將證明你的專業技能和貢獻，展示你的知識與能力。如果你被認證為一個 MLA-C01 網路公司的專家，你就會成為這個領域中最有知識的專家之一。
問題 #217
A machine learning team has several large CSV datasets in Amazon S3. Historically, models built with the Amazon SageMaker Linear Learner algorithm have taken hours to train on similar-sized datasets. The team's leaders need to accelerate the training process.
What can a machine learning specialist do to address this concern?
答案：B
解題說明：
Amazon SageMaker Pipe mode streams the data directly to the container, which improves the performance of training jobs. In Pipe mode, your training job streams data directly from Amazon S3. Streaming can provide faster start times for training jobs and better throughput. With Pipe mode, you also reduce the size of the Amazon EBS volumes for your training instances.
問題 #218
A company needs to deploy a custom-trained classification ML model on AWS. The model must make near real-time predictions with low latency and must handle variable request volumes.
Which solution will meet these requirements?
答案：D
解題說明：
For near real-time inference with low latency and variable traffic, AWS recommends deploying models to managed SageMaker endpoints. By enabling auto scaling, the endpoint automatically adjusts the number of instances based on request volume, ensuring consistent performance while optimizing cost.
Amazon SageMaker Endpoints abstracts infrastructure management, health checks, scaling, and model deployment. This provides lower operational overhead than managing EC2 instances manually.
Batch transform is for offline inference. API Gateway with S3 cannot serve ML models. EC2-based deployments require manual scaling and monitoring.
Therefore, a SageMaker endpoint with auto scaling is the correct solution.
問題 #219
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?
答案：D
解題說明：
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.
問題 #220
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.
The ML engineer needs to use an Amazon SageMaker built-in algorithm to train the model.
Which algorithm should the ML engineer use to meet this requirement?
答案：C
解題說明：
Why Linear Learner?
* SageMaker'sLinear Learneralgorithm is well-suited for binary classification problems such as fraud detection. It handles class imbalance effectively by incorporating built-in options forweight balancing across classes.
* Linear Learner can capture patterns in the data while being computationally efficient.
Key Features of Linear Learner:
* Automatically weights minority and majority classes.
* Supports both classification and regression tasks.
* Handles interdependencies among features effectively through gradient optimization.
Steps to Implement:
* Use the SageMaker Python SDK to set up a training job with the Linear Learner algorithm.
* Configure the hyperparameters to enable balanced class weights.
* Train the model with the balanced dataset created using SageMaker Data Wrangler.
問題 #221
A company is using Amazon SageMaker AI to develop a credit risk assessment model. During model validation, the company finds that the model achieves 82% accuracy on the validation data. However, the model achieved 99% accuracy on the training data. The company needs to address the model accuracy issue before deployment.
Which solution will meet this requirement?
答案：C
解題說明：
The large gap between training accuracy (99%) and validation accuracy (82%) is a textbook case of overfitting. The model has learned patterns that fit the training data extremely well but do not generalize to unseen data.
AWS ML best practices recommend regularization techniques to address overfitting. Dropout layers randomly deactivate neurons during training, preventing the network from relying too heavily on specific paths. L1 and L2 regularization penalize large weights, reducing model complexity and improving generalization. k-fold cross-validation provides a more robust evaluation by training and validating the model across multiple data splits.
Option A increases complexity, which would worsen overfitting. Option C mixes valid ideas (dimensionality reduction) with unrelated changes (loss function choice) and is less targeted. Option D focuses on data quality but does not directly address model variance.
Therefore, implementing dropout, regularization, and k-fold cross-validation is the correct solution.
問題 #222
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