Sample Page

No.50
一家公司将有关用户点击的时间序列数据存储在 Amazon S3 存储桶中。原始数据每天包含数百万行用户活动。ML 工程师访问数据以开发他们的 ML 模型。
ML 工程师需要使用 Amazon Athena 生成每日报告并分析过去 3 天的点击趋势。公司必须在存档数据之前保留数据 30 天。
哪种解决方案将为数据检索提供最高的性能？

No.47
一家公司正在使用 Amazon Redshift 数据库作为其单一数据源。部分数据是敏感数据。
数据科学家需要使用数据库中的部分敏感数据。ML 工程师必须授予数据科学家访问数据的权限，而无需转换源数据，也不必在数据库中存储匿名数据。
哪种解决方案能够以最少的实施工作量满足这些要求？

No.45
一家公司已经使用 Amazon SageMaker 训练和部署了一个 ML 模型。该公司需要实施一个解决方案来记录和监控 SageMaker 端点的所有 API 调用事件。当 API 调用事件的数量超过阈值时，该解决方案还必须提供通知。
哪种解决方案可以满足这些要求？

No.42
一家广告公司使用 AWS Lake Formation 来管理数据湖。数据湖包含结构化数据和非结构化数据。该公司的 ML 工程师被分配到特定的广告活动。
ML 工程师必须通过 Amazon Athena 与数据交互，并直接在 Amazon S3 存储桶中浏览数据。 ML 工程师必须只能访问特定于其分配的广告活动的资源。
哪种解决方案能够以最高效的方式满足这些要求？

No.41
ML 工程师需要使用 AWS CloudFormation 创建 Amazon SageMaker 端点将托管的 ML 模型。
ML 工程师应在 CloudFormation 模板中声明哪种资源来满足此要求？

No.39
一家公司希望提高其 ML 运营的可持续性。
哪些行动将减少与公司培训工作相关的能源使用和计算资源？（选择两个。）

No.38
ML 工程师需要使用 Amazon EMR 集群批量处理大量数据。任何数据丢失都是不可接受的。
哪种实例购买选项最经济高效地满足这些要求？

No.36
一家公司已实施了一条数据提取管道，用于从其电子商务网站提取销售交易数据。该公司使用 Amazon Data Firehose 将数据提取到 Amazon OpenSearch Service 中。Firehose 流的缓冲间隔设置为 60 秒。OpenSearch 线性模型根据数据生成实时销售预测，并将数据显示在 OpenSearch 仪表板中。
该公司需要优化数据提取管道，以支持实时仪表板的亚秒级延迟。
架构的哪些更改将满足这些要求？

No.32
一家金融公司从外部提供商处收到大量实时市场数据流。这些流每秒包含数千条 JSON 记录。
该公司需要在 AWS 上实施可扩展的解决方案来识别异常数据点。
哪种解决方案能够以最少的运营开销满足这些要求？

No.30
一家公司在新建 VPC 的公共子网中运行 Amazon SageMaker 域。网络配置正确，ML 工程师可以访问 SageMaker 域。
最近，该公司发现来自特定 IP 地址的域的可疑流量。该公司需要阻止来自特定 IP 地址的流量。
哪个网络配置更新将满足此要求？

No.27
一家公司使用混合云环境。部署在本地的模型使用 Amazon 53 中的数据为客户提供实时对话引擎。
该模型正在使用敏感数据。ML 工程师需要实施解决方案来识别和删除敏感数据。
哪种解决方案可以以最少的运营开销满足这些要求？

No.26
一家公司希望通过考虑每个广告的配色方案来预测广告活动的成功。一位 ML 工程师正在为神经网络模型准备数据。数据集包含颜色信息作为分类数据。
ML 工程师应该为模型使用哪种特征工程技术？

No.24
一家公司有一个检索增强生成 (RAG) 应用程序，该应用程序使用矢量数据库来存储文档的嵌入。该公司必须将应用程序迁移到 AWS，并且必须实施提供文本文件语义搜索的解决方案。该公司已将文本存储库迁移到 Amazon S3 存储桶。
哪种解决方案可以满足这些要求？

No.23
ML 工程师正在训练一个简单的神经网络模型。ML 工程师在验证数据集上跟踪模型随时间的性能。模型的性能最初会大幅提高，然后在特定数量的时期后下降。
哪些解决方案可以缓解此问题？（选择两个。）

No.22
一位 ML 工程师在 us-east-1 区域的账户 A 中有一个 Amazon Comprehend 自定义模型。ML 工程师需要将模型复制到同一区域的账户 B。
哪种解决方案可以以最少的开发工作量满足此要求？

No.20
一家公司拥有一个大型的非结构化数据集。该数据集包含多个关键属性的许多重复记录。
AWS 上的哪种解决方案将以最少的代码开发检测数据集中的重复项？

No.19
ML 工程师需要处理数千个现有 CSV 对象和上传的新 CSV 对象。CSV 对象存储在中央 Amazon S3 存储桶中，并具有相同数量的列。其中一列是交易日期。ML 工程师必须根据交易日期查询数据。
哪种解决方案可以以最少的运营开销满足这些要求？

No.18
一位 ML 工程师使用随机梯度下降 (SGD) 训练了一个神经网络。神经网络在测试集上表现不佳。训练损失和验证损失的值仍然很高，并显示出振荡模式。这些值在几个时期内下降，然后在几个时期内增加，然后重复相同的循环。
ML 工程师应该做些什么来改进训练过程？

No.15
一家公司在生产中部署了一个 XGBoost 预测模型，以预测客户是否有可能取消订阅。该公司使用 Amazon SageMaker Model Monitor 来检测 F1 分数的偏差。
在对模型质量进行基线分析时，该公司记录了 F1 分数的阈值。几个月没有变化后，模型的 F1 分数显着下降。
F1 分数降低的原因可能是什么？

No.14
一位 ML 工程师正在 AWS 上开发欺诈检测模型。训练数据集包括来自本地 MySQL 数据库的交易日志、客户资料和表。交易日志和客户资料存储在 Amazon S3 中。
数据集存在类不平衡，影响模型算法的学习。此外，许多特征具有相互依赖性。该算法并未捕获数据中所有所需的底层模式。
ML 工程师需要使用 Amazon SageMaker 内置算法来训练模型。
ML 工程师应使用哪种算法来满足此要求？

No.13
一位 ML 工程师正在 AWS 上开发欺诈检测模型。训练数据集包括来自本地 MySQL 数据库的交易日志、客户资料和表。交易日志和客户资料存储在 Amazon S3 中。
数据集存在类别不平衡问题，这会影响模型算法的学习。此外，许多功能具有相互依赖性。算法没有捕获数据中所有所需的底层模式。
在 ML 工程师训练模型之前，ML 工程师必须解决数据不平衡的问题。
哪种解决方案可以以最少的运营工作量满足此要求？

No.10
ML 工程师正在 AWS 上开发欺诈检测模型。训练数据集包括来自本地 MySQL 数据库的交易日志、客户资料和表。交易日志和客户资料存储在 Amazon S3 中。
数据集具有类不平衡，这会影响模型算法的学习。此外，许多功能具有相互依赖性。该算法没有捕获数据中所有所需的底层模式。
哪个 AWS 服务或功能可以聚合来自各种数据源的数据？

No.9
一位 ML 工程师正在开发一个 ML 模型来预测类似大小的房屋的价格。该模型将根据几个特征进行预测。ML 工程师将使用以下特征工程技术来估算房屋的价格：
• 特征分割
• 对数变换
• 独热编码
• 标准化分布
为以下特征列表选择正确的特征工程技术。每种特征工程技术都应选择一次或根本不选择（选择三种）。

No.8
一位 ML 工程师正在使用大型语言模型 (LLM) 在 Amazon Bedrock 上构建生成式 AI 应用程序。
从以下列表中为每个描述选择正确的生成式 AI 术语。每个术语应选择一次或根本不选择。 （选择三项。）
• 嵌入
• 检索增强生成 (RAG)
• 温度
• 标记

No.4
一家公司正在使用 Amazon SageMaker 构建基于 Web 的 AI 应用程序。该应用程序将提供以下功能和特性：ML 实验、训练、中央模型注册表、模型部署和模型监控。
该应用程序必须确保在 ML 生命周期内安全且独立地使用训练数据。训练数据存储在 Amazon S3 中。
该公司需要运行按需工作流来监控从应用程序部署到实时终端的模型的偏差漂移。
哪种操作可以满足此要求？

No.3
一家公司正在使用 Amazon SageMaker 构建基于 Web 的 AI 应用程序。该应用程序将提供以下功能和特性：ML 实验、训练、中央模型注册表、模型部署和模型监控。
该应用程序必须确保在 ML 生命周期内安全且独立地使用训练数据。训练数据存储在 Amazon S3 中。
公司必须实施基于手动审批的工作流程，以确保只有批准的模型才能部署到生产端点。
哪种解决方案可以满足此要求？

No.1
一家公司正在使用 Amazon SageMaker 构建基于 Web 的 AI 应用程序。该应用程序将提供以下功能和特性：ML 实验、训练、中央模型注册表、模型部署和模型监控。
该应用程序必须确保在 ML 生命周期内安全且独立地使用训练数据。训练数据存储在 Amazon S3 中。
该公司需要使用中央模型注册表来管理应用程序中不同版本的模型。
哪种操作可以以最少的运营开销满足此要求？

No.95
一家公司部署了一个使用 XGBoost 算法预测产品故障的 ML 模型。该模型托管在 Amazon SageMaker 终端节点上，并根据正常运行数据进行训练。AWS Lambda 函数为公司的应用程序提供预测。
ML 工程师必须实施一种解决方案，使用传入的实时数据来检测模型准确性随时间下降的情况。
哪种解决方案可以满足这些要求？

No.91
一家公司运行使用加速实例的 Amazon SageMaker ML 模型。这些模型需要实时响应。每个模型都有不同的扩展要求。公司不得允许模型冷启动。
哪种解决方案可以满足这些要求？

No.88
制造公司使用 ML 模型来确定产品是否符合质量标准。该模型会输出“通过”或“失败”。机器人使用该模型分析装配线上的照片，将产品分为两类。
公司应使用哪些指标来评估模型的性能？（选择两个。）

No.83
一家公司希望降低其容器化 ML 应用程序的成本。这些应用程序使用在 Amazon EC2 实例、AWS Lambda 函数和 Amazon Elastic Container Service (Amazon ECS) 集群上运行的 ML 模型。EC2 工作负载和 ECS 工作负载使用 Amazon Elastic Block Store (Amazon EBS) 卷来保存预测和工件。
ML 工程师必须识别使用效率低下的资源。ML 工程师还必须生成建议以降低这些资源的成本。
哪种解决方案能够以最少的开发工作量满足这些要求？

No.81
一家公司在生产中有一个二元分类模型。ML 工程师需要开发该模型的新版本。
新模型版本必须最大化正标签和负标签的正确预测。ML 工程师必须使用指标重新校准模型以满足这些要求。
ML 工程师应使用哪个指标进行模型重新校准？

No.77
ML 工程师使用 AWS Glue DataBrew 中的最小-最大规范化对训练数据进行了规范化。在将生产推理数据传递给模型进行预测之前，ML 工程师必须以与训练数据相同的方式对生产推理数据进行规范化。
哪种解决方案可以满足此要求？

No.74
一家公司正在使用 Amazon SageMaker 和数百万个文件来训练 ML 模型。每个文件大小为几兆字节。这些文件存储在 Amazon S3 存储桶中。该公司需要提高训练性能。
哪种解决方案可以在最短的时间内满足这些要求？

No.72
一位 ML 工程师正在使用 Amazon SageMaker 训练需要分布式训练的深度学习模型。经过几次训练尝试后，ML 工程师发现实例的表现不如预期。ML 工程师确定了训练实例之间的通信开销。
ML 工程师应该怎么做才能最大限度地减少实例之间的通信开销？

No.71
一家公司的 ML 工程师已将用于情绪分析的 ML 模型部署到 Amazon SageMaker 终端节点。ML 工程师需要向公司利益相关者说明该模型如何进行预测。
哪种解决方案将为模型的预测提供说明？

No.66
一家公司已使用 Amazon SageMaker 在生产中部署预测 ML 模型。该公司正在模型上使用 SageMaker Model Monitor。模型更新后，ML 工程师在 Model Monitor 检查中注意到数据质量问题。
ML 工程师应该做什么来缓解 Model Monitor 已识别的数据质量问题？

No.61
一家公司拥有历史数据，显示客户是否需要公司员工的长期支持。该公司需要开发一个 ML 模型来预测新客户是否需要长期支持。
公司应该使用哪种建模方法来满足此要求？

No.59
一家公司有一个应用程序，它使用不同的 API 为输入文本生成嵌入。该公司需要实施一个解决方案，每 3 个月自动轮换一次 API 令牌。
哪种解决方案可以满足此要求？

No.58
一位 ML 工程师在 Amazon SageMaker 上训练了一个 ML 模型，以从闭路电视录像中检测汽车事故。ML 工程师使用 SageMaker Data Wrangler 创建了事故和非事故图像的训练数据集。
该模型在训练和验证期间表现良好。然而，由于来自不同摄像机的图像质量存在差异，该模型在生产中表现不佳。
哪种解决方案可以在最短的时间内提高模型的准确性？

No.53
一家公司使用 Amazon SageMaker Studio 开发 ML 模型。该公司有一个 SageMaker Studio 域。ML 工程师需要实施一种解决方案，当 SageMaker 计算成本达到特定阈值时，该解决方案会提供自动警报。
哪种解决方案可以满足这些要求？

No.51
一家公司部署了一个 ML 模型，该模型可在银行应用程序中实时检测欺诈性信用卡交易。该模型使用 Amazon SageMaker 异步推理。消费者报告称，在接收推理结果时出现延迟。
ML 工程师需要实施解决方案来提高推理性能。当模型质量出现偏差时，解决方案还必须提供通知。
哪种解决方案可以满足这些要求？

No.101
A company needs an AWS solution that will automatically create versions of ML models as the models are created.
Which solution will meet this requirement?

No.102
A company needs to use Retrieval Augmented Generation (RAG) to supplement an open source large language model (LLM) that runs on Amazon Bedrock. The company's data for RAG is a set of documents in an Amazon S3 bucket. The documents consist of .csv files and .docx files.
Which solution will meet these requirements with the LEAST operational overhead?

No.103
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?

No.104
An ML engineer notices class imbalance in an image classification training job.
What should the ML engineer do to resolve this issue?

No.105
A company receives daily .csv files about customer interactions with its ML model. The company stores the files in Amazon S3 and uses the files to retrain the model. An ML engineer needs to implement a solution to mask credit card numbers in the files before the model is retrained.
Which solution will meet this requirement with the LEAST development effort?

No.106
A medical company is using AWS to build a tool to recommend treatments for patients. The company has obtained health records and self-reported textual information in English from patients. The company needs to use this information to gain insight about the patients.
Which solution will meet this requirement with the LEAST development effort?

No.107
A company needs to extract entities from a PDF document to build a classifier model.
Which solution will extract and store the entities in the LEAST amount of time?

No.108
A company shares Amazon SageMaker Studio notebooks that are accessible through a VPN. The company must enforce access controls to prevent malicious actors from exploiting presigned URLs to access the notebooks.
Which solution will meet these requirements?

No.109
An ML engineer needs to merge and transform data from two sources to retrain an existing ML model. One data source consists of .csv files that are stored in an Amazon S3 bucket. Each .csv file consists of millions of records. The other data source is an Amazon Aurora DB cluster.
The result of the merge process must be written to a second S3 bucket. The ML engineer needs to perform this merge-and-transform task every week.
Which solution will meet these requirements with the LEAST operational overhead?

No.110
An ML engineer has deployed an Amazon SageMaker model to a serverless endpoint in production. The model is invoked by the InvokeEndpoint API operation.
The model's latency in production is higher than the baseline latency in the test environment. The ML engineer thinks that the increase in latency is because of model startup time.
What should the ML engineer do to confirm or deny this hypothesis?

No.111
An ML engineer needs to ensure that a dataset complies with regulations for personally identifiable information (PII). The ML engineer will use the data to train an ML model on Amazon SageMaker instances. SageMaker must not use any of the PII.
Which solution will meet these requirements in the MOST operationally efficient way?

No.112
A company must install a custom script on any newly created Amazon SageMaker notebook instances.
Which solution will meet this requirement with the LEAST operational overhead?

★No.113
A company is building a real-time data processing pipeline for an ecommerce application. The application generates a high volume of clickstream data that must be ingested, processed, and visualized in near real time. The company needs a solution that supports SQL for data processing and Jupyter notebooks for interactive analysis.
Which solution will meet these requirements?

No.114
A medical company needs to store clinical data. The data includes personally identifiable information (PII) and protected health information (PHI).
An ML engineer needs to implement a solution to ensure that the PII and PHI are not used to train ML models.
Which solution will meet these requirements?

No.1
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company needs to use the central model registry to manage different versions of models in the application.
Which action will meet this requirement with the LEAST operational overhead?

No.2
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company is experimenting with consecutive training jobs.
How can the company MINIMIZE infrastructure startup times for these jobs?

No.3
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company must implement a manual approval-based workflow to ensure that only approved models can be deployed to production endpoints.
Which solution will meet this requirement?

No.4
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company needs to run an on-demand workflow to monitor bias drift for models that are deployed to real-time endpoints from the application.
Which action will meet this requirement?

No.5
A company stores historical data in .csv files in Amazon S3. Only some of the rows and columns in the .csv files are populated. The columns are not labeled. An ML engineer needs to prepare and store the data so that the company can use the data to train ML models.
Select and order the correct steps from the following list to perform this task. Each step should be selected one time or not at all. (Select and order three.)
• Create an Amazon SageMaker batch transform job for data cleaning and feature engineering.
• Store the resulting data back in Amazon S3.
• Use Amazon Athena to infer the schemas and available columns.
• Use AWS Glue crawlers to infer the schemas and available columns.
• Use AWS Glue DataBrew for data cleaning and feature engineering.

No.6
An ML engineer needs to use Amazon SageMaker Feature Store to create and manage features to train a model.
Select and order the steps from the following list to create and use the features in Feature Store. Each step should be selected one time. (Select and order three.)
• Access the store to build datasets for training.
• Create a feature group.
• Ingest the records.

No.7
A company wants to host an ML model on Amazon SageMaker. An ML engineer is configuring a continuous integration and continuous delivery (Cl/CD) pipeline in AWS CodePipeline to deploy the model. The pipeline must run automatically when new training data for the model is uploaded to an Amazon S3 bucket.
Select and order the pipeline's correct steps from the following list. Each step should be selected one time or not at all. (Select and order three.)
• An S3 event notification invokes the pipeline when new data is uploaded.
• S3 Lifecycle rule invokes the pipeline when new data is uploaded.
• SageMaker retrains the model by using the data in the S3 bucket.
• The pipeline deploys the model to a SageMaker endpoint.
• The pipeline deploys the model to SageMaker Model Registry.

No.8
An ML engineer is building a generative AI application on Amazon Bedrock by using large language models (LLMs).
Select the correct generative AI term from the following list for each description. Each term should be selected one time or not at all. (Select three.)
• Embedding
• Retrieval Augmented Generation (RAG)
• Temperature
• Token

No.9
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.)

No.10
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.
Which AWS service or feature can aggregate the data from the various data sources?

No.11
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.
After the data is aggregated, the ML engineer must implement a solution to automatically detect anomalies in the data and to visualize the result.
Which solution will meet these requirements?

No.12
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 training dataset includes categorical data and numerical data. The ML engineer must prepare the training dataset to maximize the accuracy of the model.
Which action will meet this requirement with the LEAST operational overhead?

No.13
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?

No.14
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?

No.15
A company has deployed an XGBoost prediction model in production to predict if a customer is likely to cancel a subscription. The company uses Amazon SageMaker Model Monitor to detect deviations in the F1 score.
During a baseline analysis of model quality, the company recorded a threshold for the F1 score. After several months of no change, the model's F1 score decreases significantly.
What could be the reason for the reduced F1 score?

No.16
A company has a team of data scientists who use Amazon SageMaker notebook instances to test ML models. When the data scientists need new permissions, the company attaches the permissions to each individual role that was created during the creation of the SageMaker notebook instance.
The company needs to centralize management of the team's permissions.
Which solution will meet this requirement?

No.17
An ML engineer needs to use an ML model to predict the price of apartments in a specific location.
Which metric should the ML engineer use to evaluate the model's performance?

No.18
An ML engineer has trained a neural network by using stochastic gradient descent (SGD). The neural network performs poorly on the test set. The values for training loss and validation loss remain high and show an oscillating pattern. The values decrease for a few epochs and then increase for a few epochs before repeating the same cycle.
What should the ML engineer do to improve the training process?

No.19
An ML engineer needs to process thousands of existing CSV objects and new CSV objects that are uploaded. The CSV objects are stored in a central Amazon S3 bucket and have the same number of columns. One of the columns is a transaction date. The ML engineer must query the data based on the transaction date.
Which solution will meet these requirements with the LEAST operational overhead?

No.20
A company has a large, unstructured dataset. The dataset includes many duplicate records across several key attributes.
Which solution on AWS will detect duplicates in the dataset with the LEAST code development?

No.21
A company needs to run a batch data-processing job on Amazon EC2 instances. The job will run during the weekend and will take 90 minutes to finish running. The processing can handle interruptions. The company will run the job every weekend for the next 6 months.
Which EC2 instance purchasing option will meet these requirements MOST cost-effectively?

No.22
An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account В in the same Region.
Which solution will meet this requirement with the LEAST development effort?

No.23
An ML engineer is training a simple neural network model. The ML engineer tracks the performance of the model over time on a validation dataset. The model's performance improves substantially at first and then degrades after a specific number of epochs.
Which solutions will mitigate this problem? (Choose two.)

No.24
A company has a Retrieval Augmented Generation (RAG) application that uses a vector database to store embeddings of documents. The company must migrate the application to AWS and must implement a solution that provides semantic search of text files. The company has already migrated the text repository to an Amazon S3 bucket.
Which solution will meet these requirements?

★No.25
A company uses Amazon Athena to query a dataset in Amazon S3. The dataset has a target variable that the company wants to predict.
The company needs to use the dataset in a solution to determine if a model can predict the target variable.
Which solution will provide this information with the LEAST development effort?

No.26
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?

No.27
A company uses a hybrid cloud environment. A model that is deployed on premises uses data in Amazon 53 to provide customers with a live conversational engine.
The model is using sensitive data. An ML engineer needs to implement a solution to identify and remove the sensitive data.
Which solution will meet these requirements with the LEAST operational overhead?

No.28
An ML engineer needs to create data ingestion pipelines and ML model deployment pipelines on AWS. All the raw data is stored in Amazon S3 buckets.
Which solution will meet these requirements?

No.29
A company that has hundreds of data scientists is using Amazon SageMaker to create ML models. The models are in model groups in the SageMaker Model Registry.
The data scientists are grouped into three categories: computer vision, natural language processing (NLP), and speech recognition. An ML engineer needs to implement a solution to organize the existing models into these groups to improve model discoverability at scale. The solution must not affect the integrity of the model artifacts and their existing groupings.
Which solution will meet these requirements?

No.30
A company runs an Amazon SageMaker domain in a public subnet of a newly created VPC. The network is configured properly, and ML engineers can access the SageMaker domain.
Recently, the company discovered suspicious traffic to the domain from a specific IP address. The company needs to block traffic from the specific IP address.
Which update to the network configuration will meet this requirement?

No.31
A company is gathering audio, video, and text data in various languages. The company needs to use a large language model (LLM) to summarize the gathered data that is in Spanish.
Which solution will meet these requirements in the LEAST amount of time?

No.32
A financial company receives a high volume of real-time market data streams from an external provider. The streams consist of thousands of JSON records every second.
The company needs to implement a scalable solution on AWS to identify anomalous data points.
Which solution will meet these requirements with the LEAST operational overhead?

No.33
A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.
Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

No.34
A company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar questions multiple times, they sometimes receive different answers. An ML engineer needs to improve the responses to be more consistent and less random.
Which solution will meet these requirements?

No.35
A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_dassifier for the predictorjype hyperparameter.
What should the company do to MINIMIZE false positives?

No.36
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?

No.37
A company has trained an ML model in Amazon SageMaker. The company needs to host the model to provide inferences in a production environment.
The model must be highly available and must respond with minimum latency. The size of each request will be between 1 KB and 3 MB. The model will receive unpredictable bursts of requests during the day. The inferences must adapt proportionally to the changes in demand.
How should the company deploy the model into production to meet these requirements?

No.38
An ML engineer needs to use an Amazon EMR cluster to process large volumes of data in batches. Any data loss is unacceptable.
Which instance purchasing option will meet these requirements MOST cost-effectively?

No.39
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.)

No.40
A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 ТВ in size and consists of CSV, JSON, Apache Parquet, and simple text files.
The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.
Which solution will meet these requirements?

No.41
An ML engineer needs to use AWS CloudFormation to create an ML model that an Amazon SageMaker endpoint will host.
Which resource should the ML engineer declare in the CloudFormation template to meet this requirement?

No.42
An advertising company uses AWS Lake Formation to manage a data lake. The data lake contains structured data and unstructured data. The company's ML engineers are assigned to specific advertisement campaigns.
The ML engineers must interact with the data through Amazon Athena and by browsing the data directly in an Amazon S3 bucket. The ML engineers must have access to only the resources that are specific to their assigned advertisement campaigns.
Which solution will meet these requirements in the MOST operationally efficient way?

No.43
An ML engineer needs to use data with Amazon SageMaker Canvas to train an ML model. The data is stored in Amazon S3 and is complex in structure. The ML engineer must use a file format that minimizes processing time for the data.
Which file format will meet these requirements?

No.44
An ML engineer is evaluating several ML models and must choose one model to use in production. The cost of false negative predictions by the models is much higher than the cost of false positive predictions.
Which metric finding should the ML engineer prioritize the MOST when choosing the model?

No.45
A company has trained and deployed an ML model by using Amazon SageMaker. The company needs to implement a solution to record and monitor all the API call events for the SageMaker endpoint. The solution also must provide a notification when the number of API call events breaches a threshold.
Which solution will meet these requirements?

No.46
A company has AWS Glue data processing jobs that are orchestrated by an AWS Glue workflow. The AWS Glue jobs can run on a schedule or can be launched manually.
The company is developing pipelines in Amazon SageMaker Pipelines for ML model development. The pipelines will use the output of the AWS Glue jobs during the data processing phase of model development. An ML engineer needs to implement a solution that integrates the AWS Glue jobs with the pipelines.
Which solution will meet these requirements with the LEAST operational overhead?

No.47
A company is using an Amazon Redshift database as its single data source. Some of the data is sensitive.
A data scientist needs to use some of the sensitive data from the database. An ML engineer must give the data scientist access to the data without transforming the source data and without storing anonymized data in the database.
Which solution will meet these requirements with the LEAST implementation effort?

No.48
An ML engineer is using a training job to fine-tune a deep learning model in Amazon SageMaker Studio. The ML engineer previously used the same pre-trained model with a similar dataset. The ML engineer expects vanishing gradient, underutilized GPU, and overfitting problems.
The ML engineer needs to implement a solution to detect these issues and to react in predefined ways when the issues occur. The solution also must provide comprehensive real-time metrics during the training.
Which solution will meet these requirements with the LEAST operational overhead?

No.49
A credit card company has a fraud detection model in production on an Amazon SageMaker endpoint. The company develops a new version of the model. The company needs to assess the new model's performance by using live data and without affecting production end users.
Which solution will meet these requirements?

No.50
A company stores time-series data about user clicks in an Amazon S3 bucket. The raw data consists of millions of rows of user activity every day. ML engineers access the data to develop their ML models.
The ML engineers need to generate daily reports and analyze click trends over the past 3 days by using Amazon Athena. The company must retain the data for 30 days before archiving the data.
Which solution will provide the HIGHEST performance for data retrieval?

No.51
A company has deployed an ML model that detects fraudulent credit card transactions in real time in a banking application. The model uses Amazon SageMaker Asynchronous Inference. Consumers are reporting delays in receiving the inference results.
An ML engineer needs to implement a solution to improve the inference performance. The solution also must provide a notification when a deviation in model quality occurs.
Which solution will meet these requirements?

No.52
An ML engineer needs to implement a solution to host a trained ML model. The rate of requests to the model will be inconsistent throughout the day.
The ML engineer needs a scalable solution that minimizes costs when the model is not in use. The solution also must maintain the model's capacity to respond to requests during times of peak usage.
Which solution will meet these requirements?

No.53
A company uses Amazon SageMaker Studio to develop an ML model. The company has a single SageMaker Studio domain. An ML engineer needs to implement a solution that provides an automated alert when SageMaker compute costs reach a specific threshold.
Which solution will meet these requirements?

No.54
A company uses Amazon SageMaker for its ML workloads. The company's ML engineer receives a 50 MB Apache Parquet data file to build a fraud detection model. The file includes several correlated columns that are not required.
What should the ML engineer do to drop the unnecessary columns in the file with the LEAST effort?

No.55
A company is creating an application that will recommend products for customers to purchase. The application will make API calls to Amazon Q Business. The company must ensure that responses from Amazon Q Business do not include the name of the company's main competitor.
Which solution will meet this requirement?

No.56
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?

No.57
A company has an ML model that needs to run one time each night to predict stock values. The model input is 3 MB of data that is collected during the current day. The model produces the predictions for the next day. The prediction process takes less than 1 minute to finish running.
How should the company deploy the model on Amazon SageMaker to meet these requirements?

No.58
An ML engineer trained an ML model on Amazon SageMaker to detect automobile accidents from dosed-circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a training dataset of images of accidents and non-accidents.
The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras.
Which solution will improve the model's accuracy in the LEAST amount of time?

No.59
A company has an application that uses different APIs to generate embeddings for input text. The company needs to implement a solution to automatically rotate the API tokens every 3 months.
Which solution will meet this requirement?

No.60
An ML engineer receives datasets that contain missing values, duplicates, and extreme outliers. The ML engineer must consolidate these datasets into a single data frame and must prepare the data for ML.
Which solution will meet these requirements?

No.61
A company has historical data that shows whether customers needed long-term support from company staff. The company needs to develop an ML model to predict whether new customers will require long-term support.
Which modeling approach should the company use to meet this requirement?

No.62
An ML engineer has developed a binary classification model outside of Amazon SageMaker. The ML engineer needs to make the model accessible to a SageMaker Canvas user for additional tuning.
The model artifacts are stored in an Amazon S3 bucket. The ML engineer and the Canvas user are part of the same SageMaker domain.
Which combination of requirements must be met so that the ML engineer can share the model with the Canvas user? (Choose two.)

No.63
A company is building a deep learning model on Amazon SageMaker. The company uses a large amount of data as the training dataset. The company needs to optimize the model's hyperparameters to minimize the loss function on the validation dataset.
Which hyperparameter tuning strategy will accomplish this goal with the LEAST computation time?

No.64
A company is planning to use Amazon Redshift ML in its primary AWS account. The source data is in an Amazon S3 bucket in a secondary account.
An ML engineer needs to set up an ML pipeline in the primary account to access the S3 bucket in the secondary account. The solution must not require public IPv4 addresses.
Which solution will meet these requirements?

No.65
A company is using an AWS Lambda function to monitor the metrics from an ML model. An ML engineer needs to implement a solution to send an email message when the metrics breach a threshold.
Which solution will meet this requirement?

No.66
A company has used Amazon SageMaker to deploy a predictive ML model in production. The company is using SageMaker Model Monitor on the model. After a model update, an ML engineer notices data quality issues in the Model Monitor checks.
What should the ML engineer do to mitigate the data quality issues that Model Monitor has identified?

No.67
A company has an ML model that generates text descriptions based on images that customers upload to the company's website. The images can be up to 50 MB in total size.
An ML engineer decides to store the images in an Amazon S3 bucket. The ML engineer must implement a processing solution that can scale to accommodate changes in demand.
Which solution will meet these requirements with the LEAST operational overhead?

No.68
An ML engineer needs to use AWS services to identify and extract meaningful unique keywords from documents.
Which solution will meet these requirements with the LEAST operational overhead?

No.69
A company needs to give its ML engineers appropriate access to training data. The ML engineers must access training data from only their own business group. The ML engineers must not be allowed to access training data from other business groups.
The company uses a single AWS account and stores all the training data in Amazon S3 buckets. All ML model training occurs in Amazon SageMaker.
Which solution will provide the ML engineers with the appropriate access?

No.70
A company needs to host a custom ML model to perform forecast analysis. The forecast analysis will occur with predictable and sustained load during the same 2-hour period every day.
Multiple invocations during the analysis period will require quick responses. The company needs AWS to manage the underlying infrastructure and any auto scaling activities.
Which solution will meet these requirements?

No.71
A company's ML engineer has deployed an ML model for sentiment analysis to an Amazon SageMaker endpoint. The ML engineer needs to explain to company stakeholders how the model makes predictions.
Which solution will provide an explanation for the model's predictions?

No.72
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?

No.73
A company is running ML models on premises by using custom Python scripts and proprietary datasets. The company is using PyTorch. The model building requires unique domain knowledge. The company needs to move the models to AWS.
Which solution will meet these requirements with the LEAST effort?

No.74
A company is using Amazon SageMaker and millions of files to train an ML model. Each file is several megabytes in size. The files are stored in an Amazon S3 bucket. The company needs to improve training performance.
Which solution will meet these requirements in the LEAST amount of time?

No.75
A company wants to develop an ML model by using tabular data from its customers. The data contains meaningful ordered features with sensitive information that should not be discarded. An ML engineer must ensure that the sensitive data is masked before another team starts to build the model.
Which solution will meet these requirements?

No.76
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?

No.77
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?

No.78
A company is planning to use Amazon SageMaker to make classification ratings that are based on images. The company has 6 ТВ of training data that is stored on an Amazon FSx for NetApp ONTAP system virtual machine (SVM). The SVM is in the same VPC as SageMaker.
An ML engineer must make the training data accessible for ML models that are in the SageMaker environment.
Which solution will meet these requirements?

No.79
A company regularly receives new training data from the vendor of an ML model. The vendor delivers cleaned and prepared data to the company's Amazon S3 bucket every 3-4 days.
The company has an Amazon SageMaker pipeline to retrain the model. An ML engineer needs to implement a solution to run the pipeline when new data is uploaded to the S3 bucket.
Which solution will meet these requirements with the LEAST operational effort?

No.80
An ML engineer is developing a fraud detection model by using the Amazon SageMaker XGBoost algorithm. The model classifies transactions as either fraudulent or legitimate.
During testing, the model excels at identifying fraud in the training dataset. However, the model is inefficient at identifying fraud in new and unseen transactions.
What should the ML engineer do to improve the fraud detection for new transactions?

No.81
A company has a binary classification model in production. An ML engineer needs to develop a new version of the model.
The new model version must maximize correct predictions of positive labels and negative labels. The ML engineer must use a metric to recalibrate the model to meet these requirements.
Which metric should the ML engineer use for the model recalibration?

★No.82
A company is using Amazon SageMaker to create ML models. The company's data scientists need fine-grained control of the ML workflows that they orchestrate. The data scientists also need the ability to visualize SageMaker jobs and workflows as a directed acyclic graph (DAG). The data scientists must keep a running history of model discovery experiments and must establish model governance for auditing and compliance verifications.
Which solution will meet these requirements?

No.83
A company wants to reduce the cost of its containerized ML applications. The applications use ML models that run on Amazon EC2 instances, AWS Lambda functions, and an Amazon Elastic Container Service (Amazon ECS) cluster. The EC2 workloads and ECS workloads use Amazon Elastic Block Store (Amazon EBS) volumes to save predictions and artifacts.
An ML engineer must identify resources that are being used inefficiently. The ML engineer also must generate recommendations to reduce the cost of these resources.
Which solution will meet these requirements with the LEAST development effort?

No.84
A company needs to create a central catalog for all the company's ML models. The models are in AWS accounts where the company developed the models initially. The models are hosted in Amazon Elastic Container Registry (Amazon ECR) repositories.
Which solution will meet these requirements?

No.85
A company has developed a new ML model. The company requires online model validation on 10% of the traffic before the company fully releases the model in production. The company uses an Amazon SageMaker endpoint behind an Application Load Balancer (ALB) to serve the model.
Which solution will set up the required online validation with the LEAST operational overhead?

No.86
A company needs to develop an ML model. The model must identify an item in an image and must provide the location of the item.
Which Amazon SageMaker algorithm will meet these requirements?

No.87
A company has an Amazon S3 bucket that contains 1 ТВ of files from different sources. The S3 bucket contains the following file types in the same S3 folder: CSV, JSON, XLSX, and Apache Parquet.
An ML engineer must implement a solution that uses AWS Glue DataBrew to process the data. The ML engineer also must store the final output in Amazon S3 so that AWS Glue can consume the output in the future.
Which solution will meet these requirements?

No.88
A manufacturing company uses an ML model to determine whether products meet a standard for quality. The model produces an output of "Passed" or "Failed." Robots separate the products into the two categories by using the model to analyze photos on the assembly line.
Which metrics should the company use to evaluate the model's performance? (Choose two.)

No.89
An ML engineer needs to encrypt all data in transit when an ML training job runs. The ML engineer must ensure that encryption in transit is applied to processes that Amazon SageMaker uses during the training job.
Which solution will meet these requirements?

No.90
An ML engineer needs to use metrics to assess the quality of a time-series forecasting model.
Which metrics apply to this model? (Choose two.)

No.91
A company runs Amazon SageMaker ML models that use accelerated instances. The models require real-time responses. Each model has different scaling requirements. The company must not allow a cold start for the models.
Which solution will meet these requirements?

No.92
A company uses Amazon SageMaker for its ML process. A compliance audit discovers that an Amazon S3 bucket for training data uses server-side encryption with S3 managed keys (SSE-S3).
The company requires customer managed keys. An ML engineer changes the S3 bucket to use server-side encryption with AWS KMS keys (SSE-KMS). The ML engineer makes no other configuration changes.
After the change to the encryption settings, SageMaker training jobs start to fail with AccessDenied errors.
What should the ML engineer do to resolve this problem?

No.93
A company runs training jobs on Amazon SageMaker by using a compute optimized instance. Demand for training runs will remain constant for the next 55 weeks. The instance needs to run for 35 hours each week. The company needs to reduce its model training costs.
Which solution will meet these requirements?

★No.94
HOTSPOT
-

A company needs to train an ML model that will use historical transaction data to predict customer behavior.
Select the correct AWS service from the following list to perform each task on the data. Each service should be selected one time or not at all. (Select three.)

• Amazon Athena
• AWS Glue
• Amazon Kinesis Data Streams
• Amazon S3

Query the data for exploration and analysis.Select ...
Select ...
Amazon Athena
AWS Glue
Amazon Kinesis Data Streams
Amazon S3

Store the data.Select ...
Select ...
Amazon Athena
AWS Glue
Amazon Kinesis Data Streams
Amazon S3

Transform the data.Select ...
Select ...
Amazon Athena
AWS Glue
Amazon Kinesis Data Streams
Amazon S3

No.95
A company deployed an ML model that uses the XGBoost algorithm to predict product failures. The model is hosted on an Amazon SageMaker endpoint and is trained on normal operating data. An AWS Lambda function provides the predictions to the company's application.
An ML engineer must implement a solution that uses incoming live data to detect decreased model accuracy over time.
Which solution will meet these requirements?

No.96
A company has an ML model that uses historical transaction data to predict customer behavior. An ML engineer is optimizing the model in Amazon SageMaker to enhance the model's predictive accuracy. The ML engineer must examine the input data and the resulting predictions to identify trends that could skew the model's performance across different demographics.
Which solution will provide this level of analysis?

No.97
A company uses 10 Reserved Instances of accelerated instance types to serve the current version of an ML model. An ML engineer needs to deploy a new version of the model to an Amazon SageMaker real-time inference endpoint.
The solution must use the original 10 instances to serve both versions of the model. The solution also must include one additional Reserved Instance that is available to use in the deployment process. The transition between versions must occur with no downtime or service interruptions.
Which solution will meet these requirements?

No.98
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?

No.99
A company is working on an ML project that will include Amazon SageMaker notebook instances. An ML engineer must ensure that the SageMaker notebook instances do not allow root access.
Which solution will prevent the deployment of notebook instances that allow root access?

No.100
A company is using Amazon SageMaker to develop ML models. The company stores sensitive training data in an Amazon S3 bucket. The model training must have network isolation from the internet.
Which solution will meet this requirement?

No.101
ある企業では、モデルの作成時に ML モデルのバージョンを自動的に作成する AWS ソリューションが必要です。
この要件を満たすソリューションはどれですか?

No.102
ある企業では、Amazon Bedrock で実行されるオープンソースの大規模言語モデル (LLM) を補完するために、Retrieval Augmented Generation (RAG) を使用する必要があります。RAG の企業のデータは、Amazon S3 バケット内のドキュメント セットです。ドキュメントは、.csv ファイルと .docx ファイルで構成されています。
これらの要件を、運用オーバーヘッドが最も少ないソリューションはどれですか?

No.103
ある企業は、Amazon SageMaker エンドポイントで本番推論用の ML モデルをデプロイする予定です。推論ペイロードの平均サイズは 100 MB から 300 MB までです。推論リクエストは 60 分以内に処理する必要があります。
これらの要件を満たす SageMaker 推論オプションはどれですか?

No.104
ML エンジニアが画像分類トレーニング ジョブでクラスの不均衡に気付きました。
ML エンジニアはこの問題を解決するために何をすべきですか?

No.105
ある企業は、顧客と ML モデルとのやり取りに関する .csv ファイルを毎日受け取ります。この企業は、ファイルを Amazon S3 に保存し、そのファイルを使用してモデルを再トレーニングします。ML エンジニアは、モデルを再トレーニングする前に、ファイル内のクレジットカード番号をマスクするソリューションを実装する必要があります。
どのソリューションが、開発の労力を最小限に抑えてこの要件を満たしますか?

No.106
ある医療会社は、患者に治療を推奨するツールを構築するために AWS を使用しています。同社は、患者から健康記録と英語の自己申告テキスト情報を入手しました。同社は、この情報を使用して患者に関する洞察を得る必要があります。
最も少ない開発労力でこの要件を満たすソリューションはどれですか?

No.107
ある会社は、分類モデルを構築するために、PDF ドキュメントからエンティティを抽出する必要があります。
最も短い時間でエンティティを抽出して保存するソリューションはどれですか?

No.108
ある企業が、VPN 経由でアクセスできる Amazon SageMaker Studio ノートブックを共有しています。企業は、悪意のある人物が署名済み URL を悪用してノートブックにアクセスするのを防ぐために、アクセス制御を実施する必要があります。
これらの要件を満たすソリューションはどれですか?

No.109
ML エンジニアは、既存の ML モデルを再トレーニングするために、2 つのソースからのデータをマージして変換する必要があります。1 つのデータ ソースは、Amazon S3 バケットに保存されている .csv ファイルで構成されています。各 .csv ファイルは、数百万のレコードで構成されています。もう 1 つのデータ ソースは、Amazon Aurora DB クラスターです。
マージ プロセスの結果は、2 番目の S3 バケットに書き込む必要があります。ML エンジニアは、このマージと変換のタスクを毎週実行する必要があります。
どのソリューションが、運用オーバーヘッドを最小限に抑えながらこれらの要件を満たしますか?

No.110
ML エンジニアが、Amazon SageMaker モデルを本番環境のサーバーレスエンドポイントにデプロイしました。モデルは、InvokeEndpoint API オペレーションによって呼び出されます。
本番環境のモデルのレイテンシーは、テスト環境のベースライン レイテンシーよりも高くなっています。ML エンジニアは、レイテンシーの増加はモデルの起動時間によるものだと考えています。
この仮説を確認または否定するには、ML エンジニアは何をすべきでしょうか?

No.111
ML エンジニアは、データセットが個人識別情報 (PII) に関する規制に準拠していることを確認する必要があります。ML エンジニアは、データを使用して Amazon SageMaker インスタンスで ML モデルをトレーニングします。SageMaker は PII を一切使用してはなりません。
どのソリューションが最も運用効率の高い方法でこれらの要件を満たしますか?

No.112
企業は、新しく作成された Amazon SageMaker ノートブックインスタンスにカスタムスクリプトをインストールする必要があります。
どのソリューションが、運用オーバーヘッドを最小限に抑えてこの要件を満たしますか?

★No.113
ある会社が、e コマース アプリケーション用のリアルタイム データ処理パイプラインを構築しています。このアプリケーションは、ほぼリアルタイムで取り込み、処理、視覚化する必要がある大量のクリックストリーム データを生成します。この会社には、データ処理用の SQL とインタラクティブな分析用の Jupyter ノートブックをサポートするソリューションが必要です。
これらの要件を満たすソリューションはどれですか?

No.114
医療会社は臨床データを保存する必要があります。データには、個人を特定できる情報 (PII) と保護された健康情報 (PHI) が含まれます。
ML エンジニアは、PII と PHI が ML モデルのトレーニングに使用されないようにするためのソリューションを実装する必要があります。
これらの要件を満たすソリューションはどれですか?