Practice Exams AWS Machine Learning Speciality Tests 2024

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课程名称:AWS机器学习专业资格考试实践测试 2024 课程概述: AWS认证机器学习-专业(MLS-C01)考试面向担任人工智能/机器学习(AI/ML)开发或数据科学角色的个人。该考试验证考生使用AWS云解决特定业务问题的机器学习(ML)解决方案的设计、构建、部署、优化、训练、调整和维护能力。考试内容包括: - 选择并解释适合特定业务问题的机器学习方法。 - 确定实现机器学习解决方案所需的AWS服务。 - 设计并实施可扩展、成本优化、可靠且安全的机器学习解决方案。 考试内容领域及其权重: - 数据工程(占得分内容的20%) - 探索性数据分析(占得分内容的24%) - 建模(占得分内容的36%) - 机器学习实施与操作(占得分内容的20%) 样题示例: 1) 针对训练速度的关切,机器学习专家可以采取哪些措施改善模型训练时间? 2) 在建立tf-idf矩阵的过程中,构成矩阵的维度是多少? 3) 公司希望自动化运行数据转换任务并维护元数据目录,最有效的解决方案是哪个? 4) 数据科学家在训练过程中优化模型,观察到相同参数的多次运行收敛到不同的值,应该如何改进训练过程? 5) 在评估不同的二元分类模型时,符合特定商业标准的混淆矩阵是哪一个? 通过一次性支付费用即可确保成功,通过考试专家提供的练习考试,您所支付的费用对于帮助您通过认证考试是值得的。每个概念都已涵盖并加以解释。请练习这些测试,自信地通过考试。

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The AWS Certified Machine Learning - Specialty (MLS-C01) exam is intended for individuals who perform an artificial intelligence/machine learning (AI/ML) development or data science role. The exam validates a candidate's ability to design, build, deploy, optimize, train, tune, and maintain ML solutions for given business problems by using the AWS Cloud. The exam also validates a candidate's ability to complete the following tasks:  Select and justify the appropriate ML approach for a given business problem.  Identify appropriate AWS services to implement ML solutions.  Design and implement scalable, cost-optimized, reliable, and secure ML solutionsThe exam has the following content domains and weightings:  Domain 1: Data Engineering (20% of scored content)  Domain 2: Exploratory Data Analysis (24% of scored content)  Domain 3: Modeling (36% of scored content)  Domain 4: Machine Learning Implementation and Operations (20% of scored content)Sample Questions:1) 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? A) Use Amazon SageMaker Pipe mode. B) Use Amazon Machine Learning to train the models. C) Use Amazon Kinesis to stream the data to Amazon SageMaker. D) Use AWS Glue to transform the CSV dataset to the JSON format. 2) A term frequency-inverse document frequency (tf-idf) matrix using both unigrams and bigrams is built from a text corpus consisting of the following two sentences: 1. Please call the number below. 2. Please do not call us. What are the dimensions of the tf-idf matrix? A) (2, 16) B) (2, 8) C) (2, 10) D) (8, 10) 3) A company is setting up a system to manage all of the datasets it stores in Amazon S3. The company would like to automate running transformation jobs on the data and maintaining a catalog of the metadata concerning the datasets. The solution should require the least amount of setup and maintenance. Which solution will allow the company to achieve its goals? A) Create an Amazon EMR cluster with Apache Hive installed. Then, create a Hive metastore and a script to run transformation jobs on a schedule. B) Create an AWS Glue crawler to populate the AWS Glue Data Catalog. Then, author an AWS Glue ETL job, and set up a schedule for data transformation jobs. C) Create an Amazon EMR cluster with Apache Spark installed. Then, create an Apache Hive metastore and a script to run transformation jobs on a schedule. D) Create an Amazon SageMaker Jupyter notebook instance that transforms the data. Then, create an Apache Hive metastore and a script to run transformation jobs on a schedule.4) A data scientist is working on optimizing a model during the training process by varying multiple parameters. The data scientist observes that, during multiple runs with identical parameters, the loss function converges to different, yet stable, values. What should the data scientist do to improve the training process? A) Increase the learning rate. Keep the batch size the same. B) Decrease the learning rate. Reduce the batch size. C) Decrease the learning rate. Keep the batch size the same. D) Do not change the learning rate. Increase the batch size. 5) A data scientist is evaluating different binary classification models. A false positive result is 5 times more expensive (from a business perspective) than a false negative result. The models should be evaluated based on the following criteria: 1) Must have a recall rate of at least 80% 2) Must have a false positive rate of 10% or less 3) Must minimize business costs After creating each binary classification model, the data scientist generates the corresponding confusion matrix. Which confusion matrix represents the model that satisfies the requirements? A) TN = 91, FP = 9 FN = 22, TP = 78 B) TN = 99, FP = 1 FN = 21, TP = 79 C) TN = 96, FP = 4 FN = 10, TP = 90 D) TN = 98, FP = 2 FN = 18, TP = 82Pay one time and ensure your success by practicing exams from exam experts. The price you pay is worth to pay for certification exams again and again.Every concept has been covered and explained. Practice these tests and pass your exam with confidence.

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