Amazon MLS-C01過去問題: AWS Certified Machine Learning - Specialty - It-Passports信頼できるプロバイダ

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AWS Certified Machine Learning - Specialty認定試験に備えて、候補者は、オンラインコース、模擬試験、ホワイトペーパーなどAWSが提供するさまざまなリソースを活用することができます。候補者はAWSパートナーが提供するトレーニングセッションやワークショップに参加し、AWSサポートやコンサルティングサービスを活用することもできます。

Amazon MLS -C01（AWS認定機械学習 - 専門）認定試験は、機械学習の概念、テクニック、ベストプラクティスを強く理解している個人向けに設計されています。この試験は、AWSプラットフォームに機械学習モデルの構築と展開に関する個人の技術的専門知識を検証することを目的としています。この認定は、データサイエンティスト、開発者、ソフトウェアエンジニアなど、機械学習技術を扱う人に適しています。

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MLS-C01認証試験 & MLS-C01テスト問題集

これらの有用な知識をよりよく取り入れるために、多くの顧客は、実践する価値のある種類の練習資料を持ちたいと考えています。すべてのコンテンツは明確で、MLS-C01実践資料で簡単に理解できます。リーズナブルな価格とオプションのさまざまなバージョンでアクセスできます。すべてのコンテンツは試験の規制に準拠しています。成功することが決まっている限り、MLS-C01学習ガイドがあなたの最善の信頼になります

Amazon AWS Certified Machine Learning - Specialty 認定 MLS-C01 試験問題 (Q98-Q103):

質問 # 98
A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression. During exploratory data analysis, the Specialist observes that many features are highly correlated with each other. This may make the model unstable.
What should be done to reduce the impact of having such a large number of features?

A. Use matrix multiplication on highly correlated features.
B. Create a new feature space using principal component analysis (PCA)
C. Perform one-hot encoding on highly correlated features.
D. Apply the Pearson correlation coefficient.

正解：B

質問 # 99
A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket. A Machine Learning Specialist wants to use SQL to run queries on this data.
Which solution requires the LEAST effort to be able to query this data?

A. Use AWS Glue to catalogue the data and Amazon Athena to run queries.
B. Use AWS Batch to run ETL on the data and Amazon Aurora to run the queries.
C. Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries.
D. Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.

正解：A

解説：
Using AWS Glue to catalogue the data and Amazon Athena to run queries is the solution that requires the least effort to be able to query the data stored in an Amazon S3 bucket using SQL. AWS Glue is a service that provides a serverless data integration platform for data preparation and transformation. AWS Glue can automatically discover, crawl, and catalogue the data stored in various sources, such as Amazon S3, Amazon RDS, Amazon Redshift, etc. AWS Glue can also use AWS KMS to encrypt the data at rest on the Glue Data Catalog and Glue ETL jobs. AWS Glue can handle both structured and unstructured data, and support various data formats, such as CSV, JSON, Parquet, etc. AWS Glue can also use built-in or custom classifiers to identify and parse the data schema and format1 Amazon Athena is a service that provides an interactive query engine that can run SQL queries directly on data stored in Amazon S3. Amazon Athena can integrate with AWS Glue to use the Glue Data Catalog as a central metadata repository for the data sources and tables.
Amazon Athena can also use AWS KMS to encrypt the data at rest on Amazon S3 and the query results.
Amazon Athena can query both structured and unstructured data, and support various data formats, such as CSV, JSON, Parquet, etc. Amazon Athena can also use partitions and compression to optimize the query performance and reduce the query cost23 The other options are not valid or require more effort to query the data stored in an Amazon S3 bucket using SQL. Using AWS Data Pipeline to transform the data and Amazon RDS to run queries is not a good option, as it involves moving the data from Amazon S3 to Amazon RDS, which can incur additional time and cost.
AWS Data Pipeline is a service that can orchestrate and automate data movement and transformation across various AWS services and on-premises data sources. AWS Data Pipeline can be integrated with Amazon EMR to run ETL jobs on the data stored in Amazon S3. Amazon RDS is a service that provides a managed relational database service that can run various database engines, such as MySQL, PostgreSQL, Oracle, etc.
Amazon RDS can use AWS KMS to encrypt the data at rest and in transit. Amazon RDS can run SQL queries on the data stored in the database tables45 Using AWS Batch to run ETL on the data and Amazon Aurora to run the queries is not a good option, as it also involves moving the data from Amazon S3 to Amazon Aurora, which can incur additional time and cost. AWS Batch is a service that can run batch computing workloads on AWS. AWS Batch can be integrated with AWS Lambda to trigger ETL jobs on the data stored in Amazon S3.
Amazon Aurora is a service that provides a compatible and scalable relational database engine that can run MySQL or PostgreSQL. Amazon Aurora can use AWS KMS to encrypt the data at rest and in transit.
Amazon Aurora can run SQL queries on the data stored in the database tables. Using AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries is not a good option, as it is not suitable for querying data stored in Amazon S3 using SQL. AWS Lambda is a service that can run serverless functions on AWS. AWS Lambda can be integrated with Amazon S3 to trigger data transformation functions on the data stored in Amazon S3. Amazon Kinesis Data Analytics is a service that can analyze streaming data using SQL or Apache Flink. Amazon Kinesis Data Analytics can be integrated with Amazon Kinesis Data Streams or Amazon Kinesis Data Firehose to ingest streaming data sources, such as web logs, social media, IoT devices, etc. Amazon Kinesis Data Analytics is not designed for querying data stored in Amazon S3 using SQL.

質問 # 100
A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.

Based on this information which model would have the HIGHEST accuracy?

A. Long short-term memory (LSTM) model with scaled exponential linear unit (SELL))
B. Support vector machine (SVM) with non-linear kernel
C. Single perceptron with tanh activation function
D. Logistic regression

正解：B

解説：
Based on the figure provided, the data is not linearly separable. Therefore, a non-linear model such as SVM with a non-linear kernel would be the best choice. SVMs are particularly effective in high-dimensional spaces and are versatile in that they can be used for both linear and non-linear data. Additionally, SVMs have a high level of accuracy and are less prone to overfitting1 References: 1: https://docs.aws.amazon.com/sagemaker/latest/dg/svm.html

質問 # 101
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Select THREE.)

A. The 1AM role that Amazon SageMaker can assume to perform tasks on behalf of the users.
B. The output path specifying where on an Amazon S3 bucket the trained model will persist.
C. Hyperparameters in a JSON array as documented for the algorithm used.
D. The training channel identifying the location of training data on an Amazon S3 bucket.
E. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
F. The validation channel identifying the location of validation data on an Amazon S3 bucket.

正解：A、B、D

解説：
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, the common parameters that must be specified are:
* The training channel identifying the location of training data on an Amazon S3 bucket. This parameter tells SageMaker where to find the input data for the algorithm and what format it is in. For example, TrainingInputMode: File means that the input data is in files stored in S3.
* The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users. This parameter grants SageMaker the necessary permissions to access the S3 buckets, ECR repositories, and other AWS resources needed for the training job. For example, RoleArn: arn:aws:iam::123456789012:
role/service-role/AmazonSageMaker-ExecutionRole-20200303T150948 means that SageMaker will use the specified role to run the training job.
* The output path specifying where on an Amazon S3 bucket the trained model will persist. This parameter tells SageMaker where to save the model artifacts, such as the model weights and parameters, after the training job is completed. For example, OutputDataConfig: {S3OutputPath:
s3://my-bucket/my-training-job} means that SageMaker will store the model artifacts in the specified S3 location.
The validation channel identifying the location of validation data on an Amazon S3 bucket is an optional parameter that can be used to provide a separate dataset for evaluating the model performance during the training process. This parameter is not required for all algorithms and can be omitted if the validation data is not available or not needed.
The hyperparameters in a JSON array as documented for the algorithm used is another optional parameter that can be used to customize the behavior and performance of the algorithm. This parameter is specific to each algorithm and can be used to tune the model accuracy, speed, complexity, and other aspects. For example, HyperParameters: {num_round: "10", objective: "binary:logistic"} means that the XGBoost algorithm will use 10 boosting rounds and the logistic loss function for binary classification.
The Amazon EC2 instance class specifying whether training will be run using CPU or GPU is not a parameter that is specified when submitting a training job using a built-in algorithm. Instead, this parameter is specified when creating a training instance, which is a containerized environment that runs the training code and algorithm. For example, ResourceConfig: {InstanceType: ml.m5.xlarge, InstanceCount: 1, VolumeSizeInGB:
10} means that SageMaker will use one m5.xlarge instance with 10 GB of storage for the training instance.
References:
* Train a Model with Amazon SageMaker
* Use Amazon SageMaker Built-in Algorithms or Pre-trained Models
* CreateTrainingJob - Amazon SageMaker Service

質問 # 102
A company uses sensors on devices such as motor engines and factory machines to measure parameters, temperature and pressure. The company wants to use the sensor data to predict equipment malfunctions and reduce services outages.
The Machine learning (ML) specialist needs to gather the sensors data to train a model to predict device malfunctions The ML spoctafst must ensure that the data does not contain outliers before training the ..el.
What can the ML specialist meet these requirements with the LEAST operational overhead?

A. Use an Amazon SageMaker Data Wrangler bias report to find outliers in the dataset Use a Data Wrangler data flow to remove outliers based on the bias report.
B. Use an Amazon SageMaker Data Wrangler anomaly detection visualization to find outliers in the dataset. Add a transformation to a Data Wrangler data flow to remove outliers.
C. Load the data into an Amazon SagcMaker Studio notebook. Calculate the first and third quartile Use a SageMaker Data Wrangler data (low to remove only values that are outside of those quartiles.
D. Use Amazon Lookout for Equipment to find and remove outliers from the dataset.

正解：B

解説：
Amazon SageMaker Data Wrangler is a tool that helps data scientists and ML developers to prepare data for ML. One of the features of Data Wrangler is the anomaly detection visualization, which uses an unsupervised ML algorithm to identify outliers in the dataset based on statistical properties. The ML specialist can use this feature to quickly explore the sensor data and find any anomalous values that may affect the model performance. The ML specialist can then add a transformation to a Data Wrangler data flow to remove the outliers from the dataset. The data flow can be exported as a script or a pipeline to automate the data preparation process. This option requires the least operational overhead compared to the other options.
Amazon SageMaker Data Wrangler - Amazon Web Services (AWS)
Anomaly Detection Visualization - Amazon SageMaker
Transform Data - Amazon SageMaker

質問 # 103
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MLS-C01試験トレントの3つのバージョンを提供しており、PDFバージョン、PCバージョン、APPオンラインバージョンが含まれています。各バージョンの機能と使用方法は異なり、実際の状況に適した最も便利なバージョンを選択できます。たとえば、PDFバージョンは、MLS-C01テストトレントをダウンロードして印刷するのに便利で、学習の閲覧に適しています。 PDFバージョンを使用している場合は、ペーパーで急流MLS-C01ガイドを印刷できます。 MLS-C01試験問題のPCバージョンは、AWS Certified Machine Learning - Specialty実際の試験環境を刺激します。

MLS-C01認証試験: https://www.it-passports.com/MLS-C01.html

ちなみに、It-Passports MLS-C01の一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=1Xy3EtTwoZMYUGog8LmBN3t5pfmddPLv4

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Amazon MLS-C01過去問題: AWS Certified Machine Learning - Specialty - It-Passports信頼できるプロバイダ

MLS-C01認証試験 & MLS-C01テスト問題集

Amazon AWS Certified Machine Learning - Specialty 認定 MLS-C01 試験問題 (Q98-Q103):

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