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実際的なAWS-Certified-Machine-Learning-Specialty資格トレーリング試験-試験の準備方法-完璧なAWS-Certified-Machine-Learning-Specialtyダウンロード
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Amazon AWS Certified Machine Learning - Specialty 認定 AWS-Certified-Machine-Learning-Specialty 試験問題 (Q191-Q196):
質問 # 191
A Machine Learning Specialist built an image classification deep learning model. However, the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%, respectively.
How should the Specialist address this issue and what is the reason behind it?
- A. The dropout rate at the flatten layer should be increased because the model is not generalized enough.
- B. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.
- C. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
- D. The learning rate should be increased because the optimization process was trapped at a local minimum.
正解：A
解説：
https://kharshit.github.io/blog/2018/05/04/dropout-prevent-overfitting
質問 # 192
A company is using Amazon SageMaker to build a machine learning (ML) model to predict customer churn based on customer call transcripts. Audio files from customer calls are located in an on-premises VoIP system that has petabytes of recorded calls. The on-premises infrastructure has high-velocity networking and connects to the company's AWS infrastructure through a VPN connection over a 100 Mbps connection.
The company has an algorithm for transcribing customer calls that requires GPUs for inference. The company wants to store these transcriptions in an Amazon S3 bucket in the AWS Cloud for model development.
Which solution should an ML specialist use to deliver the transcriptions to the S3 bucket as quickly as possible?
- A. Order and use an AWS Snowball Edge Compute Optimized device with an NVIDIA Tesla module to run the transcription algorithm. Use AWS DataSync to send the resulting transcriptions to the transcription S3 bucket.
- B. Use AWS DataSync to ingest the audio files to Amazon S3. Create an AWS Lambda function to run the transcription algorithm on the audio files when they are uploaded to Amazon S3. Configure the function to write the resulting transcriptions to the transcription S3 bucket.
- C. Order and use AWS Outposts to run the transcription algorithm on GPU-based Amazon EC2 instances.
Store the resulting transcriptions in the transcription S3 bucket. - D. Order and use an AWS Snowcone device with Amazon EC2 Inf1 instances to run the transcription algorithm Use AWS DataSync to send the resulting transcriptions to the transcription S3 bucket
正解：A
質問 # 193
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Select THREE.)
- A. The validation channel identifying the location of validation data on an Amazon S3 bucket.
- B. Hyperparameters in a JSON array as documented for the algorithm used.
- C. The output path specifying where on an Amazon S3 bucket the trained model will persist.
- D. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
- E. The 1AM role that Amazon SageMaker can assume to perform tasks on behalf of the users.
- F. The training channel identifying the location of training data on an Amazon S3 bucket.
正解：C、E、F
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 # 194
A machine learning (ML) specialist needs to solve a binary classification problem for a marketing dataset.
The ML specialist must maximize the Area Under the ROC Curve (AUC) of the algorithm by training an XGBoost algorithm. The ML specialist must find values for the eta, alpha, min_child_weight, and max_depth hyperparameter that will generate the most accurate model.
Which approach will meet these requirements with the LEAST operational overhead?
- A. Use a bootstrap script to install scikit-learn on an Amazon EMR cluster. Deploy the EMR cluster.
Apply k-fold cross-validation methods to the algorithm. - B. Deploy Amazon SageMaker prebuilt Docker images that have scikit-learn installed. Apply k-fold cross- validation methods to the algorithm.
- C. Use Amazon SageMaker automatic model tuning (AMT). Specify a range of values for each hyperparameter.
- D. Subscribe to an AUC algorithm that is on AWS Marketplace. Specify a range of values for each hyperparameter.
正解：C
解説：
SageMaker Automatic Model Tuning (AMT) is a fully managed hyperparameter optimization feature that finds the best values for model parameters like eta, alpha, min_child_weight, and max_depth.
"Use SageMaker automatic model tuning to find the best version of a model by running many training jobs on your dataset using the ranges of hyperparameters that you specify." It supports built-in algorithms like XGBoost, and can optimize for evaluation metrics like AUC, making it the least operational overhead solution for this task.
質問 # 195
A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.
The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:
* Real-time analytics
* Interactive analytics of historical data
* Clickstream analytics
* Product recommendations
Which services should the Specialist use?
- A. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
- B. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations
- C. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-realtime data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendations
- D. AWS Glue as the data dialog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
正解：D
解説：
The best services to use for building a data ingestion solution for the company's Amazon S3-based data lake are:
* AWS Glue as the data catalog: AWS Glue is a fully managed extract, transform, and load (ETL) service that can discover, crawl, and catalog data from various sources and formats, and make it available for analysis. AWS Glue can also generate ETL code in Python or Scala to transform, enrich, and join data using AWS Glue Data Catalog as the metadata repository. AWS Glue Data Catalog is a central metadata store that integrates with Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum, allowing users to create a unified view of their data across various sources and formats.
* Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights: Amazon Kinesis Data Streams is a service that enables users to collect, process, and analyze real-time streaming data at any scale. Users can create data streams that can capture data from various sources, such as web and mobile applications, IoT devices, and social media platforms. Amazon Kinesis Data Analytics is a service that allows users to analyze streaming data using standard SQL queries or Apache Flink applications. Users can create real-time dashboards, metrics, and alerts based on the streaming data analysis results.
* Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics: Amazon Kinesis Data Firehose is a service that enables users to load streaming data into data lakes, data stores, and analytics services. Users can configure Kinesis Data Firehose to automatically deliver data to various destinations, such as Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and third-party solutions. For clickstream analytics, users can use Kinesis Data Firehose to deliver data to Amazon OpenSearch Service, a fully managed service that offers search and analytics capabilities for log data.
Users can use Amazon OpenSearch Service to perform interactive analysis and visualization of clickstream data using Kibana, an open-source tool that is integrated with Amazon OpenSearch Service.
* Amazon EMR to generate personalized product recommendations: Amazon EMR is a service that enables users to run distributed data processing frameworks, such as Apache Spark, Apache Hadoop, and Apache Hive, on scalable clusters of EC2 instances. Users can use Amazon EMR to perform advanced analytics, such as machine learning, on large and complex datasets stored in Amazon S3 or other sources. For product recommendations, users can use Amazon EMR to run Spark MLlib, a library that provides scalable machine learning algorithms, such as collaborative filtering, to generate personalized recommendations based on user behavior and preferences.
References:
* AWS Glue - Fully Managed ETL Service
* Amazon Kinesis - Data Streaming Service
* Amazon OpenSearch Service - Managed OpenSearch Service
* Amazon EMR - Managed Hadoop Framework
質問 # 196
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