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Amazon AWS-Certified-Machine-Learning-Specialty日本語pdf問題 & AWS-Certified-Machine-Learning-Specialty受験トレーリング

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Amazon AWS Certified Machine Learning - Specialty 認定 AWS-Certified-Machine-Learning-Specialty 試験問題 (Q48-Q53):

質問 # 48
A real-estate company is launching a new product that predicts the prices of new houses. The historical data for the properties and prices is stored in .csv format in an Amazon S3 bucket. The data has a header, some categorical fields, and some missing values. The company's data scientists have used Python with a common open-source library to fill the missing values with zeros. The data scientists have dropped all of the categorical fields and have trained a model by using the open-source linear regression algorithm with the default parameters.
The accuracy of the predictions with the current model is below 50%. The company wants to improve the model performance and launch the new product as soon as possible.
Which solution will meet these requirements with the LEAST operational overhead?

A. Create an IAM role with access to Amazon S3, Amazon SageMaker, and AWS Lambda. Create a training job with the SageMaker built-in XGBoost model pointing to the bucket with the dataset.
Specify the price as the target feature. Wait for the job to complete. Load the model artifact to a Lambda function for inference on prices of new houses.
B. Create an IAM role for Amazon SageMaker with access to the S3 bucket. Create a SageMaker AutoML job with SageMaker Autopilot pointing to the bucket with the dataset. Specify the price as the target attribute. Wait for the job to complete. Deploy the best model for predictions.
C. Create an Amazon SageMaker notebook with a new IAM role that is associated with the notebook. Pull the dataset from the S3 bucket. Explore different combinations of feature engineering transformations, regression algorithms, and hyperparameters. Compare all the results in the notebook, and deploy the most accurate configuration in an endpoint for predictions.
D. Create a service-linked role for Amazon Elastic Container Service (Amazon ECS) with access to the S3 bucket. Create an ECS cluster that is based on an AWS Deep Learning Containers image. Write the code to perform the feature engineering. Train a logistic regression model for predicting the price, pointing to the bucket with the dataset. Wait for the training job to complete. Perform the inferences.

正解：B

解説：
Explanation
The solution D meets the requirements with the least operational overhead because it uses Amazon SageMaker Autopilot, which is a fully managed service that automates the end-to-end process of building, training, and deploying machine learning models. Amazon SageMaker Autopilot can handle data preprocessing, feature engineering, algorithm selection, hyperparameter tuning, and model deployment. The company only needs to create an IAM role for Amazon SageMaker with access to the S3 bucket, create a SageMaker AutoML job pointing to the bucket with the dataset, specify the price as the target attribute, and wait for the job to complete. Amazon SageMaker Autopilot will generate a list of candidate models with different configurations and performance metrics, and the company can deploy the best model for predictions1.
The other options are not suitable because:
Option A: Creating a service-linked role for Amazon Elastic Container Service (Amazon ECS) with access to the S3 bucket, creating an ECS cluster based on an AWS Deep Learning Containers image, writing the code to perform the feature engineering, training a logistic regression model for predicting the price, and performing the inferences will incur more operational overhead than using Amazon SageMaker Autopilot. The company will have to manage the ECS cluster, the container image, the code, the model, and the inference endpoint. Moreover, logistic regression may not be the best algorithm for predicting the price, as it is more suitable for binary classification tasks2.
Option B: Creating an Amazon SageMaker notebook with a new IAM role that is associated with the notebook, pulling the dataset from the S3 bucket, exploring different combinations of feature engineering transformations, regression algorithms, and hyperparameters, comparing all the results in the notebook, and deploying the most accurate configuration in an endpoint for predictions will incur more operational overhead than using Amazon SageMaker Autopilot. The company will have to write the code for the feature engineering, the model training, the model evaluation, and the model deployment. The company will also have to manually compare the results and select the best configuration3.
Option C: Creating an IAM role with access to Amazon S3, Amazon SageMaker, and AWS Lambda, creating a training job with the SageMaker built-in XGBoost model pointing to the bucket with the dataset, specifying the price as the target feature, loading the model artifact to a Lambda function for inference on prices of new houses will incur more operational overhead than using Amazon SageMaker Autopilot. The company will have to create and manage the Lambda function, the model artifact, and the inference endpoint. Moreover, XGBoost may not be the best algorithm for predicting the price, as it is more suitable for classification and ranking tasks4.
References:
1: Amazon SageMaker Autopilot
2: Amazon Elastic Container Service
3: Amazon SageMaker Notebook Instances
4: Amazon SageMaker XGBoost Algorithm

質問 # 49
A data scientist receives a new dataset in .csv format and stores the dataset in Amazon S3. The data scientist will use this dataset to train a machine learning (ML) model.
The data scientist first needs to identify any potential data quality issues in the dataset. The data scientist must identify values that are missing or values that are not valid. The data scientist must also identify the number of outliers in the dataset.
Which solution will meet these requirements with the LEAST operational effort?)

A. Leave the dataset in .csv format. Import the data into Amazon SageMaker Data Wrangler. Use the Data Quality and Insights Report to retrieve the required information.
B. Create an AWS Glue job to transform the data from .csv format to Apache Parquet format. Import the data into Amazon SageMaker Data Wrangler. Use the Data Quality and Insights Report to retrieve the required information.
C. Leave the dataset in .csv format. Use an AWS Glue crawler and Amazon Athena with appropriate SQL queries to retrieve the required information.
D. Create an AWS Glue job to transform the data from .csv format to Apache Parquet format. Use an AWS Glue crawler and Amazon Athena with appropriate SQL queries to retrieve the required information.

正解：A

解説：
SageMaker Data Wrangler provides a built-in Data Quality and Insights Report, which can analyze datasets and provide insights such as:
* Missing values
* Invalid entries
* Column statistics
* Outlier detection
"Data Wrangler's Data Quality and Insights Report helps you detect and understand data quality issues including missing values, invalid data types, and outliers." Leaving the data in .csv format avoids unnecessary transformation steps and reduces operational complexity.
Simply importing the file and generating the report offers a low-effort, effective solution.

質問 # 50
A manufacturing company wants to use machine learning (ML) to automate quality control in its facilities. The facilities are in remote locations and have limited internet connectivity. The company has 20 TB of training data that consists of labeled images of defective product parts. The training data is in the corporate on-premises data center.
The company will use this data to train a model for real-time defect detection in new parts as the parts move on a conveyor belt in the facilities. The company needs a solution that minimizes costs for compute infrastructure and that maximizes the scalability of resources for training. The solution also must facilitate the company's use of an ML model in the low-connectivity environments.
Which solution will meet these requirements?

A. Move the training data to an Amazon S3 bucket. Train and evaluate the model by using Amazon SageMaker. Optimize the model by using SageMaker Neo. Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. Deploy the model on the edge device.
B. Train and evaluate the model on premises. Upload the model to an Amazon S3 bucket. Deploy the model on an Amazon SageMaker hosting services endpoint.
C. Train the model on premises. Upload the model to an Amazon S3 bucket. Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. Deploy the model on the edge device.
D. Move the training data to an Amazon S3 bucket. Train and evaluate the model by using Amazon SageMaker. Optimize the model by using SageMaker Neo. Deploy the model on a SageMaker hosting services endpoint.

正解：A

解説：
The solution C meets the requirements because it minimizes costs for compute infrastructure, maximizes the scalability of resources for training, and facilitates the use of an ML model in low-connectivity environments. The solution C involves the following steps:
Move the training data to an Amazon S3 bucket. This will enable the company to store the large amount of data in a durable, scalable, and cost-effective way. It will also allow the company to access the data from the cloud for training and evaluation purposes1.
Train and evaluate the model by using Amazon SageMaker. This will enable the company to use a fully managed service that provides various features and tools for building, training, tuning, and deploying ML models. Amazon SageMaker can handle large-scale data processing and distributed training, and it can leverage the power of AWS compute resources such as Amazon EC2, Amazon EKS, and AWS Fargate2.
Optimize the model by using SageMaker Neo. This will enable the company to reduce the size of the model and improve its performance and efficiency. SageMaker Neo can compile the model into an executable that can run on various hardware platforms, such as CPUs, GPUs, and edge devices3.
Set up an edge device in the manufacturing facilities with AWS IoT Greengrass. This will enable the company to deploy the model on a local device that can run inference in real time, even in low-connectivity environments. AWS IoT Greengrass can extend AWS cloud capabilities to the edge, and it can securely communicate with the cloud for updates and synchronization4.
Deploy the model on the edge device. This will enable the company to automate quality control in its facilities by using the model to detect defects in new parts as they move on a conveyor belt. The model can run inference locally on the edge device without requiring internet connectivity, and it can send the results to the cloud when the connection is available4.
The other options are not suitable because:
Option A: Deploying the model on a SageMaker hosting services endpoint will not facilitate the use of the model in low-connectivity environments, as it will require internet access to perform inference. Moreover, it may incur higher costs for hosting and data transfer than deploying the model on an edge device.
Option B: Training and evaluating the model on premises will not minimize costs for compute infrastructure, as it will require the company to maintain and upgrade its own hardware and software. Moreover, it will not maximize the scalability of resources for training, as it will limit the company's ability to leverage the cloud's elasticity and flexibility.
Option D: Training the model on premises will not minimize costs for compute infrastructure, nor maximize the scalability of resources for training, for the same reasons as option B.
References:
1: Amazon S3
2: Amazon SageMaker
3: SageMaker Neo
4: AWS IoT Greengrass

質問 # 51
A pharmaceutical company performs periodic audits of clinical trial sites to quickly resolve critical findings.
The company stores audit documents in text format. Auditors have requested help from a data science team to quickly analyze the documents. The auditors need to discover the 10 main topics within the documents to prioritize and distribute the review work among the auditing team members. Documents that describe adverse events must receive the highest priority.
A data scientist will use statistical modeling to discover abstract topics and to provide a list of the top words for each category to help the auditors assess the relevance of the topic.
Which algorithms are best suited to this scenario? (Choose two.)

A. Random Forest classifier
B. Latent Dirichlet allocation (LDA)
C. Neural topic modeling (NTM)
D. Linear support vector machine
E. Linear regression

正解：B、C

解説：
The algorithms that are best suited to this scenario are latent Dirichlet allocation (LDA) and neural topic modeling (NTM), as they are both unsupervised learning methods that can discover abstract topics from a collection of text documents. LDA and NTM can provide a list of the top words for each topic, as well as the topic distribution for each document, which can help the auditors assess the relevance and priority of the topic12.
The other options are not suitable because:
* Option B: A random forest classifier is a supervised learning method that can perform classification or regression tasks by using an ensemble of decision trees. A random forest classifier is not suitable for discovering abstract topics from text documents, as it requires labeled data and predefined classes3.
* Option D: A linear support vector machine is a supervised learning method that can perform classification or regression tasks by using a linear function that separates the data into different classes. A linear support vector machine is not suitable for discovering abstract topics from text documents, as it requires labeled data and predefined classes4.
* Option E: A linear regression is a supervised learning method that can perform regression tasks by using a linear function that models the relationship between a dependent variable and one or more independent variables. A linear regression is not suitable for discovering abstract topics from text documents, as it requires labeled data and a continuous output variable5.
References:
* 1: Latent Dirichlet Allocation
* 2: Neural Topic Modeling
* 3: Random Forest Classifier
* 4: Linear Support Vector Machine
* 5: Linear Regression

質問 # 52
A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.
Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population How should the Data Scientist correct this issue?

A. Drop all records from the dataset where age has been set to 0.
B. Drop the age feature from the dataset and train the model using the rest of the features.
C. Replace the age field value for records with a value of 0 with the mean or median value from the dataset
D. Use k-means clustering to handle missing features

正解：A

解説：
Explanation

質問 # 53
......

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