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Amazon AWS Certified Machine Learning - Specialty 認定 AWS-Certified-Machine-Learning-Specialty 試験問題 (Q93-Q98):

質問 # 93
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%r 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 learning rate should be increased because the optimization process was trapped at a local minimum.
C. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
D. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.

正解：D

質問 # 94
A Machine Learning Specialist is working with multiple data sources containing billions of records that need to be joined. What feature engineering and model development approach should the Specialist take with a dataset this large?

A. Use an Amazon SageMaker notebook for both feature engineering and model development
B. Use an Amazon SageMaker notebook for feature engineering and Amazon ML for model development
C. Use Amazon EMR for feature engineering and Amazon SageMaker SDK for model development
D. Use Amazon ML for both feature engineering and model development.

正解：C

解説：
Amazon EMR is a service that can process large amounts of data efficiently and cost-effectively. It can run distributed frameworks such as Apache Spark, which can perform feature engineering on big data. Amazon SageMaker SDK is a Python library that can interact with Amazon SageMaker service to train and deploy machine learning models. It can also use Amazon EMR as a data source for training data. References:
Amazon EMR
Amazon SageMaker SDK

質問 # 95
A Machine Learning Specialist is developing recommendation engine for a photography blog Given a picture, the recommendation engine should show a picture that captures similar objects The Specialist would like to create a numerical representation feature to perform nearest-neighbor searches What actions would allow the Specialist to get relevant numerical representations?

A. Reduce image resolution and use reduced resolution pixel values as features
B. Run images through a neural network pie-trained on ImageNet, and collect the feature vectors from the penultimate layer
C. Use Amazon Mechanical Turk to label image content and create a one-hot representation indicating the presence of specific labels
D. Average colors by channel to obtain three-dimensional representations of images.

正解：B

解説：
A neural network pre-trained on ImageNet is a deep learning model that has been trained on a large dataset of images containing 1000 classes of objects. The model can learn to extract high-level features from the images that capture the semantic and visual information of the objects. The penultimate layer of the model is the layer before the final output layer, and it contains a feature vector that represents the input image in a lower-dimensional space. By running images through a pre-trained neural network and collecting the feature vectors from the penultimate layer, the Specialist can obtain relevant numerical representations that can be used for nearest-neighbor searches. The feature vectors can capture the similarity between images based on the presence and appearance of similar objects, and they can be compared using distance metrics such as Euclidean distance or cosine similarity. This approach can enable the recommendation engine to show a picture that captures similar objects to a given picture.
References:
ImageNet - Wikipedia
How to use a pre-trained neural network to extract features from images | by Rishabh Anand | Analytics Vidhya | Medium Image Similarity using Deep Ranking | by Aditya Oke | Towards Data Science

質問 # 96
A Machine Learning Specialist at a company sensitive to security is preparing a dataset for model training. The dataset is stored in Amazon S3 and contains Personally Identifiable Information (Pll). The dataset:
* Must be accessible from a VPC only.
* Must not traverse the public internet.
How can these requirements be satisfied?

A. Create a VPC endpoint and apply a bucket access policy that restricts access to the given VPC endpoint and the VPC.
B. Create a VPC endpoint and use Network Access Control Lists (NACLs) to allow traffic between only the given VPC endpoint and an Amazon EC2 instance.
C. Create a VPC endpoint and apply a bucket access policy that allows access from the given VPC endpoint and an Amazon EC2 instance.
D. Create a VPC endpoint and use security groups to restrict access to the given VPC endpoint and an Amazon EC2 instance.

正解：C

質問 # 97
A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:
* Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.
* Support event-driven ETL pipelines.
* Provide a quick and easy way to understand metadata.
Which approach meets trfese requirements?

A. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.
B. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.
C. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.
D. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.

正解：B

解説：
To build a robust serverless data lake on Amazon S3 that meets the requirements, the financial services company should use the following AWS services:
AWS Glue crawler: This is a service that connects to a data store, progresses through a prioritized list of classifiers to determine the schema for the data, and then creates metadata tables in the AWS Glue Data Catalog1. The company can use an AWS Glue crawler to crawl the S3 data and infer the schema, format, and partition structure of the data. The crawler can also detect schema changes and update the metadata tables accordingly. This enables the company to support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum, which are serverless interactive query services that use the AWS Glue Data Catalog as a central location for storing and retrieving table metadata23.
AWS Lambda function: This is a service that lets you run code without provisioning or managing servers. You pay only for the compute time you consume - there is no charge when your code is not running. You can also use AWS Lambda to create event-driven ETL pipelines, by triggering other AWS services based on events such as object creation or deletion in S3 buckets4. The company can use an AWS Lambda function to trigger an AWS Glue ETL job, which is a serverless way to extract, transform, and load data for analytics. The AWS Glue ETL job can perform various data processing tasks, such as converting data formats, filtering, aggregating, joining, and more.
AWS Glue Data Catalog: This is a managed service that acts as a central metadata repository for data assets across AWS and on-premises data sources. The AWS Glue Data Catalog provides a uniform repository where disparate systems can store and find metadata to keep track of data in data silos, and use that metadata to query and transform the data. The company can use the AWS Glue Data Catalog to search and discover metadata, such as table definitions, schemas, and partitions. The AWS Glue Data Catalog also integrates with Amazon Athena, Amazon Redshift Spectrum, Amazon EMR, and AWS Glue ETL jobs, providing a consistent view of the data across different query and analysis services.
References:
1: What Is a Crawler? - AWS Glue
2: What Is Amazon Athena? - Amazon Athena
3: Amazon Redshift Spectrum - Amazon Redshift
4: What is AWS Lambda? - AWS Lambda
5: AWS Glue ETL Jobs - AWS Glue
6: What Is the AWS Glue Data Catalog? - AWS Glue

質問 # 98
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Amazon AWS-Certified-Machine-Learning-Specialty「AWS Certified Machine Learning - Specialty」認証試験に合格することが簡単ではなくて、Amazon AWS-Certified-Machine-Learning-Specialty証明書は君にとってはIT業界に入るの一つの手づるになるかもしれません、Amazon AWS-Certified-Machine-Learning-Specialty合格体験談望ましい問題集を支払うと、あなたはすぐにそれを得ることができます、Amazon AWS-Certified-Machine-Learning-Specialty合格体験談チャンスは準備された心を支持します、Amazon AWS-Certified-Machine-Learning-Specialty合格体験談試験は非常に難しいでしょう、AWS-Certified-Machine-Learning-Specialty試験問題を選択すると、より良い自己になります、もしあなたはAWS-Certified-Machine-Learning-Specialty学習資料を購入したら、あなたは我々のAWS Certified Machine Learning - Specialtyテスト練習問題集をできるだけ速やかにダウンロードできます。

あたしここにいられない、さあ、話さなくては、Amazon AWS-Certified-Machine-Learning-Specialty「AWS Certified Machine Learning - Specialty」認証試験に合格することが簡単ではなくて、Amazon AWS-Certified-Machine-Learning-Specialty証明書は君にとってはIT業界に入るの一つの手づるになるかもしれません。

こんなに便利な AWS-Certified-Machine-Learning-Specialty 問題集

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