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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q171-Q176):

NEW QUESTION # 171
A company is running an Amazon SageMaker training job that will access data stored in its Amazon S3 bucket A compliance policy requires that the data never be transmitted across the internet How should the company set up the job?

A. Launch the notebook instances in a public subnet and access the data through the public S3 endpoint
B. Launch the notebook instances in a private subnet and access the data through a NAT gateway
C. Launch the notebook instances in a public subnet and access the data through a NAT gateway
D. Launch the notebook instances in a private subnet and access the data through an S3 VPC endpoint.

Answer: D

Explanation:
A private subnet is a subnet that does not have a route to the internet gateway, which means that the resources in the private subnet cannot access the internet or be accessed from the internet. An S3 VPC endpoint is a gateway endpoint that allows the resources in the VPC to access the S3 service without going through the internet. By launching the notebook instances in a private subnet and accessing the data through an S3 VPC endpoint, the company can set up the job in a secure and compliant way, as the data never leaves the AWS network and is not exposed to the internet. This can also improve the performance and reliability of the data transfer, as the traffic does not depend on the internet bandwidth or availability.
References:
Amazon VPC Endpoints - Amazon Virtual Private Cloud
Endpoints for Amazon S3 - Amazon Virtual Private Cloud
Connect to SageMaker Within your VPC - Amazon SageMaker
Working with VPCs and Subnets - Amazon Virtual Private Cloud

NEW QUESTION # 172
During mini-batch training of a neural network for a classification problem, a Data Scientist notices that training accuracy oscillates What is the MOST likely cause of this issue?

A. The class distribution in the dataset is imbalanced
B. The learning rate is very high
C. Dataset shuffling is disabled
D. The batch size is too big

Answer: C

NEW QUESTION # 173
An e-commerce company needs a customized training model to classify images of its shirts and pants products The company needs a proof of concept in 2 to 3 days with good accuracy Which compute choice should the Machine Learning Specialist select to train and achieve good accuracy on the model quickly?

A. m5 4xlarge (general purpose)
B. p3.2xlarge (GPU accelerated computing)
C. r5.2xlarge (memory optimized)
D. p3 8xlarge (GPU accelerated computing)

Answer: B

Explanation:
Image classification is a machine learning task that involves assigning labels to images based on their content.
Image classification can be performed using various algorithms, such as convolutional neural networks (CNNs), which are a type of deep learning model that can learn to extract high-level features from images. To train a customized image classification model, the e-commerce company needs a compute choice that can support the high computational demands of deep learning and provide good accuracy on the model quickly. A GPU accelerated computing instance, such as p3.2xlarge, is a suitable choice for this task, as it can leverage the parallel processing power of GPUs to speed up the training process and reduce the training time. A p3.
2xlarge instance has one NVIDIA Tesla V100 GPU, which can provide up to 125 teraflops of mixed- precision performance and 16 GB of GPU memory. A p3.2xlarge instance can also use various deep learning frameworks, such as TensorFlow, PyTorch, MXNet, etc., to build and train the image classification model. A p3.2xlarge instance is also more cost-effective than a p3.8xlarge instance, which has four NVIDIA Tesla V100 GPUs, as the latter may not be necessary for a proof of concept with a small dataset. Therefore, the Machine Learning Specialist should select p3.2xlarge as the compute choice to train and achieve good accuracy on the model quickly.
Amazon EC2 P3 Instances - Amazon Web Services
Image Classification - Amazon SageMaker
Convolutional Neural Networks - Amazon SageMaker
Deep Learning AMIs - Amazon Web Services

NEW QUESTION # 174
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 Glue ETL job, and an external Apache Hive metastore to search and discover metadata.
D. 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.

Answer: B

Explanation:
Explanation
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

NEW QUESTION # 175
A chemical company has developed several machine learning (ML) solutions to identify chemical process abnormalities. The time series values of independent variables and the labels are available for the past 2 years and are sufficient to accurately model the problem.
The regular operation label is marked as 0. The abnormal operation label is marked as 1 . Process abnormalities have a significant negative effect on the companys profits. The company must avoid these abnormalities.
Which metrics will indicate an ML solution that will provide the GREATEST probability of detecting an abnormality?

A. Precision = 0.61 Recall = 0.98
B. Precision = 0.7 Recall = 0.9
C. Precision = 0.98 Recall = 0.8
D. Precision = 0.91 Recall = 0.6

Answer: A

Explanation:
The metrics that will indicate an ML solution that will provide the greatest probability of detecting an abnormality are precision and recall. Precision is the ratio of true positives (TP) to the total number of predicted positives (TP + FP), where FP is false positives. Recall is the ratio of true positives (TP) to the total number of actual positives (TP + FN), where FN is false negatives. A high precision means that the ML solution has a low rate of false alarms, while a high recall means that the ML solution has a high rate of true detections. For the chemical company, the goal is to avoid process abnormalities, which are marked as 1 in the labels. Therefore, the company needs an ML solution that has a high recall for the positive class, meaning that it can detect most of the abnormalities and minimize the false negatives. Among the four options, option B has the highest recall for the positive class, which is 0.98. This means that the ML solution can detect 98% of the abnormalities and miss only 2%. Option B also has a reasonable precision for the positive class, which is 0.61. This means that the ML solution has a false alarm rate of 39%, which may be acceptable for the company, depending on the cost and benefit analysis. The other options have lower recall for the positive class, which means that they have higher false negative rates, which can be more detrimental for the company than false positive rates.
1: AWS Certified Machine Learning - Specialty Exam Guide
2: AWS Training - Machine Learning on AWS
3: AWS Whitepaper - An Overview of Machine Learning on AWS
4: Precision and recall

NEW QUESTION # 176
......

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q171-Q176):

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