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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q71-Q76):
NEW QUESTION # 71
A Machine Learning Specialist wants to bring a custom algorithm to Amazon SageMaker. The Specialist implements the algorithm in a Docker container supported by Amazon SageMaker.
How should the Specialist package the Docker container so that Amazon SageMaker can launch the training correctly?
- A. Copy the training program to directory /opt/ml/train
- B. Configure the training program as an ENTRYPOINT named train
- C. Use CMD config in the Dockerfile to add the training program as a CMD of the image
- D. Modify the bash_profile file in the container and add a bash command to start the training program
Answer: B
Explanation:
Explanation
To use a custom algorithm in Amazon SageMaker, the Docker container image must have an executable file named train that acts as the ENTRYPOINT for the container. This file is responsible for running the training code and communicating with the Amazon SageMaker service. The train file must be in the PATH of the container and have execute permissions. The other options are not valid ways to package the Docker container for Amazon SageMaker. References:
Use Docker containers to build models - Amazon SageMaker
Create a container with your own algorithms and models - Amazon SageMaker
NEW QUESTION # 72
A machine learning (ML) specialist is using Amazon SageMaker hyperparameter optimization (HPO) to improve a model's accuracy. The learning rate parameter is specified in the following HPO configuration:
During the results analysis, the ML specialist determines that most of the training jobs had a learning rate between 0.01 and 0.1. The best result had a learning rate of less than 0.01. Training jobs need to run regularly over a changing dataset. The ML specialist needs to find a tuning mechanism that uses different learning rates more evenly from the provided range between MinValue and MaxValue.
Which solution provides the MOST accurate result?
- A. Modify the HPO configuration as follows:
Select the most accurate hyperparameter configuration form this training job. - B. Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue. Divide the number of training jobs for each HPO job by three:
[0.01, 0.1]
[0.001, 0.01]
[0.0001, 0.001]
Select the most accurate hyperparameter configuration form these three HPO jobs. - C. Run three different HPO jobs that use different learning rates form the following intervals for MinValue and MaxValue while using the same number of training jobs for each HPO job:
[0.01, 0.1]
[0.001, 0.01]
[0.0001, 0.001]
Select the most accurate hyperparameter configuration form these three HPO jobs. - D. Modify the HPO configuration as follows:
Select the most accurate hyperparameter configuration form this HPO job.
Answer: A
NEW QUESTION # 73
A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier:
Total number of images available = 1,000
Test set images = 100 (constant test set)
The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.
Which techniques can be used by the ML Specialist to improve this specific test error?
- A. Increase the number of layers for the neural network.
- B. Increase the number of epochs for model training
- C. Increase the training data by adding variation in rotation for training images.
- D. Increase the dropout rate for the second-to-last layer.
Answer: B
NEW QUESTION # 74
A company plans to build a custom natural language processing (NLP) model to classify and prioritize user feedback. The company hosts the data and all machine learning (ML) infrastructure in the AWS Cloud. The ML team works from the company's office, which has an IPsec VPN connection to one VPC in the AWS Cloud.
The company has set both the enableDnsHostnames attribute and the enableDnsSupport attribute of the VPC to true. The company's DNS resolvers point to the VPC DNS. The company does not allow the ML team to access Amazon SageMaker notebooks through connections that use the public internet. The connection must stay within a private network and within the AWS internal network.
Which solution will meet these requirements with the LEAST development effort?
- A. Create a VPC interface endpoint for the SageMaker notebook in the VPC. Access the notebook through a VPN connection and the VPC endpoint.
- B. Create a bastion host by using Amazon EC2 in a public subnet within the VPC. Log in to the bastion host through a VPN connection. Access the SageMaker notebook from the bastion host.
- C. Create a NAT gateway in the VPC. Access the SageMaker notebook HTTPS endpoint through a VPN connection and the NAT gateway.
- D. Create a bastion host by using Amazon EC2 in a private subnet within the VPC with a NAT gateway.
Log in to the bastion host through a VPN connection. Access the SageMaker notebook from the bastion host.
Answer: A
Explanation:
In this scenario, the company requires that access to the Amazon SageMaker notebook remain within the AWS internal network, avoiding the public internet. By creating a VPC interface endpoint for SageMaker, the company can ensure that traffic to the SageMaker notebook remains internal to the VPC and is accessible over a private connection. The VPC interface endpoint allows private network access to AWS services, and it operates over AWS's internal network, respecting the security and connectivity policies the company requires.
This solution requires minimal development effort compared to options involving bastion hosts or NAT gateways, as it directly provides private network access to the SageMaker notebook.
NEW QUESTION # 75
A bank has collected customer data for 10 years in CSV format. The bank stores the data in an on-premises server. A data science team wants to use Amazon SageMaker to build and train a machine learning (ML) model to predict churn probability. The team will use the historical data. The data scientists want to perform data transformations quickly and to generate data insights before the team builds a model for production.
Which solution will meet these requirements with the LEAST development effort?
- A. Upload the data into the SageMaker Data Wrangler console directly. Perform data transformations and generate insights within Data Wrangler.
- B. Upload the data into an Amazon S3 bucket. Allow SageMaker to access the data that is in the bucket.Import the data from the bucket into SageMaker Data Wrangler. Perform data transformations in Data Wrangler. Save the data into a second S3 bucket. Use a SageMaker Studio notebook to generate data insights.
- C. Upload the data into the SageMaker Data Wrangler console directly. Allow SageMaker and Amazon QuickSight to access the data that is in an Amazon S3 bucket. Perform data transformations in Data Wrangler and save the transformed data into a second S3 bucket. Use QuickSight to generate data insights.
- D. Upload the data into an Amazon S3 bucket. Allow SageMaker to access the data that is in the bucket.
Import the data from the S3 bucket into SageMaker Data Wrangler. Perform data transformations and generate insights within Data Wrangler.
Answer: D
Explanation:
To prepare and transform historical data efficiently with minimal setup, Amazon SageMaker Data Wrangler is the optimal tool. Data Wrangler simplifies data preprocessing and exploratory data analysis (EDA) by providing a graphical interface for transformations and insights. By first uploading the CSV data to Amazon S3, the data becomes easily accessible to SageMaker and can be imported directly into Data Wrangler.
Once in Data Wrangler, the team can perform required data transformations and generate insights in a single workflow, avoiding the need for additional tools like Amazon QuickSight or further notebook configuration.
This approach provides the simplest and most integrated solution for the data science team.
NEW QUESTION # 76
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