Cloud Practice

#71 (Accuracy: 100% / 13 votes)

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 learning rate should be increased because the optimization process was trapped at a local minimum.
B. The dropout rate at the flatten layer should be increased because the model is not generalized enough.
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.

#72 (Accuracy: 100% / 4 votes)

A media company wants to create a solution that identifies celebrities in pictures that users upload. The company also wants to identify the IP address and the timestamp details from the users so the company can prevent users from uploading pictures from unauthorized locations.

Which solution will meet these requirements with LEAST development effort?

A. Use AWS Panorama to identify celebrities in the pictures. Use AWS CloudTrail to capture IP address and timestamp details.
B. Use AWS Panorama to identify celebrities in the pictures. Make calls to the AWS Panorama Device SDK to capture IP address and timestamp details.
C. Use Amazon Rekognition to identify celebrities in the pictures. Use AWS CloudTrail to capture IP address and timestamp details.
D. Use Amazon Rekognition to identify celebrities in the pictures. Use the text detection feature to capture IP address and timestamp details.

#73 (Accuracy: 100% / 6 votes)

A company is converting a large number of unstructured paper receipts into images. The company wants to create a model based on natural language processing
(NLP) to find relevant entities such as date, location, and notes, as well as some custom entities such as receipt numbers.
The company is using optical character recognition (OCR) to extract text for data labeling. However, documents are in different structures and formats, and the company is facing challenges with setting up the manual workflows for each document type. Additionally, the company trained a named entity recognition (NER) model for custom entity detection using a small sample size. This model has a very low confidence score and will require retraining with a large dataset.
Which solution for text extraction and entity detection will require the LEAST amount of effort?

A. Extract text from receipt images by using Amazon Textract. Use the Amazon SageMaker BlazingText algorithm to train on the text for entities and custom entities.
B. Extract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use the NER deep learning model to extract entities.
C. Extract text from receipt images by using Amazon Textract. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.
D. Extract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.

#74 (Accuracy: 100% / 3 votes)

A company decides to use Amazon SageMaker to develop machine learning (ML) models. The company will host SageMaker notebook instances in a VPC. The company stores training data in an Amazon S3 bucket. Company security policy states that SageMaker notebook instances must not have internet connectivity.

Which solution will meet the company’s security requirements?

A. Connect the SageMaker notebook instances that are in the VPC by using AWS Site-to-Site VPN to encrypt all internet-bound traffic. Configure VPC flow logs. Monitor all network traffic to detect and prevent any malicious activity.
B. Configure the VPC that contains the SageMaker notebook instances to use VPC interface endpoints to establish connections for training and hosting. Modify any existing security groups that are associated with the VPC interface endpoint to allow only outbound connections for training and hosting.
C. Create an IAM policy that prevents access the internet. Apply the IAM policy to an IAM role. Assign the IAM role to the SageMaker notebook instances in addition to any IAM roles that are already assigned to the instances.
D. Create VPC security groups to prevent all incoming and outgoing traffic. Assign the security groups to the SageMaker notebook instances.

#75 (Accuracy: 100% / 2 votes)

A machine learning (ML) engineer uses Bayesian optimization for a hyperpara meter tuning job in Amazon SageMaker. The ML engineer uses precision as the objective metric.

The ML engineer wants to use recall as the objective metric. The ML engineer also wants to expand the hyperparameter range for a new hyperparameter tuning job. The new hyperparameter range will include the range of the previously performed tuning job.

Which approach will run the new hyperparameter tuning job in the LEAST amount of time?

A. Use a warm start hyperparameter tuning job.
B. Use a checkpointing hyperparameter tuning job.
C. Use the same random seed for the hyperparameter tuning job.
D. Use multiple jobs in parallel for the hyperparameter tuning job.

#76 (Accuracy: 90% / 5 votes)

A manufacturing company asks its machine learning specialist to develop a model that classifies defective parts into one of eight defect types. The company has provided roughly 100,000 images per defect type for training. During the initial training of the image classification model, the specialist notices that the validation accuracy is 80%, while the training accuracy is 90%. It is known that human-level performance for this type of image classification is around 90%.
What should the specialist consider to fix this issue?

A. A longer training time
B. Making the network larger
C. Using a different optimizer
D. Using some form of regularization

#77 (Accuracy: 100% / 4 votes)

A data scientist is working on a forecast problem by using a dataset that consists of .csv files that are stored in Amazon S3. The files contain a timestamp variable in the following format:

March 1st, 2020, 08:14pm -

There is a hypothesis about seasonal differences in the dependent variable. This number could be higher or lower for weekdays because some days and hours present varying values, so the day of the week, month, or hour could be an important factor. As a result, the data scientist needs to transform the timestamp into weekdays, month, and day as three separate variables to conduct an analysis.

Which solution requires the LEAST operational overhead to create a new dataset with the added features?

A. Create an Amazon EMR cluster. Develop PySpark code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.
B. Create a processing job in Amazon SageMaker. Develop Python code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.
C. Create a new flow in Amazon SageMaker Data Wrangler. Import the S3 file, use the Featurize date/time transform to generate the new variables, and save the dataset as a new file in Amazon S3.
D. Create an AWS Glue job. Develop code that can read the timestamp variable as a string, transform and create the new variables, and save the dataset as a new file in Amazon S3.

#78 (Accuracy: 100% / 5 votes)

A machine learning (ML) specialist is training a linear regression model. The specialist notices that the model is overfitting. The specialist applies an L1 regularization parameter and runs the model again. This change results in all features having zero weights.

What should the ML specialist do to improve the model results?

A. Increase the L1 regularization parameter. Do not change any other training parameters.
B. Decrease the L1 regularization parameter. Do not change any other training parameters.
C. Introduce a large L2 regularization parameter. Do not change the current L1 regularization value.
D. Introduce a small L2 regularization parameter. Do not change the current L1 regularization value.

#79 (Accuracy: 100% / 4 votes)

A wildlife research company has a set of images of lions and cheetahs. The company created a dataset of the images. The company labeled each image with a binary label that indicates whether an image contains a lion or cheetah. The company wants to train a model to identify whether new images contain a lion or cheetah.

Which Amazon SageMaker algorithm will meet this requirement?

A. XGBoost
B. Image Classification - TensorFlow
C. Object Detection - TensorFlow
D. Semantic segmentation - MXNet

#80 (Accuracy: 100% / 4 votes)

An engraving company wants to automate its quality control process for plaques. The company performs the process before mailing each customized plaque to a customer. The company has created an Amazon S3 bucket that contains images of defects that should cause a plaque to be rejected. Low-confidence predictions must be sent to an internal team of reviewers who are using Amazon Augmented AI (Amazon A2I).

Which solution will meet these requirements?

A. Use Amazon Textract for automatic processing. Use Amazon A2I with Amazon Mechanical Turk for manual review.
B. Use Amazon Rekognition for automatic processing. Use Amazon A2I with a private workforce option for manual review.
C. Use Amazon Transcribe for automatic processing. Use Amazon A2I with a private workforce option for manual review.
D. Use AWS Panorama for automatic processing. Use Amazon A2I with Amazon Mechanical Turk for manual review.

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