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NEW QUESTION # 23
A Machine Learning team uses Amazon SageMaker to train an Apache MXNet handwritten digit classifier model using a research dataset. The team wants to receive a notification when the model is overfitting. Auditors want to view the Amazon SageMaker log activity report to ensure there are no unauthorized API calls.
What should the Machine Learning team do to address the requirements with the least amount of code and fewest steps?
- A. Implement an AWS Lambda function to long Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
- B. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
- C. Implement an AWS Lambda function to log Amazon SageMaker API calls to AWS CloudTrail. Add code to push a custom metric to Amazon CloudWatch. Create an alarm in CloudWatch with Amazon SNS to receive a notification when the model is overfitting.
- D. Use AWS CloudTrail to log Amazon SageMaker API calls to Amazon S3. Set up Amazon SNS to receive a notification when the model is overfitting.
Answer: B
Explanation:
To log Amazon SageMaker API calls, the team can use AWS CloudTrail, which is a service that provides a record of actions taken by a user, role, or an AWS service in SageMaker1. CloudTrail captures all API calls for SageMaker, with the exception of InvokeEndpoint and InvokeEndpointAsync, as events1. The calls captured include calls from the SageMaker console and code calls to the SageMaker API operations1. The team can create a trail to enable continuous delivery of CloudTrail events to an Amazon S3 bucket, and configure other AWS services to further analyze and act upon the event data collected in CloudTrail logs1. The auditors can view the CloudTrail log activity report in the CloudTrail console or download the log files from the S3 bucket1.
To receive a notification when the model is overfitting, the team can add code to push a custom metric to Amazon CloudWatch, which is a service that provides monitoring and observability for AWS resources and applications2. The team can use the MXNet metric API to define and compute the custom metric, such as the validation accuracy or the validation loss, and use the boto3 CloudWatch client to put the metric data to CloudWatch3 . The team can then create an alarm in CloudWatch with Amazon SNS to receive a notification when the custom metric crosses a threshold that indicates overfitting . For example, the team can set the alarm to trigger when the validation loss increases for a certain number of consecutive periods, which means the model is learning the noise in the training data and not generalizing well to the validation data.
References:
1: Log Amazon SageMaker API Calls with AWS CloudTrail - Amazon SageMaker
2: What Is Amazon CloudWatch? - Amazon CloudWatch
3: Metric API - Apache MXNet documentation
4: CloudWatch - Boto 3 Docs 1.20.21 documentation
5: Creating Amazon CloudWatch Alarms - Amazon CloudWatch
6: What is Amazon Simple Notification Service? - Amazon Simple Notification Service
7: Overfitting and Underfitting - Machine Learning Crash Course
NEW QUESTION # 24
A company is building a line-counting application for use in a quick-service restaurant. The company wants to use video cameras pointed at the line of customers at a given register to measure how many people are in line and deliver notifications to managers if the line grows too long. The restaurant locations have limited bandwidth for connections to external services and cannot accommodate multiple video streams without impacting other operations.
Which solution should a machine learning specialist implement to meet these requirements?
- A. Install cameras compatible with Amazon Kinesis Video Streams to stream the data to AWS over the restaurant's existing internet connection. Write an AWS Lambda function to take an image and send it to Amazon Rekognition to count the number of faces in the image. Send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.
- B. Deploy AWS DeepLens cameras in the restaurant to capture video. Enable Amazon Rekognition on the AWS DeepLens device, and use it to trigger a local AWS Lambda function when a person is recognized. Use the Lambda function to send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.
- C. Build a custom model in Amazon SageMaker to recognize the number of people in an image. Install cameras compatible with Amazon Kinesis Video Streams in the restaurant. Write an AWS Lambda function to take an image. Use the SageMaker endpoint to call the model to count people. Send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.
- D. Build a custom model in Amazon SageMaker to recognize the number of people in an image. Deploy AWS DeepLens cameras in the restaurant. Deploy the model to the cameras. Deploy an AWS Lambda function to the cameras to use the model to count people and send an Amazon Simple Notification Service (Amazon SNS) notification if the line is too long.
Answer: A
NEW QUESTION # 25
An agency collects census information within a country to determine healthcare and social program needs by province and city. The census form collects responses for approximately 500 questions from each citizen Which combination of algorithms would provide the appropriate insights? (Select TWO )
- A. The k-means algorithm
- B. The principal component analysis (PCA) algorithm
- C. The Random Cut Forest (RCF) algorithm
- D. The factorization machines (FM) algorithm
- E. The Latent Dirichlet Allocation (LDA) algorithm
Answer: A,B
Explanation:
The PCA and K-means algorithms are useful in collection of data using census form.
NEW QUESTION # 26
A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.
Which prior probability distribution should the ML Specialist use for this variable?
- A. Uniform distribution
- B. Binomial distribution
- C. Normal distribution
- D. Poisson distribution ,
Answer: A
NEW QUESTION # 27
A company wants to segment a large group of customers into subgroups based on shared characteristics. The company's data scientist is planning to use the Amazon SageMaker built-in k-means clustering algorithm for this task. The data scientist needs to determine the optimal number of subgroups (k) to use.
Which data visualization approach will MOST accurately determine the optimal value of k?
- A. Run the k-means clustering algorithm for a range of k. For each value of k, calculate the sum of squared errors (SSE). Plot a line chart of the SSE for each value of k. The optimal value of k is the point after which the curve starts decreasing in a linear fashion.
- B. Create a t-distributed stochastic neighbor embedding (t-SNE) plot for a range of perplexity values. The optimal value of k is the value of perplexity, where the clusters start to look reasonably separated.
- C. Calculate the principal component analysis (PCA) components. Create a line plot of the number of components against the explained variance. The optimal value of k is the number of PCA components after which the curve starts decreasing in a linear fashion.
- D. Calculate the principal component analysis (PCA) components. Run the k-means clustering algorithm for a range of k by using only the first two PCA components. For each value of k, create a scatter plot with a different color for each cluster. The optimal value of k is the value where the clusters start to look reasonably separated.
Answer: A
Explanation:
Explanation
The solution D is the best data visualization approach to determine the optimal value of k for the k-means clustering algorithm. The solution D involves the following steps:
Run the k-means clustering algorithm for a range of k. For each value of k, calculate the sum of squared errors (SSE). The SSE is a measure of how well the clusters fit the data. It is calculated by summing the squared distances of each data point to its closest cluster center. A lower SSE indicates a better fit, but it will always decrease as the number of clusters increases. Therefore, the goal is to find the smallest value of k that still has a low SSE1.
Plot a line chart of the SSE for each value of k. The line chart will show how the SSE changes as the value of k increases. Typically, the line chart will have a shape of an elbow, where the SSE drops rapidly at first and then levels off. The optimal value of k is the point after which the curve starts decreasing in a linear fashion. This point is also known as the elbow point, and it represents the balance between the number of clusters and the SSE1.
The other options are not suitable because:
Option A: Calculating the principal component analysis (PCA) components, running the k-means clustering algorithm for a range of k by using only the first two PCA components, and creating a scatter plot with a different color for each cluster will not accurately determine the optimal value of k. PCA is a technique that reduces the dimensionality of the data by transforming it into a new set of features that capture the most variance in the data. However, PCA may not preserve the original structure and distances of the data, and it may lose some information in the process. Therefore, running the k-means clustering algorithm on the PCA components may not reflect the true clusters in the data. Moreover, using only the first two PCA components may not capture enough variance to represent the data well. Furthermore, creating a scatter plot may not be reliable, as it depends on the subjective judgment of the data scientist to decide when the clusters look reasonably separated2.
Option B: Calculating the PCA components and creating a line plot of the number of components against the explained variance will not determine the optimal value of k. This approach is used to determine the optimal number of PCA components to use for dimensionality reduction, not for clustering. The explained variance is the ratio of the variance of each PCA component to the total variance of the data. The optimal number of PCA components is the point where adding more components does not significantly increase the explained variance. However, this number may not correspond to the optimal number of clusters, as PCA and k-means clustering have different objectives and assumptions2.
Option C: Creating a t-distributed stochastic neighbor embedding (t-SNE) plot for a range of perplexity values will not determine the optimal value of k. t-SNE is a technique that reduces the dimensionality of the data by embedding it into a lower-dimensional space, such as a two-dimensional plane. t-SNE preserves the local structure and distances of the data, and it can reveal clusters and patterns in the data.
However, t-SNE does not assign labels or centroids to the clusters, and it does not provide a measure of how well the clusters fit the data. Therefore, t-SNE cannot determine the optimal number of clusters, as it only visualizes the data. Moreover, t-SNE depends on the perplexity parameter, which is a measure of how many neighbors each point considers. The perplexity parameter can affect the shape and size of the clusters, and there is no optimal value for it. Therefore, creating a t-SNE plot for a range of perplexity values may not be consistent or reliable3.
References:
1: How to Determine the Optimal K for K-Means?
2: Principal Component Analysis
3: t-Distributed Stochastic Neighbor Embedding
NEW QUESTION # 28
For the given confusion matrix, what is the recall and precision of the model?
- A. Recall = 0.8 Precision = 0.92
- B. Recall = 0.92 Precision = 0.84
- C. Recall = 0.92 Precision = 0.8
- D. Recall = 0.84 Precision = 0.8
Answer: B
NEW QUESTION # 29
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 Amazon Rekognition to identify celebrities in the pictures. Use the text detection feature to capture IP address and timestamp details.
- C. Use AWS Panorama to identify celebrities in the pictures. Make calls to the AWS Panorama Device SDK to capture IP address and timestamp details.
- D. Use Amazon Rekognition to identify celebrities in the pictures. Use AWS CloudTrail to capture IP address and timestamp details.
Answer: D
Explanation:
Explanation
The solution C will meet the requirements with the least development effort because it uses Amazon Rekognition and AWS CloudTrail, which are fully managed services that can provide the desired functionality. The solution C involves the following steps:
Use Amazon Rekognition to identify celebrities in the pictures. Amazon Rekognition is a service that can analyze images and videos and extract insights such as faces, objects, scenes, emotions, and more.
Amazon Rekognition also provides a feature called Celebrity Recognition, which can recognize thousands of celebrities across a number of categories, such as politics, sports, entertainment, and media. Amazon Rekognition can return the name, face, and confidence score of the recognized celebrities, as well as additional information such as URLs and biographies1.
Use AWS CloudTrail to capture IP address and timestamp details. AWS CloudTrail is a service that can record the API calls and events made by or on behalf of AWS accounts. AWS CloudTrail can provide information such as the source IP address, the user identity, the request parameters, and the response elements of the API calls. AWS CloudTrail can also deliver the event records to an Amazon S3 bucket or an Amazon CloudWatch Logs group for further analysis and auditing2.
The other options are not suitable because:
Option A: Using AWS Panorama to identify celebrities in the pictures and using AWS CloudTrail to capture IP address and timestamp details will not meet the requirements effectively. AWS Panorama is a service that can extend computer vision to the edge, where it can run inference on video streams from cameras and other devices. AWS Panorama is not designed for identifying celebrities in pictures, and it may not provide accurate or relevant results. Moreover, AWS Panorama requires the use of an AWS Panorama Appliance or a compatible device, which may incur additional costs and complexity3.
Option B: Using AWS Panorama to identify celebrities in the pictures and making calls to the AWS Panorama Device SDK to capture IP address and timestamp details will not meet the requirements effectively, for the same reasons as option A. Additionally, making calls to the AWS Panorama Device SDK will require more development effort than using AWS CloudTrail, as it will involve writing custom code and handling errors and exceptions4.
Option D: Using Amazon Rekognition to identify celebrities in the pictures and using the text detection feature to capture IP address and timestamp details will not meet the requirements effectively. The text detection feature of Amazon Rekognition is used to detect and recognize text in images and videos, such as street names, captions, product names, and license plates. It is not suitable for capturing IP address and timestamp details, as these are not part of the pictures that users upload. Moreover, the text detection feature may not be accurate or reliable, as it depends on the quality and clarity of the text in the images and videos5.
References:
1: Amazon Rekognition Celebrity Recognition
2: AWS CloudTrail Overview
3: AWS Panorama Overview
4: AWS Panorama Device SDK
5: Amazon Rekognition Text Detection
NEW QUESTION # 30
A Machine Learning Specialist has completed a proof of concept for a company using a small data sample, and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker. The historical training data is stored in Amazon RDS.
Which approach should the Specialist use for training a model using that data?
- A. Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in.
- B. Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the notebook to pull data in for fast access.
- C. Write a direct connection to the SQL database within the notebook and pull data in
- D. Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
Answer: D
NEW QUESTION # 31
A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset Which tool should be used to improve the validation accuracy?
- A. Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers
- B. Amazon SageMaker BlazingText allow mode
- C. Amazon Comprehend syntax analysts and entity detection
- D. Natural Language Toolkit (NLTK) stemming and stop word removal
Answer: A
Explanation:
Explanation
Term frequency-inverse document frequency (TF-IDF) is a technique that assigns a weight to each word in a document based on how important it is to the meaning of the document. The term frequency (TF) measures how often a word appears in a document, while the inverse document frequency (IDF) measures how rare a word is across a collection of documents. The TF-IDF weight is the product of the TF and IDF values, and it is high for words that are frequent in a specific document but rare in the overall corpus. TF-IDF can help improve the validation accuracy of a sentiment analysis model by reducing the impact of common words that have little or no sentiment value, such as "the", "a", "and", etc. Scikit-learn is a popular Python library for machine learning that provides a TF-IDF vectorizer class that can transform a collection of text documents into a matrix of TF-IDF features. By using this tool, the Data Scientist can create a more informative and discriminative feature representation for the sentiment analysis task.
References:
TfidfVectorizer - scikit-learn
Text feature extraction - scikit-learn
TF-IDF for Beginners | by Jana Schmidt | Towards Data Science
Sentiment Analysis: Concept, Analysis and Applications | by Susan Li | Towards Data Science
NEW QUESTION # 32
IT leadership wants Jo transition a company's existing machine learning data storage environment to AWS as a temporary ad hoc solution The company currently uses a custom software process that heavily leverages SOL as a query language and exclusively stores generated csv documents for machine learning The ideal state for the company would be a solution that allows it to continue to use the current workforce of SQL experts The solution must also support the storage of csv and JSON files, and be able to query over semi-structured data The following are high priorities for the company:
* Solution simplicity
* Fast development time
* Low cost
* High flexibility
What technologies meet the company's requirements?
- A. Amazon Redshift and AWS Glue
- B. Amazon DynamoDB and DynamoDB Accelerator (DAX)
- C. Amazon S3 and Amazon Athena
- D. Amazon RDS and Amazon ES
Answer: C
Explanation:
Explanation
Amazon S3 and Amazon Athena are technologies that meet the company's requirements for a temporary ad hoc solution for machine learning data storage and query. Amazon S3 and Amazon Athena have the following features and benefits:
Amazon S3 is a service that provides scalable, durable, and secure object storage for any type of data.
Amazon S3 can store csv and JSON files, as well as other formats, and can handle large volumes of data with high availability and performance. Amazon S3 also integrates with other AWS services, such as Amazon Athena, for further processing and analysis of the data.
Amazon Athena is a service that allows querying data stored in Amazon S3 using standard SQL.
Amazon Athena can query over semi-structured data, such as JSON, as well as structured data, such as csv, without requiring any loading or transformation. Amazon Athena is serverless, meaning that there is no infrastructure to manage and users only pay for the queries they run. Amazon Athena also supports the use of AWS Glue Data Catalog, which is a centralized metadata repository that can store and manage the schema and partition information of the data in Amazon S3.
Using Amazon S3 and Amazon Athena, the company can achieve the following high priorities:
Solution simplicity: Amazon S3 and Amazon Athena are easy to use and require minimal configuration and maintenance. The company can simply upload the csv and JSON files to Amazon S3 and use Amazon Athena to query them using SQL. The company does not need to worry about provisioning, scaling, or managing any servers or clusters.
Fast development time: Amazon S3 and Amazon Athena can enable the company to quickly access and analyze the data without any data preparation or loading. The company can use the existing workforce of SQL experts to write and run queries on Amazon Athena and get results in seconds or minutes.
Low cost: Amazon S3 and Amazon Athena are cost-effective and offer pay-as-you-go pricing models.
Amazon S3 charges based on the amount of storage used and the number of requests made. Amazon Athena charges based on the amount of data scanned by the queries. The company can also reduce the costs by using compression, encryption, and partitioning techniques to optimize the data storage and query performance.
High flexibility: Amazon S3 and Amazon Athena are flexible and can support various data types, formats, and sources. The company can store and query any type of data in Amazon S3, such as csv, JSON, Parquet, ORC, etc. The company can also query data from multiple sources in Amazon S3, such as data lakes, data warehouses, log files, etc.
The other options are not as suitable as option A for the company's requirements for the following reasons:
Option B: Amazon Redshift and AWS Glue are technologies that can be used for data warehousing and data integration, but they are not ideal for a temporary ad hoc solution. Amazon Redshift is a service that provides a fully managed, petabyte-scale data warehouse that can run complex analytical queries using SQL. AWS Glue is a service that provides a fully managed extract, transform, and load (ETL) service that can prepare and load data for analytics. However, using Amazon Redshift and AWS Glue would require more effort and cost than using Amazon S3 and Amazon Athena. The company would need to load the data from Amazon S3 to Amazon Redshift using AWS Glue, which can take time and incur additional charges. The company would also need to manage the capacity and performance of the Amazon Redshift cluster, which can be complex and expensive.
Option C: Amazon DynamoDB and DynamoDB Accelerator (DAX) are technologies that can be used for fast and scalable NoSQL database and caching, but they are not suitable for the company's data storage and query needs. Amazon DynamoDB is a service that provides a fully managed, key-value and document database that can deliver single-digit millisecond performance at any scale. DynamoDB Accelerator (DAX) is a service that provides a fully managed, in-memory cache for DynamoDB that can improve the read performance by up to 10 times. However, using Amazon DynamoDB and DAX would not allow the company to continue to use SQL as a query language, as Amazon DynamoDB does not support SQL. The company would need to use the DynamoDB API or the AWS SDKs to access and query the data, which can require more coding and learning effort. The company would also need to transform the csv and JSON files into DynamoDB items, which can involve additional processing and complexity.
Option D: Amazon RDS and Amazon ES are technologies that can be used for relational database and search and analytics, but they are not optimal for the company's data storage and query scenario.
Amazon RDS is a service that provides a fully managed, relational database that supports various database engines, such as MySQL, PostgreSQL, Oracle, etc. Amazon ES is a service that provides a fully managed, Elasticsearch cluster, which is mainly used for search and analytics purposes. However, using Amazon RDS and Amazon ES would not be as simple and cost-effective as using Amazon S3 and Amazon Athena. The company would need to load the data from Amazon S3 to Amazon RDS, which can take time and incur additional charges. The company would also need to manage the capacity and performance of the Amazon RDS and Amazon ES clusters, which can be complex and expensive.
Moreover, Amazon RDS and Amazon ES are not designed to handle semi-structured data, such as JSON, as well as Amazon S3 and Amazon Athena.
References:
Amazon S3
Amazon Athena
Amazon Redshift
AWS Glue
Amazon DynamoDB
[DynamoDB Accelerator (DAX)]
[Amazon RDS]
[Amazon ES]
NEW QUESTION # 33
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.
.... Dh Amazon SageMaker algorithm will meet this requirement?
- A. Object Detection - TensorFlow
- B. Image Classification - TensorFlow
- C. Semantic segmentation - MXNet
- D. XGBoost
Answer: B
Explanation:
The best Amazon SageMaker algorithm for this task is Image Classification - TensorFlow. This algorithm is a supervised learning algorithm that supports transfer learning with many pretrained models from the TensorFlow Hub. Transfer learning allows the company to fine-tune one of the available pretrained models on their own dataset, even if a large amount of image data is not available. The image classification algorithm takes an image as input and outputs a probability for each provided class label. The company can choose from a variety of models, such as MobileNet, ResNet, or Inception, depending on their accuracy and speed requirements. The algorithm also supports distributed training, data augmentation, and hyperparameter tuning.
References:
Image Classification - TensorFlow - Amazon SageMaker
Amazon SageMaker Provides New Built-in TensorFlow Image Classification Algorithm Image Classification with ResNet :: Amazon SageMaker Workshop Image classification on Amazon SageMaker | by Julien Simon - Medium
NEW QUESTION # 34
A data scientist needs to identify fraudulent user accounts for a company's ecommerce platform. The company wants the ability to determine if a newly created account is associated with a previously known fraudulent user.
The data scientist is using AWS Glue to cleanse the company's application logs during ingestion.
Which strategy will allow the data scientist to identify fraudulent accounts?
- A. Execute the built-in FindDuplicates Amazon Athena query.
- B. Create an AWS Glue crawler to infer duplicate accounts in the source data.
- C. Search for duplicate accounts in the AWS Glue Data Catalog.
- D. Create a FindMatches machine learning transform in AWS Glue.
Answer: D
Explanation:
Explanation/Reference: https://docs.aws.amazon.com/glue/latest/dg/machine-learning.html
NEW QUESTION # 35
A media company with a very large archive of unlabeled images, text, audio, and video footage wishes to index its assets to allow rapid identification of relevant content by the Research team. The company wants to use machine learning to accelerate the efforts of its in-house researchers who have limited machine learning expertise.
Which is the FASTEST route to index the assets?
- A. Use Amazon Rekognition, Amazon Comprehend, and Amazon Transcribe to tag data into distinct categories/classes.
- B. Use Amazon Transcribe to convert speech to text. Use the Amazon SageMaker Neural Topic Model (NTM) and Object Detection algorithms to tag data into distinct categories/classes.
- C. Create a set of Amazon Mechanical Turk Human Intelligence Tasks to label all footage.
- D. Use the AWS Deep Learning AMI and Amazon EC2 GPU instances to create custom models for audio transcription and topic modeling, and use object detection to tag data into distinct categories/classes.
Answer: A
Explanation:
Amazon Rekognition, Amazon Comprehend, and Amazon Transcribe are AWS machine learning services that can analyze and extract metadata from images, text, audio, and video content. These services are easy to use, scalable, and do not require any machine learning expertise. They can help the media company to quickly index its assets and enable rapid identification of relevant content by the research team. Using these services is the fastest route to index the assets, compared to the other options that involve human intervention, custom model development, or additional steps. References:
AWS Media Intelligence Solutions
AWS Machine Learning Services
The Best Services For Running Machine Learning Models On AWS
NEW QUESTION # 36
A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City. One of the random variables is discrete, and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.
Which prior probability distribution should the ML Specialist use for this variable?
- A. Normal distribution
- B. Uniform distribution
- C. Poisson distribution ,
- D. Binomial distribution
Answer: D
NEW QUESTION # 37
A company's Machine Learning Specialist needs to improve the training speed of a time-series forecasting model using TensorFlow. The training is currently implemented on a single-GPU machine and takes approximately 23 hours to complete. The training needs to be run daily.
The model accuracy js acceptable, but the company anticipates a continuous increase in the size of the training data and a need to update the model on an hourly, rather than a daily, basis. The company also wants to minimize coding effort and infrastructure changes What should the Machine Learning Specialist do to the training solution to allow it to scale for future demand?
- A. Change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Parallelize the training to as many machines as needed to achieve the business goals.
- B. Move the training to Amazon EMR and distribute the workload to as many machines as needed to achieve the business goals.
- C. Do not change the TensorFlow code. Change the machine to one with a more powerful GPU to speed up the training.
- D. Switch to using a built-in AWS SageMaker DeepAR model. Parallelize the training to as many machines as needed to achieve the business goals.
Answer: A
Explanation:
To improve the training speed of a time-series forecasting model using TensorFlow, the Machine Learning Specialist should change the TensorFlow code to implement a Horovod distributed framework supported by Amazon SageMaker. Horovod is a free and open-source software framework for distributed deep learning training using TensorFlow, Keras, PyTorch, and Apache MXNet1. Horovod can scale up to hundreds of GPUs with upwards of 90% scaling efficiency2. Horovod is easy to use, as it requires only a few lines of Python code to modify an existing training script2. Horovod is also portable, as it runs the same for TensorFlow, Keras, PyTorch, and MXNet; on premise, in the cloud, and on Apache Spark2.
Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly3. Amazon SageMaker supports Horovod as a built-in distributed training framework, which means that the Machine Learning Specialist does not need to install or configure Horovod separately4. Amazon SageMaker also provides a number of features and tools to simplify and optimize the distributed training process, such as automatic scaling, debugging, profiling, and monitoring4. By using Amazon SageMaker, the Machine Learning Specialist can parallelize the training to as many machines as needed to achieve the business goals, while minimizing coding effort and infrastructure changes.
References:
1: Horovod (machine learning) - Wikipedia
2: Home - Horovod
3: Amazon SageMaker - Machine Learning Service - AWS
4: Use Horovod with Amazon SageMaker - Amazon SageMaker
NEW QUESTION # 38
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. 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.
- B. 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.
- C. 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.
- D. 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.
Answer: B
Explanation:
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
NEW QUESTION # 39
A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.
Based on this information, which model would have the HIGHEST accuracy?
- A. Support vector machine (SVM) with non-linear kernel
- B. Single perceptron with tanh activation function
- C. Logistic regression
- D. Long short-term memory (LSTM) model with scaled exponential linear unit (SELU)
Answer: C
Explanation:
Explanation/Reference: https://machinelearningmastery.com/logistic-regression-for-machine-learning/
NEW QUESTION # 40
A Data Scientist is developing a binary classifier to predict whether a patient has a particular disease on a series of test results. The Data Scientist has data on 400 patients randomly selected from the population. The disease is seen in 3% of the population.
Which cross-validation strategy should the Data Scientist adopt?
- A. An 80/20 stratified split between training and validation
- B. A k-fold cross-validation strategy with k=5
- C. A stratified k-fold cross-validation strategy with k=5
- D. A k-fold cross-validation strategy with k=5 and 3 repeats
Answer: C
NEW QUESTION # 41
A company that manufactures mobile devices wants to determine and calibrate the appropriate sales price for its devices. The company is collecting the relevant data and is determining data features that it can use to train machine learning (ML) models. There are more than 1,000 features, and the company wants to determine the primary features that contribute to the sales price.
Which techniques should the company use for feature selection? (Choose three.)
- A. Data binning
- B. Correlation plot with heat maps
- C. Data scaling with standardization and normalization
- D. Univariate selection
- E. Data augmentation
- F. Feature importance with a tree-based classifier
Answer: B,D,F
Explanation:
Feature selection is the process of selecting a subset of extracted features that are relevant and contribute to minimizing the error rate of a trained model. Some techniques for feature selection are:
Correlation plot with heat maps: This technique visualizes the correlation between features using a color-coded matrix. Features that are highly correlated with each other or with the target variable can be identified and removed to reduce redundancy and noise.
Univariate selection: This technique evaluates each feature individually based on a statistical test, such as chi-square, ANOVA, or mutual information, and selects the features that have the highest scores or p-values. This technique is simple and fast, but it does not consider the interactions between features.
Feature importance with a tree-based classifier: This technique uses a tree-based classifier, such as random forest or gradient boosting, to rank the features based on their importance in splitting the nodes. Features that have low importance scores can be dropped from the model. This technique can capture the non-linear relationships and interactions between features.
The other options are not techniques for feature selection, but rather for feature engineering, which is the process of creating, transforming, or extracting features from the original data. Feature engineering can improve the performance and interpretability of the model, but it does not reduce the number of features.
Data scaling with standardization and normalization: This technique transforms the features to have a common scale, such as zero mean and unit variance, or a range between 0 and 1. This technique can help some algorithms, such as k-means or logistic regression, to converge faster and avoid numerical instability, but it does not change the number of features.
Data binning: This technique groups the continuous features into discrete bins or categories based on some criteria, such as equal width, equal frequency, or clustering. This technique can reduce the noise and outliers in the data, and also create ordinal or nominal features that can be used for some algorithms, such as decision trees or naive Bayes, but it does not reduce the number of features.
Data augmentation: This technique generates new data from the existing data by applying some transformations, such as rotation, flipping, cropping, or noise addition. This technique can increase the size and diversity of the data, and help prevent overfitting, but it does not reduce the number of features.
References:
Feature engineering - Machine Learning Lens
Amazon SageMaker Autopilot now provides feature selection and the ability to change data types while creating an AutoML experiment Feature Selection in Machine Learning | Baeldung on Computer Science Feature Selection in Machine Learning: An easy Introduction
NEW QUESTION # 42
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