PASS GUARANTEED AMAZON - ACCURATE MLS-C01 - RELIABLE STUDY AWS CERTIFIED MACHINE LEARNING - SPECIALTY QUESTIONS

Pass Guaranteed Amazon - Accurate MLS-C01 - Reliable Study AWS Certified Machine Learning - Specialty Questions

Pass Guaranteed Amazon - Accurate MLS-C01 - Reliable Study AWS Certified Machine Learning - Specialty Questions

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The Amazon MLS-C01 Exam is intended for professionals who are already working in the field of machine learning or those who are planning to start a career in this field. To take the exam, candidates should have a good understanding of programming languages such as Python or R, as well as experience with AWS services like S3, EC2, and SageMaker.

The Amazon MLS-C01 exam is intended for those who have a deep understanding of machine learning algorithms and frameworks, as well as experience with AWS services such as Amazon SageMaker, Amazon S3, Amazon EC2, and Amazon EMR. Candidates must also have experience with programming languages such as Python and R, as well as experience with data preprocessing, feature engineering, and model evaluation.

Amazon AWS-Certified-Machine-Learning-Specialty (AWS Certified Machine Learning - Specialty) certification exam is designed for professionals who want to demonstrate their expertise in machine learning on the Amazon Web Services (AWS) platform. AWS Certified Machine Learning - Specialty certification exam validates the candidate's ability to design, implement, deploy, and maintain machine learning solutions using AWS services.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q166-Q171):

NEW QUESTION # 166
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. Natural Language Toolkit (NLTK) stemming and stop word removal
  • B. Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers
  • C. Amazon SageMaker BlazingText allow mode
  • D. Amazon Comprehend syntax analysts and entity detection

Answer: B

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 # 167
A company is running a machine learning prediction service that generates 100 TB of predictions every day A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team.
Which solution requires the LEAST coding effort?

  • A. Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.
  • B. Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team
  • C. Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Give the Business team read-only access to S3
  • D. Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team

Answer: D

Explanation:
Explanation
A precision-recall curve is a plot that shows the trade-off between the precision and recall of a binary classifier as the decision threshold is varied. It is a useful tool for evaluating and comparing the performance of different models. To generate a precision-recall curve, the following steps are needed:
Calculate the precision and recall values for different threshold values using the predictions and the true labels of the data.
Plot the precision values on the y-axis and the recall values on the x-axis for each threshold value.
Optionally, calculate the area under the curve (AUC) as a summary metric of the model performance.
Among the four options, option C requires the least coding effort to generate and share a visualization of the daily precision-recall curve from the predictions. This option involves the following steps:
Run a daily Amazon EMR workflow to generate precision-recall data: Amazon EMR is a service that allows running big data frameworks, such as Apache Spark, on a managed cluster of EC2 instances.
Amazon EMR can handle large-scale data processing and analysis, such as calculating the precision and recall values for different threshold values from 100 TB of predictions. Amazon EMR supports various languages, such as Python, Scala, and R, for writing the code to perform the calculations. Amazon EMR also supports scheduling workflows using Apache Airflow or AWS Step Functions, which can automate the daily execution of the code.
Save the results in Amazon S3: Amazon S3 is a service that provides scalable, durable, and secure object storage. Amazon S3 can store the precision-recall data generated by Amazon EMR in a cost-effective and accessible way. Amazon S3 supports various data formats, such as CSV, JSON, or Parquet, for storing the data. Amazon S3 also integrates with other AWS services, such as Amazon QuickSight, for further processing and visualization of the data.
Visualize the arrays in Amazon QuickSight: Amazon QuickSight is a service that provides fast, easy-to-use, and interactive business intelligence and data visualization. Amazon QuickSight can connect to Amazon S3 as a data source and import the precision-recall data into a dataset. Amazon QuickSight can then create a line chart to plot the precision-recall curve from the dataset. Amazon QuickSight also supports calculating the AUC and adding it as an annotation to the chart.
Publish them in a dashboard shared with the Business team: Amazon QuickSight allows creating and publishing dashboards that contain one or more visualizations from the datasets. Amazon QuickSight also allows sharing the dashboards with other users or groups within the same AWS account or across different AWS accounts. The Business team can access the dashboard with read-only permissions and view the daily precision-recall curve from the predictions.
The other options require more coding effort than option C for the following reasons:
Option A: This option requires writing code to plot the precision-recall curve from the data stored in Amazon S3, as well as creating a mechanism to share the plot with the Business team. This can involve using additional libraries or tools, such as matplotlib, seaborn, or plotly, for creating the plot, and using email, web, or cloud services, such as AWS Lambda or Amazon SNS, for sharing the plot.
Option B: This option requires transforming the predictions into a format that Amazon QuickSight can recognize and import as a data source, such as CSV, JSON, or Parquet. This can involve writing code to process and convert the predictions, as well as uploading them to a storage service, such as Amazon S3 or Amazon Redshift, that Amazon QuickSight can connect to.
Option D: This option requires writing code to generate precision-recall data in Amazon ES, as well as creating a dashboard to visualize the data. Amazon ES is a service that provides a fully managed Elasticsearch cluster, which is mainly used for search and analytics purposes. Amazon ES is not designed for generating precision-recall data, and it requires using a specific data format, such as JSON, for storing the data. Amazon ES also requires using a tool, such as Kibana, for creating and sharing the dashboard, which can involve additional configuration and customization steps.
References:
Precision-Recall
What Is Amazon EMR?
What Is Amazon S3?
[What Is Amazon QuickSight?]
[What Is Amazon Elasticsearch Service?]


NEW QUESTION # 168
The chief editor for a product catalog wants the research and development team to build a machine learning system that can be used to detect whether or not individuals in a collection of images are wearing the company's retail brand. The team has a set of training data.
Which machine learning algorithm should the researchers use that BEST meets their requirements?

  • A. K-means
  • B. Convolutional neural network (CNN)
  • C. Recurrent neural network (RNN)
  • D. Latent Dirichlet Allocation (LDA)

Answer: B

Explanation:
The problem of detecting whether or not individuals in a collection of images are wearing the company's retail brand is an example of image recognition, which is a type of machine learning task that identifies and classifies objects in an image. Convolutional neural networks (CNNs) are a type of machine learning algorithm that are well-suited for image recognition, as they can learn to extract features from images and handle variations in size, shape, color, and orientation of the objects. CNNs consist of multiple layers that perform convolution, pooling, and activation operations on the input images, resulting in a high-level representation that can be used for classification or detection. Therefore, option D is the best choice for the machine learning algorithm that meets the requirements of the chief editor.
Option A is incorrect because latent Dirichlet allocation (LDA) is a type of machine learning algorithm that is used for topic modeling, which is a task that discovers the hidden themes or topics in a collection of text documents. LDA is not suitable for image recognition, as it does not preserve the spatial information of the pixels. Option B is incorrect because recurrent neural networks (RNNs) are a type of machine learning algorithm that are used for sequential data, such as text, speech, or time series. RNNs can learn from the temporal dependencies and patterns in the input data, and generate outputs that depend on the previous states.
RNNs are not suitable for image recognition, as they do not capture the spatial dependencies and patterns in the input images. Option C is incorrect because k-means is a type of machine learning algorithm that is used for clustering, which is a task that groups similar data points together based on their features. K-means is not suitable for image recognition, as it does not perform classification or detection of the objects in the images.
References:
* Image Recognition Software - ML Image & Video Analysis - Amazon ...
* Image classification and object detection using Amazon Rekognition ...
* AWS Amazon Rekognition - Deep Learning Face and Image Recognition ...
* GitHub - awslabs/aws-ai-solution-kit: Machine Learning APIs for common ...
* Meet iNaturalist, an AWS-powered nature app that helps you identify ...


NEW QUESTION # 169
A library is developing an automatic book-borrowing system that uses Amazon Rekognition. Images of library members' faces are stored in an Amazon S3 bucket. When members borrow books, the Amazon Rekognition CompareFaces API operation compares real faces against the stored faces in Amazon S3.
The library needs to improve security by making sure that images are encrypted at rest. Also, when the images are used with Amazon Rekognition. they need to be encrypted in transit. The library also must ensure that the images are not used to improve Amazon Rekognition as a service.
How should a machine learning specialist architect the solution to satisfy these requirements?

  • A. Switch to using an Amazon Rekognition collection to store the images. Use the IndexFaces and SearchFacesByImage API operations instead of the CompareFaces API operation.
  • B. Enable server-side encryption on the S3 bucket. Submit an AWS Support ticket to opt out of allowing images to be used for improving the service, and follow the process provided by AWS Support.
  • C. Switch to using the AWS GovCloud (US) Region for Amazon S3 to store images and for Amazon Rekognition to compare faces. Set up a VPN connection and only call the Amazon Rekognition API operations through the VPN.
  • D. Enable client-side encryption on the S3 bucket. Set up a VPN connection and only call the Amazon Rekognition API operations through the VPN.

Answer: B

Explanation:
Explanation
The best solution for encrypting images at rest and in transit, and opting out of data usage for service improvement, is to use the following steps:
Enable server-side encryption on the S3 bucket. This will encrypt the images stored in the bucket using AWS Key Management Service (AWS KMS) customer master keys (CMKs). This will protect the data at rest from unauthorized access1 Submit an AWS Support ticket to opt out of allowing images to be used for improving the service, and follow the process provided by AWS Support. This will prevent AWS from storing or using the images processed by Amazon Rekognition for service development or enhancement purposes. This will protect the data privacy and ownership2 Use HTTPS to call the Amazon Rekognition CompareFaces API operation. This will encrypt the data in transit between the client and the server using SSL/TLS protocols. This will protect the data from interception or tampering3 The other options are incorrect because they either do not encrypt the images at rest or in transit, or do not opt out of data usage for service improvement. For example:
Option B switches to using an Amazon Rekognition collection to store the images. A collection is a container for storing face vectors that are calculated by Amazon Rekognition. It does not encrypt the images at rest or in transit, and it does not opt out of data usage for service improvement. It also requires changing the API operations from CompareFaces to IndexFaces and SearchFacesByImage, which may not have the same functionality or performance4 Option C switches to using the AWS GovCloud (US) Region for Amazon S3 and Amazon Rekognition.
The AWS GovCloud (US) Region is an isolated AWS Region designed to host sensitive data and regulated workloads in the cloud. It does not automatically encrypt the images at rest or in transit, and it does not opt out of data usage for service improvement. It also requires migrating the data and the application to a different Region, which may incur additional costs and complexity5 Option D enables client-side encryption on the S3 bucket. This means that the client is responsible for encrypting and decrypting the images before uploading or downloading them from the bucket. This adds extra overhead and complexity to the client application, and it does not encrypt the data in transit when calling the Amazon Rekognition API. It also does not opt out of data usage for service improvement.
References:
1: Protecting Data Using Server-Side Encryption with AWS KMS-Managed Keys (SSE-KMS) - Amazon Simple Storage Service
2: Opting Out of Content Storage and Use for Service Improvements - Amazon Rekognition
3: HTTPS - Wikipedia
4: Working with Stored Faces - Amazon Rekognition
5: AWS GovCloud (US) - Amazon Web Services
6: Protecting Data Using Client-Side Encryption - Amazon Simple Storage Service


NEW QUESTION # 170
A data scientist has a dataset of machine part images stored in Amazon Elastic File System (Amazon EFS).
The data scientist needs to use Amazon SageMaker to create and train an image classification machine learning model based on this dataset. Because of budget and time constraints, management wants the data scientist to create and train a model with the least number of steps and integration work required.
How should the data scientist meet these requirements?

  • A. Mount the EFS file system to a SageMaker notebook and run a script that copies the data to an Amazon FSx for Lustre file system. Run the SageMaker training job with the FSx for Lustre file system as the data source.
  • B. Launch a transient Amazon EMR cluster. Configure steps to mount the EFS file system and copy the data to an Amazon S3 bucket by using S3DistCp. Run the SageMaker training job with Amazon S3 as the data source.
  • C. Mount the EFS file system to an Amazon EC2 instance and use the AWS CLI to copy the data to an Amazon S3 bucket. Run the SageMaker training job with Amazon S3 as the data source.
  • D. Run a SageMaker training job with an EFS file system as the data source.

Answer: D

Explanation:
Explanation
The simplest and fastest way to use the EFS dataset for SageMaker training is to run a SageMaker training job with an EFS file system as the data source. This option does not require any data copying or additional integration steps. SageMaker supports EFS as a data source for training jobs, and it can mount the EFS file system to the training container using the FileSystemConfig parameter. This way, the training script can access the data files as if they were on the local disk of the training instance. References:
Access Training Data - Amazon SageMaker
Mount an EFS file system to an Amazon SageMaker notebook (with lifecycle configurations) | AWS Machine Learning Blog


NEW QUESTION # 171
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