MLS-C01 EXAM EXERCISE, RELIABLE MLS-C01 EXAM BOOTCAMP

MLS-C01 Exam Exercise, Reliable MLS-C01 Exam Bootcamp

MLS-C01 Exam Exercise, Reliable MLS-C01 Exam Bootcamp

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Achieving the AWS Certified Machine Learning - Specialty certification through the Amazon MLS-C01 Exam can demonstrate to employers and clients that you have the skills and knowledge needed to design and implement machine learning solutions on AWS. AWS Certified Machine Learning - Specialty certification can help individuals advance their careers as data scientists, machine learning engineers, and solution architects.

How to Prepare For AWS Certified Machine Learning - Specialty

Preparation Guide for AWS Certified Machine Learning - Specialty

Introduction for AWS Certified Machine Learning - Specialty

The AWS Certified Machine Learning - Specialty (MLS-C01) examination is intended for individuals who perform a development or data science role. This exam validates an examinee's ability to build, train, tune, and deploy learning (ML) models using the AWS Cloud.

It validates an examinee's ability to design, implement, deploy, and maintain ML solutions for given business problems. It will validate the candidate's ability to:

  • Identify appropriate AWS services to implement ML solutions.
  • Select and justify the appropriate ML approach for a given business problem.
  • Design and implement scalable, cost-optimized, reliable, and secure ML solutions

The AWS Certified Machine Learning - Specialty certification is intended for individuals who perform a development or data science role. It validates a candidate's ability to design, implement, deploy, and maintain machine learning (ML) solutions for given business problems.

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Reliable MLS-C01 Exam Bootcamp - MLS-C01 Hot Questions

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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.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q234-Q239):

NEW QUESTION # 234
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 a deep learning OCR model from the AWS Marketplace. Use the NER deep learning model to extract entities.
  • B. 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.
  • C. 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.
  • 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.

Answer: B

Explanation:
The best solution for text extraction and entity detection with the least amount of effort is to use Amazon Textract and Amazon Comprehend. These services are:
* Amazon Textract for text extraction from receipt images. Amazon Textract is a machine learning service that can automatically extract text and data from scanned documents. It can handle different structures and formats of documents, such as PDF, TIFF, PNG, and JPEG, without any preprocessing steps. It can also extract key-value pairs and tables from documents1
* Amazon Comprehend for entity detection and custom entity detection. Amazon Comprehend is a natural language processing service that can identify entities, such as dates, locations, and notes, from unstructured text. It can also detect custom entities, such as receipt numbers, by using a custom entity recognizer that can be trained with a small amount of labeled data2 The other options are not suitable because they either require more effort for text extraction, entity detection, or custom entity detection. For example:
* Option A uses the Amazon SageMaker BlazingText algorithm to train on the text for entities and custom entities. BlazingText is a supervised learning algorithm that can perform text classification and word2vec. It requires users to provide a large amount of labeled data, preprocess the data into a specific format, and tune the hyperparameters of the model3
* Option B uses a deep learning OCR model from the AWS Marketplace and a NER deep learning model for text extraction and entity detection. These models are pre-trained and may not be suitable for the specific use case of receipt processing. They also require users to deploy and manage the models on Amazon SageMaker or Amazon EC2 instances4
* Option D uses a deep learning OCR model from the AWS Marketplace for text extraction. This model has the same drawbacks as option B. It also requires users to integrate the model output with Amazon Comprehend for entity detection and custom entity detection.
1: Amazon Textract - Extract text and data from documents
2: Amazon Comprehend - Natural Language Processing (NLP) and Machine Learning (ML)
3: BlazingText - Amazon SageMaker
4: AWS Marketplace: OCR


NEW QUESTION # 235
An aircraft engine manufacturing company is measuring 200 performance metrics in a time-series. Engineers want to detect critical manufacturing defects in near-real time during testing. All of the data needs to be stored for offline analysis.
What approach would be the MOST effective to perform near-real time defect detection?

  • A. Use AWS IoT Analytics for ingestion, storage, and further analysis. Use Jupyter notebooks from withinAWS IoT Analytics to carry out analysis for anomalies.
  • B. Use Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest(RCF) to perform anomaly detection. Use Kinesis Data Firehose to store data in Amazon S3 for furtheranalysis.
  • C. Use Amazon S3 for ingestion, storage, and further analysis. Use an Amazon EMR cluster to carry outApache Spark ML k-means clustering to determine anomalies.
  • D. Use Amazon S3 for ingestion, storage, and further analysis. Use the Amazon SageMaker Random CutForest (RCF) algorithm to determine anomalies.

Answer: B

Explanation:
* The company wants to perform near-real time defect detection on a time-series of 200 performance metrics, and store all the data for offline analysis. The best approach for this scenario is to use Amazon Kinesis Data Firehose for ingestion and Amazon Kinesis Data Analytics Random Cut Forest (RCF) to perform anomaly detection. Use Kinesis Data Firehose to store data in Amazon S3 for further analysis.
* Amazon Kinesis Data Firehose is a service that can capture, transform, and deliver streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon OpenSearch Service, and Splunk. Kinesis Data Firehose can handle any amount and frequency of data, and automatically scale to match the throughput. Kinesis Data Firehose can also compress, encrypt, and batch the data before delivering it to the destination, reducing the storage cost and enhancing the security.
* Amazon Kinesis Data Analytics is a service that can analyze streaming data in real time using SQL or Apache Flink applications. Kinesis Data Analytics can use built-in functions and algorithms to perform various analytics tasks, such as aggregations, joins, filters, windows, and anomaly detection. One of the built-in algorithms that Kinesis Data Analytics supports is Random Cut Forest (RCF), which is a supervised learning algorithm for forecasting scalar time series using recurrent neural networks. RCF can detect anomalies in streaming data by assigning an anomaly score to each data point, based on how distant it is from the rest of the data. RCF can handle multiple related time series, such as the performance metrics of the aircraft engine, and learn a global model that captures the common patterns and trends across the time series.
* Therefore, the company can use the following architecture to build the near-real time defect detection solution:
* Use Amazon Kinesis Data Firehose for ingestion: The company can use Kinesis Data Firehose to capture the streaming data from the aircraft engine testing, and deliver it to two destinations:
Amazon S3 and Amazon Kinesis Data Analytics. The company can configure the Kinesis Data Firehose delivery stream to specify the source, the buffer size and interval, the compression and encryption options, the error handling and retry logic, and the destination details.
* Use Amazon Kinesis Data Analytics Random Cut Forest (RCF) to perform anomaly detection:
The company can use Kinesis Data Analytics to create a SQL application that can read the streaming data from the Kinesis Data Firehose delivery stream, and apply the RCF algorithm to detect anomalies. The company can use the RANDOM_CUT_FOREST or RANDOM_CUT_FOREST_WITH_EXPLANATION functions to compute the anomaly scores and attributions for each data point, and use the WHERE clause to filter out the normal data points. The company can also use the CURSOR function to specify the input stream, and the PUMP function to write the output stream to another destination, such as Amazon Kinesis Data Streams or AWS Lambda.
* Use Kinesis Data Firehose to store data in Amazon S3 for further analysis: The company can use Kinesis Data Firehose to store the raw and processed data in Amazon S3 for offline analysis. The company can use the S3 destination of the Kinesis Data Firehose delivery stream to store the raw data, and use another Kinesis Data Firehose delivery stream to store the output of the Kinesis Data Analytics application. The company can also use AWS Glue or Amazon Athena to catalog, query, and analyze the data in Amazon S3.
What Is Amazon Kinesis Data Firehose?
What Is Amazon Kinesis Data Analytics for SQL Applications?
DeepAR Forecasting Algorithm - Amazon SageMaker


NEW QUESTION # 236
A beauty supply store wants to understand some characteristics of visitors to the store. The store has security video recordings from the past several years. The store wants to generate a report of hourly visitors from the recordings. The report should group visitors by hair style and hair color.
Which solution will meet these requirements with the LEAST amount of effort?

  • A. Use an object detection algorithm to identify a visitor's hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.
  • B. Use a semantic segmentation algorithm to identify a visitor's hair in video frames. Pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color.
  • C. Use an object detection algorithm to identify a visitor's hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair color.
  • D. Use a semantic segmentation algorithm to identify a visitor's hair in video frames. Pass the identified hair to an XGBoost algorithm to determine hair style and hair.

Answer: B

Explanation:
The solution that will meet the requirements with the least amount of effort is to use a semantic segmentation algorithm to identify a visitor's hair in video frames, and pass the identified hair to an ResNet-50 algorithm to determine hair style and hair color. This solution can leverage the existing Amazon SageMaker algorithms and frameworks to perform the tasks of hair segmentation and classification.
Semantic segmentation is a computer vision technique that assigns a class label to every pixel in an image, such that pixels with the same label share certain characteristics. Semantic segmentation can be used to identify and isolate different objects or regions in an image, such as a visitor's hair in a video frame. Amazon SageMaker provides a built-in semantic segmentation algorithm that can train and deploy models for semantic segmentation tasks. The algorithm supports three state-of-the-art network architectures: Fully Convolutional Network (FCN), Pyramid Scene Parsing Network (PSP), and DeepLab v3. The algorithm can also use pre- trained or randomly initialized ResNet-50 or ResNet-101 as the backbone network. The algorithm can be trained using P2/P3 type Amazon EC2 instances in single machine configurations1.
ResNet-50 is a convolutional neural network that is 50 layers deep and can classify images into 1000 object categories. ResNet-50 is trained on more than a million images from the ImageNet database and can achieve high accuracy on various image recognition tasks. ResNet-50 can be used to determine hair style and hair color from the segmented hair regions in the video frames. Amazon SageMaker provides a built-in image classification algorithm that can use ResNet-50 as the network architecture. The algorithm can also perform transfer learning by fine-tuning the pre-trained ResNet-50 model with new data. The algorithm can be trained using P2/P3 type Amazon EC2 instances in single or multiple machine configurations2.
The other options are either less effective or more complex to implement. Using an object detection algorithm to identify a visitor's hair in video frames would not segment the hair at the pixel level, but only draw bounding boxes around the hair regions. This could result in inaccurate or incomplete hair segmentation, especially if the hair is occluded or has irregular shapes. Using an XGBoost algorithm to determine hair style and hair color would require transforming the segmented hair images into numerical features, which could lose some information or introduce noise. XGBoost is also not designed for image classification tasks, and may not achieve high accuracy or performance.
1: Semantic Segmentation Algorithm - Amazon SageMaker
2: Image Classification Algorithm - Amazon SageMaker


NEW QUESTION # 237
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: B

Explanation:
Mini-batch gradient descent is a variant of gradient descent that updates the model parameters using a subset of the training data (called a mini-batch) at each iteration. The learning rate is a hyperparameter that controls how much the model parameters change in response to the gradient. If the learning rate is very high, the model parameters may overshoot the optimal values and oscillate around the minimum of the cost function. This can cause the training accuracy to fluctuate and prevent the model from converging to a stable solution. To avoid this issue, the learning rate should be chosen carefully, such as by using a learning rate decay schedule or an adaptive learning rate algorithm1. Alternatively, the batch size can be increased to reduce the variance of the gradient estimates2. However, the batch size should not be too big, as this can slow down the training process and reduce the generalization ability of the model3. Dataset shuffling and class distribution are not likely to cause oscillations in training accuracy, as they do not affect the gradient updates directly. Dataset shuffling can help avoid getting stuck in local minima and improve the convergence speed of mini-batch gradient descent4. Class distribution can affect the performance and fairness of the model, especially if the dataset is imbalanced, but it does not necessarily cause fluctuations in training accuracy.


NEW QUESTION # 238
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs What does the Specialist need to do1?

  • A. Build the Docker container to be NVIDIA-Docker compatible
  • B. Organize the Docker container's file structure to execute on GPU instances.
  • C. Bundle the NVIDIA drivers with the Docker image
  • D. Set the GPU flag in the Amazon SageMaker Create TrainingJob request body

Answer: C


NEW QUESTION # 239
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