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What Topics Are Covered in AWS Machine Learning - Specialty Certification Exam?

The certification exam for the AWS Machine Learning – Specialty certification tests the candidates' ability to select the best machine learning strategy to improve the business processes. Also, they will be able to identify the best AWS service needed to implement different machine learning solutions. Besides, candidates will be able to design and put into practice reliable, scalable, and cost-optimized machine learning solutions. The AWS MLS-C01 exam, in particular, focuses on four domains. They are the following:

  • Modeling;
  • Exploratory Data Analysis;
  • Data engineering;
  • Machine Learning Implementation and Operations.

The first topic handles data engineering and has 3 sections. The first one handles the creation of data repositories for efficient machine learning strategies. Also, candidates will learn how to effectively identify and implement solutions related to data-ingestion. The third sub-domain included in this section focuses on the implementation and identification of different data-transformation solutions.

The second tested area concentrates on exploratory data analysis. When they prepare for this topic, candidates will learn how to sanitize and prepare data for modeling. Also, they will learn how to make a performance when it comes to feature engineering. Finally, candidates will learn how to visualize data and analyze different parameters for machine learning.

Within the modeling section, candidates will learn how to frame different business problems related to machine learning issues. Besides, they will find how to select the right models for different machine learning problems. In this section, candidates will also learn how to train effectively for machine learning models. Another essential concept that candidates will discover in this part is related to hyperparameter optimization performance. Last but not least, applicants will understand how to correctly evaluate machine learning models.

The final domain concentrates on machine learning operations and implementation. This segment focuses on helping candidates develop advanced abilities in building machine learning solutions to achieve the highest performance and fault tolerance. These solutions will help them ensure availability, resilience, and scalability. Another subtopic included in the last objective deals with the recommendations and implementation of the right machine learning services adapted to the business context. Candidates will also learn how to apply fundamental AWS security practices to solve different machine learning issues. Finally, they will become ready to deploy and operationalize various machine learning solutions.

>> Valid AWS-Certified-Machine-Learning-Specialty Exam Topics <<

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q120-Q125):

NEW QUESTION # 120
A company is observing low accuracy while training on the default built-in image classification algorithm in Amazon SageMaker. The Data Science team wants to use an Inception neural network architecture instead of a ResNet architecture.
Which of the following will accomplish this? (Select TWO.)

  • A. Use custom code in Amazon SageMaker with TensorFlow Estimator to load the model with an Inception network and use this for model training.
  • B. Customize the built-in image classification algorithm to use Inception and use this for model training.
  • C. Create a support case with the SageMaker team to change the default image classification algorithm to Inception.
  • D. Download and apt-get install the inception network code into an Amazon EC2 instance and use this instance as a Jupyter notebook in Amazon SageMaker.
  • E. Bundle a Docker container with TensorFlow Estimator loaded with an Inception network and use this for model training.

Answer: A,B


NEW QUESTION # 121
A Data Scientist needs to analyze employment data. The dataset contains approximately 10 million observations on people across 10 different features. During the preliminary analysis, the Data Scientist notices that income and age distributions are not normal. While income levels shows a right skew as expected, with fewer individuals having a higher income, the age distribution also show a right skew, with fewer older individuals participating in the workforce.
Which feature transformations can the Data Scientist apply to fix the incorrectly skewed data? (Choose two.)

  • A. Logarithmic transformation
  • B. High-degree polynomial transformation
  • C. One hot encoding
  • D. Numerical value binning
  • E. Cross-validation

Answer: D,E


NEW QUESTION # 122
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 Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.
  • 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 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.
  • D. 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.

Answer: A


NEW QUESTION # 123
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. Amazon SageMaker BlazingText allow mode
  • B. Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers
  • C. Natural Language Toolkit (NLTK) stemming and stop word removal
  • D. Amazon Comprehend syntax analysts and entity detection

Answer: C


NEW QUESTION # 124
A power company wants to forecast future energy consumption for its customers in residential properties and commercial business properties. Historical power consumption data for the last 10 years is available. A team of data scientists who performed the initial data analysis and feature selection will include the historical power consumption data and data such as weather, number of individuals on the property, and public holidays.
The data scientists are using Amazon Forecast to generate the forecasts.
Which algorithm in Forecast should the data scientists use to meet these requirements?

  • A. Autoregressive Integrated Moving Average (AIRMA)
  • B. Convolutional Neural Network - Quantile Regression (CNN-QR)
  • C. Prophet
  • D. Exponential Smoothing (ETS)

Answer: D


NEW QUESTION # 125
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