NEW AMAZON AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY EXAM OBJECTIVES - LATEST AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY EXAM DURATION

New Amazon AWS-Certified-Machine-Learning-Specialty Exam Objectives - Latest AWS-Certified-Machine-Learning-Specialty Exam Duration

New Amazon AWS-Certified-Machine-Learning-Specialty Exam Objectives - Latest AWS-Certified-Machine-Learning-Specialty Exam Duration

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The AWS Certified Machine Learning - Specialty certification exam covers a wide range of topics, including exploring data, building and training ML models, deploying models, and managing and optimizing ML solutions. AWS-Certified-Machine-Learning-Specialty exam also tests the candidate's knowledge of machine learning algorithms, deep learning, neural networks, and other related technologies. Passing this certification exam requires a deep understanding of AWS ML services, including Amazon SageMaker, Amazon Rekognition, and Amazon Comprehend.

To be eligible for the AWS Certified Machine Learning - Specialty certification, candidates must have a minimum of one-year experience in developing and deploying machine learning models on AWS. They must also have a solid understanding of AWS services, such as Amazon SageMaker, Amazon S3, and Amazon EC2, and have experience with programming languages such as Python and R. AWS-Certified-Machine-Learning-Specialty Exam itself consists of 65 multiple-choice and multiple-response questions, which must be completed within 3 hours. Candidates who pass the exam will receive an AWS Certified Machine Learning - Specialty certification, which is valid for three years.

The AWS Certified Machine Learning - Specialty certification exam covers various topics related to ML, such as data preparation, feature engineering, model training and evaluation, deployment and implementation, and machine learning algorithms. AWS-Certified-Machine-Learning-Specialty exam is designed to test the candidate’s ability to design and implement ML models using Amazon SageMaker, understand the core ML concepts, and select the appropriate AWS services to deploy ML solutions. Additionally, the exam also tests the candidate’s ability to optimize and tune ML models to achieve the desired outcome.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q18-Q23):

NEW QUESTION # 18
A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among
200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance.
What type of machine learning model should be used?

  • A. Classification month-to-month using supervised learning of the 200 categories based on claim contents.
  • B. Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.
  • C. Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.
  • D. Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.

Answer: D


NEW QUESTION # 19
A company has raw user and transaction data stored in AmazonS3 a MySQL database, and Amazon RedShift A Data Scientist needs to perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon RedShift, and then calculating the average-of a few selected columns from the joined data Which AWS service should the Data Scientist use?

  • A. Amazon Redshift Spectrum
  • B. AWS Glue
  • C. Amazon Athena
  • D. Amazon QuickSight

Answer: C


NEW QUESTION # 20
A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.
The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:
* Real-time analytics
* Interactive analytics of historical data
* Clickstream analytics
* Product recommendations
Which services should the Specialist use?

  • A. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations
  • B. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
  • C. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-realtime data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendations
  • D. AWS Glue as the data dialog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations

Answer: D


NEW QUESTION # 21
A retail company wants to update its customer support system. The company wants to implement automatic routing of customer claims to different queues to prioritize the claims by category.
Currently, an operator manually performs the category assignment and routing. After the operator classifies and routes the claim, the company stores the claim's record in a central database. The claim's record includes the claim's category.
The company has no data science team or experience in the field of machine learning (ML). The company's small development team needs a solution that requires no ML expertise.
Which solution meets these requirements?

  • A. Export the database to a .csv file with two columns: claim_label and claim_text. Use the Amazon SageMaker Object2Vec algorithm and the .csv file to train a model. Use SageMaker to deploy the model to an inference endpoint. Develop a service in the application to use the inference endpoint to process incoming claims, predict the labels, and route the claims to the appropriate queue.
  • B. Use Amazon Textract to process the database and automatically detect two columns: claim_label and claim_text. Use Amazon Comprehend custom classification and the extracted information to train the custom classifier. Develop a service in the application to use the Amazon Comprehend API to process incoming claims, predict the labels, and route the claims to the appropriate queue.
  • C. Export the database to a .csv file with two columns: claim_label and claim_text. Use Amazon Comprehend custom classification and the .csv file to train the custom classifier. Develop a service in the application to use the Amazon Comprehend API to process incoming claims, predict the labels, and route the claims to the appropriate queue.
  • D. Export the database to a .csv file with one column: claim_text. Use the Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm and the .csv file to train a model. Use the LDA algorithm to detect labels automatically. Use SageMaker to deploy the model to an inference endpoint. Develop a service in the application to use the inference endpoint to process incoming claims, predict the labels, and route the claims to the appropriate queue.

Answer: B


NEW QUESTION # 22
A company is creating an application to identify, count, and classify animal images that are uploaded to the company's website. The company is using the Amazon SageMaker image classification algorithm with an ImageNetV2 convolutional neural network (CNN). The solution works well for most animal images but does not recognize many animal species that are less common.
The company obtains 10,000 labeled images of less common animal species and stores the images in Amazon S3. A machine learning (ML) engineer needs to incorporate the images into the model by using Pipe mode in SageMaker.
Which combination of steps should the ML engineer take to train the model? (Choose two.)

  • A. Initiate transfer learning. Train the model by using the images of less common species.
  • B. Use an augmented manifest file in JSON Lines format.
  • C. Use an Inception model that is available with the SageMaker image classification algorithm.
  • D. Use a ResNet model. Initiate full training mode by initializing the network with random weights.
  • E. Create a .lst file that contains a list of image files and corresponding class labels. Upload the .lst file to Amazon S3.

Answer: A,E

Explanation:
The combination of steps that the ML engineer should take to train the model are to create a .lst file that contains a list of image files and corresponding class labels, upload the .lst file to Amazon S3, and initiate transfer learning by training the model using the images of less common species. This approach will allow the ML engineer to leverage the existing ImageNetV2 CNN model and fine-tune it with the new data using Pipe mode in SageMaker.
A .lst file is a text file that contains a list of image files and corresponding class labels, separated by tabs. The .
lst file format is required for using the SageMaker image classification algorithm with Pipe mode. Pipe mode is a feature of SageMaker that enables streaming data directly from Amazon S3 to the training instances, without downloading the data first. Pipe mode can reduce the startup time, improve the I/O throughput, and enable training on large datasets that exceed the disk size limit. To use Pipe mode, the ML engineer needs to upload the .lst file to Amazon S3 and specify the S3 path as the input data channel for the training job1.
Transfer learning is a technique that enables reusing a pre-trained model for a new task by fine-tuning the model parameters with new data. Transfer learning can save time and computational resources, as well as improve the performance of the model, especially when the new task is similar to the original task. The SageMaker image classification algorithm supports transfer learning by allowing the ML engineer to specify the number of output classes and the number of layers to be retrained. The ML engineer can use the existing ImageNetV2 CNN model, which is trained on 1,000 classes of common objects, and fine-tune it with the new data of less common animal species, which is a similar task2.
The other options are either less effective or not supported by the SageMaker image classification algorithm.
Using a ResNet model and initiating full training mode would require training the model from scratch, which would take more time and resources than transfer learning. Using an Inception model is not possible, as the SageMaker image classification algorithm only supports ResNet and ImageNetV2 models. Using an augmented manifest file in JSON Lines format is not compatible with Pipe mode, as Pipe mode only supports .
lst files for image classification1.
1: Using Pipe input mode for Amazon SageMaker algorithms | AWS Machine Learning Blog
2: Image Classification Algorithm - Amazon SageMaker


NEW QUESTION # 23
......

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