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AWS-Certified-Machine-Learning-Specialty시험패스자료, AWS-Certified-Machine-Learning-Specialty최고덤프자료

근 몇년간IT산업이 전례없이 신속히 발전하여 IT업계에 종사하는 분들이 여느때보다 많습니다. 경쟁이 이와같이 치열한 환경속에서 누구도 대체할수 없는 자기만의 자리를 찾으려면 IT인증자격증취득은 무조건 해야 하는것이 아닌가 싶습니다. Amazon인증 AWS-Certified-Machine-Learning-Specialty시험은 IT인증시험중 가장 인기있는 시험입니다. Itcertkr에서는 여러분이Amazon인증 AWS-Certified-Machine-Learning-Specialty시험을 한방에 패스하도록 실제시험문제에 대비한Amazon인증 AWS-Certified-Machine-Learning-Specialty덤프를 발췌하여 저렴한 가격에 제공해드립니다.시험패스 못할시 덤프비용은 환불처리 해드리기에 고객님께 아무런 페를 끼치지 않을것입니다.

AWS Certified Machine Learning - Specialty 자격증 시험을 준비하기 위해, 후보자들은 AWS에서 제공하는 온라인 강좌, 모의고사 및 화이트페이퍼 등 다양한 자원을 활용할 수 있습니다. 후보자들은 AWS 파트너가 제공하는 교육 세션 및 워크샵에 참석하고 AWS 지원 및 컨설팅 서비스를 활용할 수도 있습니다.

시험을 통과하기 위해서는, 지원자들은 여러 가지 주요 분야에서 능력을 증명해야 합니다. 이는 AWS 기계 학습 솔루션을 설계, 구축 및 배포하는 능력과 더불어 기계 학습을 실제 비즈니스 문제에 적용하는 능력을 포함합니다. 시험은 또한 AWS 서비스와 도구를 사용하여 확장 가능하고 안전하며 고가용성의 기계 학습 모델을 구축하는 지원자의 지식을 평가합니다.

>> AWS-Certified-Machine-Learning-Specialty시험패스자료 <<

Amazon AWS-Certified-Machine-Learning-Specialty최고덤프자료 & AWS-Certified-Machine-Learning-Specialty시험덤프

Itcertkr의Amazon AWS-Certified-Machine-Learning-Specialty시험자료의 문제와 답이 실제시험의 문제와 답과 아주 비슷합니다. 우리의 짧은 학습가이드로 빠른 시일 내에 관련지식을 터득하여 응시준비를 하게 합니다. 우리는 우리의Amazon AWS-Certified-Machine-Learning-Specialty인증시험덤프로 시험패스를 보장합니다.

AWS 인증 기계 학습 - 전문가 시험은 준비와 공부를 필요로 하는 도전적이고 포괄적인 자격증 프로그램입니다. 이 시험은 65개의 객관식 및 다중응답 문제로 구성되어 있으며, 시험 시간은 180분입니다. 시험 비용은 300달러이며, 영어, 일본어, 한국어 및 중국어 간체 등 다양한 언어로 제공됩니다.

최신 AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty 무료샘플문제 (Q54-Q59):

질문 # 54
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. 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.
• B. 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.
• C. 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.
• D. 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.

정답：C

질문 # 55
A Machine Learning Specialist is creating a new natural language processing application that processes a dataset comprised of 1 million sentences. The aim is to then run Word2Vec to generate embeddings of the sentences and enable different types of predictions.
Here is an example from the dataset:
"The quck BROWN FOX jumps over the lazy dog."
Which of the following are the operations the Specialist needs to perform to correctly sanitize and prepare the data in a repeatable manner? (Choose three.)

• A. One-hot encode all words in the sentence.
• B. Tokenize the sentence into words.
• C. Remove stop words using an English stopword dictionary.
• D. Normalize all words by making the sentence lowercase.
• E. Correct the typography on "quck" to "quick."
• F. Perform part-of-speech tagging and keep the action verb and the nouns only.

정답：B,C,D

설명：
1- Apply words stemming and lemmatization
2- Remove Stop words
3- Tokensize the sentences
https://towardsdatascience.com/nlp-extracting-the-main-topics-from-your-dataset-using-lda-in- minutes-21486f5aa925

질문 # 56
A Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access notebooks, train models, and deploy endpoints. To ensure the best operational performance, the Specialist needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked.
Which services are integrated with Amazon SageMaker to track this information? (Select TWO.)

• A. AWS Config
• B. Amazon CloudWatch
• C. AWS Trusted Advisor
• D. AWS CloudTrail
• E. AWS Health

정답：B,D

질문 # 57
A company uses camera images of the tops of items displayed on store shelves to determine which items were removed and which ones still remain. After several hours of data labeling, the company has a total of
1,000 hand-labeled images covering 10 distinct items. The training results were poor.
Which machine learning approach fulfills the company's long-term needs?

• A. Augment training data for each item using image variants like inversions and translations, build the model, and iterate.
• B. Reduce the number of distinct items from 10 to 2, build the model, and iterate
• C. Convert the images to grayscale and retrain the model
• D. Attach different colored labels to each item, take the images again, and build the model

정답：A

설명：
Explanation
Data augmentation is a technique that can increase the size and diversity of the training data by applying various transformations to the original images, such as inversions, translations, rotations, scaling, cropping, flipping, and color variations. Data augmentation can help improve the performance and generalization of image classification models by reducing overfitting and introducing more variability to the data. Data augmentation is especially useful when the original data is limited or imbalanced, as in the case of the company's problem. By augmenting the training data for each item using image variants, the company can build a more robust and accurate model that can recognize the items on the store shelves from different angles, positions, and lighting conditions. The company can also iterate on the model by adding more data or fine-tuning the hyperparameters to achieve better results.
References:
Build high performing image classification models using Amazon SageMaker JumpStart The Effectiveness of Data Augmentation in Image Classification using Deep Learning Data augmentation for improving deep learning in image classification problem Class-Adaptive Data Augmentation for Image Classification

질문 # 58
A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet.
How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?

• A. Route Amazon SageMaker traffic through an on-premises network.
• B. Create a NAT gateway within the corporate VPC.
• C. Create Amazon SageMaker VPC interface endpoints within the corporate VPC.
• D. Create VPC peering with Amazon VPC hosting Amazon SageMaker.

정답：B

설명：
https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-dg.pdf (46)

질문 # 59
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