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인공지능은 개인용 가상 어시스턴트에서 자율 주행 자동차까지 우리가 알고 있는 세상을 크게 바꾸고 있습니다. 이에 구글 프로페셔널 머신 러닝 엔지니어 인증을 취득하면 높은 연봉과 흥미로운 경력을 제공받을 수 있습니다. 이 인증은 ML 모델을 디자인, 개발, 생산화 및 모니터링하고 팀 간 협력을 조율하는 역량을 인정받으며, 지식과 산업에서 인정받을 수 있습니다.

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Professional-Machine-Learning-Engineer퍼펙트 덤프문제 - Professional-Machine-Learning-Engineer시험대비 최신버전 문제

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Google Professional Machine Learning Engineer 인증을 받으려면 응시자는 Google Cloud 플랫폼을 사용하여 기계 학습 모델을 설계, 구현 및 최적화하는 능력을 테스트하는 엄격한 시험을 통과해야합니다. 시험에는 데이터 준비, 모델 교육, 모델 평가 및 배포 전략을 포함한 광범위한 주제가 다룹니다. 또한이 시험은 또한 윤리적이고 책임있는 AI 관행에 대한 지식뿐만 아니라 성능 및 확장 성 모델을 최적화하는 후보자의 능력을 테스트합니다.

Google Professional Machine Learning Engineer 자격증을 취득하면 고용주 및 클라이언트에게 Google Cloud Platform에서 효과적인 기계 학습 솔루션을 디자인하고 구현할 수 있는 기술과 지식이 있다는 것을 증명합니다. 이 자격증은 기계 학습과 클라우드 컴퓨팅에서 기술을 개발하고자하는 데이터 과학자, 소프트웨어 엔지니어 및 기타 전문가들에게 유용한 자격증입니다.

최신 Google Cloud Certified Professional-Machine-Learning-Engineer 무료샘플문제 (Q192-Q197):

질문 # 192
Your organization manages an online message board A few months ago, you discovered an increase in toxic language and bullying on the message board. You deployed an automated text classifier that flags certain comments as toxic or harmful. Now some users are reporting that benign comments referencing their religion are being misclassified as abusive Upon further inspection, you find that your classifier's false positive rate is higher for comments that reference certain underrepresented religious groups. Your team has a limited budget and is already overextended. What should you do?

정답：B

설명：
The problem of the text classifier is that it has a high false positive rate for comments that reference certain underrepresented religious groups. This means that the classifier is not able to distinguish between toxic and non-toxic language when those groups are mentioned. One possible reason for this is that the training data does not have enough examples of non-toxic comments that reference those groups, leading to a biased model. Therefore, a possible solution is to add synthetic training data where those phrases are used in non- toxic ways, which can help the model learn to generalize better and reduce the false positive rate. Synthetic data is artificially generated data that mimics the characteristics of real data, and can be used to augment the existing data when the real data is scarce or imbalanced. References:
* Preparing for Google Cloud Certification: Machine Learning Engineer, Course 5: Responsible AI, Week 3: Fairness
* Google Cloud Professional Machine Learning Engineer Exam Guide, Section 4: Ensuring solution quality, 4.4 Evaluating fairness and bias in ML models
* Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 9:
Responsible AI, Section 9.3: Fairness and Bias


질문 # 193
You work for a toy manufacturer that has been experiencing a large increase in demand. You need to build an ML model to reduce the amount of time spent by quality control inspectors checking for product defects.
Faster defect detection is a priority. The factory does not have reliable Wi-Fi. Your company wants to implement the new ML model as soon as possible. Which model should you use?

정답：C

설명：
AutoML Vision Edge is a service that allows you to create custom image classification and object detection models that can run on edge devices, such as mobile phones, tablets, or IoT devices1. AutoMLVision Edge offers four types of models that vary in size, accuracy, and latency: mobile-versatile-1,mobile-low-latency-1, mobile-high-accuracy-1, and mobile-core-ml-low-latency-12. Each model has its own trade-offs and use cases, depending on the device specifications and the application requirements.
For the use case of building an ML model to reduce the amount of time spent by quality control inspectors checking for product defects, the best model to use is the AutoML Vision Edge mobile-low-latency-1 model. This model is optimized for fast inference on mobile devices, with a latency of less than 50 milliseconds on a Pixel 1 phone2. Faster defect detection is a priority for the toy manufacturer, and the factory does not have reliable Wi-Fi, so a low-latency model that can run on the device without internet connection is ideal. The mobile-low-latency-1 model also has a small size of less than 4 MB, which makes it easy to deploy and update2. The mobile-low-latency-1 model has a slightly lower accuracy than the mobile-high-accuracy-1 model, but it is still suitable for most image classification tasks2. Therefore, the AutoML Vision Edge mobile-low-latency-1 model is the best option for this use case.
References:
* AutoML Vision Edge documentation
* AutoML Vision Edge model types


질문 # 194
You work for a pet food company that manages an online forum Customers upload photos of their pets on the forum to share with others About 20 photos are uploaded daily You want to automatically and in near real time detect whether each uploaded photo has an animal You want to prioritize time and minimize cost of your application development and deployment What should you do?

정답：C

설명：
Cloud Vision API is a service that allows you to analyze images using pre-trained machine learning models 1
. You can use Cloud Vision API to perform various tasks, such as face detection, text extraction, logo recognition, and object localization 1 . Object localization is a feature that allows you to detect multiple objects in an image and draw bounding boxes around them 2 . You can also get the labels and confidence scores for each detected object 2 .
By sending user-submitted images to the Cloud Vision API, you can use object localization to identify all objects in the image and compare the results against a list of animals. You can use the OBJECT_LOCALIZATION feature type in the A nnotateImageRequest to request object localization 3
. You can then use the localizedObjectAnnotations field in the AnnotateImageResponse to get the list of detected objects, their labels, and their confidence scores. You can compare the labels with a predefined list of animals, such as dogs, cats, birds, etc., and determine whether the image has an animal or not.
This option is the best for your scenario, because it allows you to automatically and in near real time detect whether each uploaded photo has an animal, without requiring any manual labeling, model training, or model deployment. You can also prioritize time and minimize cost of your application development and deployment, as you can use the Cloud Vision API as a ready-to-use service, without needing any machine learning expertise or infrastructure.
The other options are not suitable for your scenario, because they either require manual labeling, model training, or model deployment, which would increase the time and cost of your application development and deployment, or they use object detection models, which are more complex and computationally expensive than object localization models, and are not necessary for your simple task of detecting whether an image has an animal or not.
:
Cloud Vision API | Google Cloud
Object localization | Cloud Vision API | Google Cloud
AnnotateI mageRequest | Cloud Vision API | Google Cloud
[AnnotateImageResponse | Cloud Vision API | Google Cloud]


질문 # 195
You have trained a DNN regressor with TensorFlow to predict housing prices using a set of predictive features. Your default precision is tf.float64, and you use a standard TensorFlow estimator; estimator = tf.estimator.DNNRegressor( feature_columns=[YOUR_LIST_OF_FEATURES], hidden_units-[1024, 512, 256], dropout=None) Your model performs well, but Just before deploying it to production, you discover that your current serving latency is 10ms @ 90 percentile and you currently serve on CPUs. Your production requirements expect a model latency of 8ms @ 90 percentile. You are willing to accept a small decrease in performance in order to reach the latency requirement Therefore your plan is to improve latency while evaluating how much the model's prediction decreases. What should you first try to quickly lower the serving latency?

정답：C

설명：
Applying quantization to your SavedModel by reducing the floating point precision can help reduce the serving latency by decreasing the amount of memory and computation required to make a prediction. TensorFlow provides tools such as the tf.quantization module that can be used to quantize models and reduce their precision, which can significantly reduce serving latency without a significant decrease in model performance.


질문 # 196
You have been asked to build a model using a dataset that is stored in a medium-sized (~10 GB) BigQuery table. You need to quickly determine whether this data is suitable for model development. You want to create a one-time report that includes both informative visualizations of data distributions and more sophisticated statistical analyses to share with other ML engineers on your team. You require maximum flexibility to create your report. What should you do?

정답：C


질문 # 197
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