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완벽한Professional-Machine-Learning-Engineer최신업데이트시험덤프덤프샘플문제다운로드
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인증 시험은 후보자의 기계 학습 모델 및 시스템을 설계, 구축 및 최적화하는 능력을 평가합니다. 이 시험은 기계 학습 알고리즘, 데이터 전처리 및 기능 엔지니어링, 모델 선택 및 교육, 하이퍼 파라미터 튜닝, 모델 평가 및 배포에 대한 후보자의 지식을 테스트하도록 설계되었습니다. 이 시험은 또한 대규모 데이터 세트, 분산 컴퓨팅 시스템 및 클라우드 기반 머신 러닝 서비스를 사용하는 응시자의 능력에 중점을 둡니다.
이 인증을 받으려면 응시자는 기계 학습 및 클라우드 컴퓨팅과 관련된 광범위한 주제를 다루는 엄격한 시험을 통과해야합니다. 시험은 객관식 및 시나리오 기반 질문으로 구성되며, 후보자는 시험을 완료하기 위해 2 시간 반 동안 주어집니다. 시험은 온라인으로 관리되며 전 세계 어디에서나 가져올 수 있습니다. 시험을 통과하면 응시자는 링크드 인 프로필, 이력서 또는 웹 사이트에 표시 할 수있는 디지털 배지를 받게되며, 이는 머신 러닝 분야 및 Google 클라우드 플랫폼에서 숙련도를 보여 주었음을 나타냅니다. 이 인증은 업계 전문가에 의해 인정되며 개인이 기계 학습 및 클라우드 컴퓨팅 분야에서 경력을 발전시키는 데 도움이 될 수 있습니다.
>> Professional-Machine-Learning-Engineer최신 업데이트 시험덤프 <<
Professional-Machine-Learning-Engineer최신 업데이트 시험덤프 최신 업데이트버전 인증덤프
Google인증 Professional-Machine-Learning-Engineer시험을 등록하였는데 시험준비를 어떻게 해애 될지 몰라 고민중이시라면 이 글을 보고Pass4Test를 찾아주세요. Pass4Test의Google인증 Professional-Machine-Learning-Engineer덤프샘플을 체험해보시면 시험에 대한 두려움이 사라질것입니다. Pass4Test의Google인증 Professional-Machine-Learning-Engineer덤프는Google인증 Professional-Machine-Learning-Engineer실제시험문제를 마스터한 기초에서 제작한 최신시험에 대비한 공부자료로서 시험패스율이 100%입니다. 하루 빨리 덤프를 마련하여 시험을 준비하시면 자격증 취득이 빨라집니다.
Google Professional Machine Learning 엔지니어 인증을 얻으려면 응시자는 객관식 및 시나리오 기반 질문으로 구성된 2 시간 시험을 통과해야합니다. 이 시험은 Google Cloud 플랫폼을 사용하여 확장 가능하고 효율적이며 안전한 머신 러닝 모델을 설계하고 개발하는 능력에 대한 후보자를 평가합니다. 인증은 전 세계적으로 인정되며 기계 학습 분야의 최첨단 기술을 사용하는 전문 지식과 능력을 보여주기 때문에 고용주는 고용주가 높이 평가합니다. 이 인증은 또한 Google의 리소스 및 기계 학습 전문가 커뮤니티에 대한 액세스를 제공 하여이 분야에서 경력을 발전시키려는 사람에게 귀중한 자산입니다.
최신 Google Cloud Certified Professional-Machine-Learning-Engineer 무료샘플문제 (Q28-Q33):
질문 # 28
You are an ML engineer at a global shoe store. You manage the ML models for the company's website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users. What should you do?
- A. Build a regression model using the features as predictors
- B. Build a collaborative-based filtering model
- C. Build a classification model
- D. Build a knowledge-based filtering model
정답：B
질문 # 29
You need to build an ML model for a social media application to predict whether a user's submitted profile photo meets the requirements. The application will inform the user if the picture meets the requirements. How should you build a model to ensure that the application does not falsely accept a non-compliant picture?
- A. Use Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that do not meet the profile photo requirements.
- B. Use AutoML to optimize the model's F1 score in order to balance the accuracy of false positives and false negatives.
- C. Use Vertex AI Workbench user-managed notebooks to build a custom model that has three times as many examples of pictures that meet the profile photo requirements.
- D. Use AutoML to optimize the model's recall in order to minimize false negatives.
정답：D
설명：
Recall is the ratio of true positives to the sum of true positives and false negatives. It measures how well the model can identify all the relevant cases. In this scenario, the relevant cases are the pictures that do not meet the profile photo requirements. Therefore, minimizing false negatives means minimizing the cases where the model incorrectly predicts that a non-compliant picture meets the requirements. By using AutoML to optimize the model's recall, the model will be more likely to reject a non-compliant picture and inform the user accordingly. References:
* [AutoML Vision] is a service that allows you to train custom ML models for image classification and object detection tasks. You can use AutoML to optimize your model for different metrics, such as recall,
* precision, or F1 score.
* [Recall] is one of the evaluation metrics for ML models. It is defined as TP / (TP + FN), where TP is the number of true positives and FN is the number of false negatives. Recall measures how well the model can identify all the relevant cases. A high recall means that the model has a low rate of false negatives.
질문 # 30
You are working on a Neural Network-based project. The dataset provided to you has columns with different ranges. While preparing the data for model training, you discover that gradient optimization is having difficulty moving weights to a good solution. What should you do?
- A. Use feature construction to combine the strongest features.
- B. Use the representation transformation (normalization) technique.
- C. Improve the data cleaning step by removing features with missing values.
- D. Change the partitioning step to reduce the dimension of the test set and have a larger training set.
정답：B
설명：
https://developers.google.com/machine-learning/data-prep/transform/transform-numeric
- NN models needs features with close ranges
- SGD converges well using features in [0, 1] scale
- The question specifically mention "different ranges"
Documentation - https://developers.google.com/machine-learning/data-prep/transform/transform-numeric
질문 # 31
You recently used BigQuery ML to train an AutoML regression model. You shared results with your team and received positive feedback. You need to deploy your model for online prediction as quickly as possible. What should you do?
- A. Export the model from BigQuery ML to Cloud Storage Import the model into Vertex Al Model Registry Deploy the model to a Vertex Al endpoint.
- B. Retrain the model by using BigQuery ML. and specify Vertex Al as the model registry Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.
- C. Retrain the model by using Vertex Al Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.
- D. Alter the model by using BigQuery ML and specify Vertex Al as the model registry Deploy the model from Vertex Al Model Registry to a Vertex Al endpoint.
정답：A
질문 # 32
You are developing a mode! to detect fraudulent credit card transactions. You need to prioritize detection because missing even one fraudulent transaction could severely impact the credit card holder. You used AutoML to tram a model on users' profile information and credit card transaction data. After training the initial model, you notice that the model is failing to detect many fraudulent transactions. How should you adjust the training parameters in AutoML to improve model performance?
Choose 2 answers
- A. Add more positive examples to the training set.
- B. Reduce the maximum number of node hours for training.
- C. Add more negative examples to the training set.
- D. Increase the score threshold.
- E. Decrease the score threshold.
정답：A,E
설명：
The best options for adjusting the training parameters in AutoML to improve model performance are to decrease the score threshold and add more positive examples to the training set. These options can help increase the detection rate of fraudulent transactions, which is the priority for this use case. The score threshold is a parameter that determines the minimum probability score that a prediction must have to be classified as positive. Decreasing the score threshold can increase the recall of the model, which is the proportion of actual positive cases that are correctly identified. Increasing the recall can help reduce the number of false negatives, which are fraudulent transactions that aremissed by the model. However, decreasing the score threshold can also decrease the precision of the model, which is the proportion of positive predictions that are actually correct. Decreasing the precision can increase the number of false positives, which are legitimate transactions that are flagged as fraudulent by the model. Therefore, there is a trade-off between recall and precision, and the optimal score threshold depends on the business objective and the cost of errors1.
Adding more positive examples to the training set can help balance the data distribution and improve the model performance. Positive examples are the instances that belong to the target class, which in this case are fraudulent transactions. Negative examples are the instances that belong to the other class, which in this case are legitimate transactions. Fraudulent transactions are usually rare and imbalanced compared to legitimate transactions, which can cause the model to be biased towards the majority class and fail to learn the characteristics of the minority class. Adding more positive examples can help the model learn more features and patterns of the fraudulent transactions, and increase the detection rate2.
The other options are not as good as options B and C, for the following reasons:
* Option A: Increasing the score threshold would decrease the detection rate of fraudulent transactions, which is the opposite of the desired outcome. Increasing the score threshold would decrease the recall of the model, which is the proportion of actual positive cases that are correctly identified. Decreasing the recall would increase the number of false negatives, which are fraudulent transactions that are missed by the model. Increasing the score threshold would increase the precision of the model, which is the proportion of positive predictions that are actually correct. Increasing the precision would decrease the number of false positives, which are legitimate transactions that are flagged as fraudulent by the
* model. However, in this use case, the cost of false negatives is much higher than the cost of false positives, so increasing the score threshold is not a good option1.
* Option D: Adding more negative examples to the training set would not improve the model performance, and could worsen the data imbalance. Negative examples are the instances that belong to the other class, which in this case are legitimate transactions. Legitimate transactions are usually abundant and dominant compared to fraudulent transactions, which can cause the model to be biased towards the majority class and fail to learn the characteristics of the minority class. Adding more negative examples would exacerbate this problem, and decrease the detection rate of the fraudulent transactions2.
* Option E: Reducing the maximum number of node hours for training would not improve the model performance, and could limit the model optimization. Node hours are the units of computation that are used to train an AutoML model. The maximum number of node hours is a parameter that determines the upper limit of node hours that can be used for training. Reducing the maximum number of node hours would reduce the training time and cost, but also the model quality and accuracy. Reducing the maximum number of node hours would limit the number of iterations, trials, and evaluations that the model can perform, and prevent the model from finding the optimal hyperparameters and architecture3.
References:
* Preparing for Google Cloud Certification: Machine Learning Engineer, Course 5: Responsible AI, Week
4: Evaluation
* Google Cloud Professional Machine Learning Engineer Exam Guide, Section 2: Developing high-quality ML models, 2.2 Handling imbalanced data
* Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 4:
Low-code ML Solutions, Section 4.3: AutoML
* Understanding the score threshold slider
* Handling imbalanced data sets in machine learning
* AutoML Vision pricing
질문 # 33
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Professional-Machine-Learning-Engineer시험덤프데모: https://www.pass4test.net/Professional-Machine-Learning-Engineer.html
