Professional-Machine-Learning-Engineer시험문제집완벽한시험최신덤프공부

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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:

Section	Objectives
Topic 1: Serving and scaling models	- Model optimization (Quantization, Distillation) - Batch prediction - Hardware accelerators (GPU/TPU) in serving - Online prediction (Vertex AI Prediction)
Topic 2: Scaling prototypes into ML models	- Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) - Training at scale (Distributed training, TPUs) - Hyperparameter tuning
Topic 3: Collaborating within and across teams to manage data and models	- Data management and governance - Version control and reproducibility (e.g., DVC, MLOps) - Collaboration between Data Scientists, Data Engineers, and ML Engineers
Topic 4: Automating and orchestrating ML pipelines	- Vertex AI Pipelines (Kubeflow Pipelines) - CI/CD for ML systems - Triggering and scheduling pipelines
Topic 5: Architecting low-code ML solutions	- Implementing BigQuery ML for basic models - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) - AutoML capabilities and implementation
Topic 6: Monitoring ML solutions	- Logging and alerting (Cloud Monitoring) - Performance monitoring and drift detection - Model retraining strategies

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Professional-Machine-Learning-Engineer공부자료, Professional-Machine-Learning-Engineer최신 덤프문제

Google Professional-Machine-Learning-Engineer 덤프구매전 한국어 온라인상담서비스부터 구매후 덤프 무료 업데이트버전제공 , Google Professional-Machine-Learning-Engineer시험불합격시 덤프비용 전액환불 혹은 다른 과목으로 교환 등 저희는 구매전부터 구매후까지 철저한 서비스를 제공해드립니다. Google Professional-Machine-Learning-Engineer 덤프는 인기덤프인데 지금까지 덤프를 구매한후 환불신청하신 분은 아직 없었습니다.

최신 Google Cloud Certified Professional-Machine-Learning-Engineer 무료샘플문제 (Q92-Q97):

질문 # 92
You have trained a text classification model in TensorFlow using Al Platform. You want to use the trained model for batch predictions on text data stored in BigQuery while minimizing computational overhead. What should you do?

A. Export the model to BigQuery ML.
B. Submit a batch prediction job on Al Platform that points to the model location in Cloud Storage.
C. Use Dataflow with the SavedModel to read the data from BigQuery
D. Deploy and version the model on Al Platform.

정답：B

설명：
This answer is correct because it allows you to use the trained TensorFlow model for batch predictions on text data stored in BigQuery without any additional processing or overhead. Al Platform provides a service for running batch prediction jobs that can take input data from BigQuery or Cloud Storage and write the output to BigQuery or Cloud Storage. You can use the SavedModel format to export your TensorFlow model to Cloud Storage and then submit a batch prediction job that points to the model location and the input data location. Al Platform will handle the scaling and distribution of the prediction requests and return the results in the specified output location. Reference:
[Al Platform: Batch prediction overview]
[Al Platform: Exporting a SavedModel for prediction]

질문 # 93
You work as an ML researcher at an investment bank and are experimenting with the Gemini large language model (LLM). You plan to deploy the model for an internal use case and need full control of the model's underlying infrastructure while minimizing inference time. Which serving configuration should you use for this task?

A. Deploy the model on a Vertex AI endpoint manually by creating a custom inference container.
B. Deploy the model on a Google Kubernetes Engine (GKE) cluster manually by creating a custom YAML manifest.
C. Deploy the model on a Google Kubernetes Engine (GKE) cluster using the deployment options in Model Garden.
D. Deploy the model on a Vertex AI endpoint using one-click deployment in Model Garden.

정답：B

질문 # 94
You work for a gaming company that manages a popular online multiplayer game where teams with 6 players play against each other in 5-minute battles. There are many new players every day. You need to build a model that automatically assigns available players to teams in real time.
User research indicates that the game is more enjoyable when battles have players with similar skill levels. Which business metrics should you track to measure your model's performance?

A. Rate of return as measured by additional revenue generated minus the cost of developing a new model
B. Precision and recall of assigning players to teams based on their predicted versus actual ability
C. Average time players wait before being assigned to a team
D. User engagement as measured by the number of battles played daily per user

정답：D

질문 # 95
You work for a company that provides an anti-spam service that flags and hides spam posts on social media platforms. Your company currently uses a list of 200,000 keywords to identify suspected spam posts. If a post contains more than a few of these keywords, the post is identified as spam. You want to start using machine learning to flag spam posts for human review. What is the main advantage of implementing machine learning for this business case?

A. New problematic phrases can be identified in spam posts.
B. Spam posts can be flagged using far fewer keywords.
C. A much longer keyword list can be used to flag spam posts.
D. Posts can be compared to the keyword list much more quickly.

정답：A

설명：
Machine learning algorithms can learn to identify spam posts based on a wider range of factors, such as the content of the post, the user's behavior, and the context in which the post appears.

질문 # 96
You work for a company that captures live video footage of checkout areas in their retail stores You need to use the live video footage to build a mode! to detect the number of customers waiting for service in near real time You want to implement a solution quickly and with minimal effort How should you build the model?

A. Train a Seq2Seq+ object detection model on an annotated dataset by using Vertex AutoML
B. Use the Vertex Al Vision Occupancy Analytics model.
C. Use the Vertex Al Vision Person/vehicle detector model
D. Train an AutoML object detection model on an annotated dataset by using Vertex AutoML

정답：B

설명：
According to the official exam guide1, one of the skills assessed in the exam is to "design, build, and productionalize ML models to solve business challenges using Google Cloud technologies". The Vertex AI Vision Occupancy Analytics model2 is a specialized pre-built vision model that lets you count people or vehicles given specific inputs you add in video frames. It provides advanced features such as active zones counting, line crossing counting, and dwelling detection. This model is suitable for the use case of detecting the number of customers waiting for service in near real time. You can easily create and deploy an occupancy analytics application using Vertex AI Vision3. The other options are not relevant or optimal for this scenario. Reference:
Professional ML Engineer Exam Guide
Occupancy analytics guide
Create an occupancy analytics app with BigQuery forecasting
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

질문 # 97
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