Professional-Machine-Learning-Engineer Praxisprüfung & Professional-Machine-Learning-Engineer Dumps Deutsch

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

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

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Google Professional Machine Learning Engineer Professional-Machine-Learning-Engineer Prüfungsfragen mit Lösungen (Q369-Q374):

369. Frage
You are working on a prototype of a text classification model in a managed Vertex AI Workbench notebook. You want to quickly experiment with tokenizing text by using a Natural Language Toolkit (NLTK) library. How should you add the library to your Jupyter kernel?

A. Install the NLTK library from a terminal by using the pip install nltk command.
B. Install the NLTK library from a Jupyter cell by using the !pip install nltk --user command.
C. Create a new Vertex AI Workbench notebook with a custom image that includes the NLTK library.
D. Write a custom Dataflow job that uses NLTK to tokenize your text and saves the output to Cloud Storage.

Antwort: B

370. Frage
You work for a pharmaceutical company based in Canad
a. Your team developed a BigQuery ML model to predict the number of flu infections for the next month in Canada Weather data is published weekly and flu infection statistics are published monthly. You need to configure a model retraining policy that minimizes cost What should you do?

A. Download the weather and flu data each week Configure Cloud Scheduler to execute a Vertex Al pipeline to retrain the model weekly.
B. Download the weather and flu data each week Configure Cloud Scheduler to execute a Vertex Al pipeline to retrain the model every month.
C. Download the weather data each week, and download the flu data each month Deploy the model to a Vertex Al endpoint with feature drift monitoring. and retrain the model if a monitoring alert is detected.
D. Download the weather and flu data each month Configure Cloud Scheduler to execute a Vertex Al pipeline to retrain the model monthly.

Antwort: C

371. Frage
You are deploying a generative AI chatbot for an online gaming community using Gemini Enterprise Agent Platform. During beta testing, you discover that users are able to input malicious prompts that cause the model to generate hate speech and violent content. You need to implement a guardrail to block this content in production with minimal development overhead.
What should you do?

A. Implement and configure Model Armor to sanitize responses if hateful content is detected.
B. Update the system instruction (system prompt) of the model to explicitly state: "You are a helpful assistant. Do not generate hate speech or violent content."
C. Integrate the Cloud Natural Language API's moderation capabilities to analyze and block toxic user prompts before they reach the chatbot model.
D. Create a supervised fine-tuning (SFT) job in Agent Platform, and tune the model using a curated dataset of safe, policy-compliant gaming conversations.

Antwort: A

Begründung:
Model Armor provides a managed guardrail that inspects prompts and responses for unsafe content, including hate speech and violence, and can block or sanitize detected content. It reduces development and maintenance effort compared with building custom moderation logic or relying only on prompting.

372. Frage
Your work for a textile manufacturing company. Your company has hundreds of machines and each machine has many sensors. Your team used the sensory data to build hundreds of ML models that detect machine anomalies Models are retrained daily and you need to deploy these models in a cost-effective way. The models must operate 24/7 without downtime and make sub millisecond predictions. What should you do?

A. Deploy a Dataflow batch pipeline and a Vertex Al Prediction endpoint.
B. Deploy a Dataflow streaming pipeline and a Vertex Al Prediction endpoint with autoscaling.
C. Deploy a Dataflow streaming pipeline with the Runlnference API and use automatic model refresh.
D. Deploy a Dataflow batch pipeline with the Runlnference API. and use model refresh.

Antwort: C

Begründung:
A Dataflow streaming pipeline is a cost-effective way to process large volumes of real-time data from sensors. The RunInference API is a Dataflow transform that allows you to run online predictions on your streaming data using your ML models. By using the RunInference API, you can avoid the latency and cost of using a separate prediction service. The automatic model refresh feature enables you to update your models in the pipeline without redeploying the pipeline. This way, you can ensure that your models are always up-to-date and accurate. By deploying a Dataflow streaming pipeline with the RunInference API and using automatic model refresh, you can achieve sub-millisecond predictions, 24/7 availability, and low operational overhead for your ML models. Reference:
Dataflow documentation
RunInference API documentation
Automatic model refresh documentation
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

373. Frage
Your team is building an application for a global bank that will be used by millions of customers. You built a forecasting model that predicts customers1 account balances 3 days in the future. Your team will use the results in a new feature that will notify users when their account balance is likely to drop below $25. How should you serve your predictions?

A. 1 Build a notification system on Firebase
2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when your model predicts that a user's account balance will drop below the $25 threshold
B. 1. Build a notification system on Firebase
2. Register each user with a user ID on the Firebase Cloud Messaging server, which sends a notification when the average of all account balance predictions drops below the $25 threshold
C. 1. Create a Pub/Sub topic for each user
2 Deploy a Cloud Function that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold.
D. 1. Create a Pub/Sub topic for each user
2. Deploy an application on the App Engine standard environment that sends a notification when your model predicts that a user's account balance will drop below the $25 threshold

Antwort: A

Begründung:
This answer is correct because it uses Firebase, a platform that provides a scalable and reliable notification system for mobile and web applications. Firebase Cloud Messaging (FCM) allows you to send messages and notifications to users across different devices and platforms. By registering each user with a user ID on the FCM server, you can target specific users based on their account balance predictions and send them personalized notifications when their balance is likely to drop below the $25 threshold. This way, you can provide a useful and timely feature for your customers and increase their engagement and retention.
References:
* [Firebase Cloud Messaging]
* [Firebase Cloud Messaging: Send messages to specific devices]

374. Frage
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