Hilfsreiche Prüfungsunterlagen verwirklicht Ihren Wunsch nach der Zertifikat der Google Professional Machine Learning Engineer

P.S. Kostenlose und neue Professional-Machine-Learning-Engineer Prüfungsfragen sind auf Google Drive freigegeben von PrüfungFrage verfügbar: https://drive.google.com/open?id=1B5VrMuo77kQvNbEpTR7xLPsrLgkFYmJt

Die von PrüfungFrage gebotenen Prüfungsfragen enthalten wertvolle Prüfungserfahrungen und relevante Prüfungsmaterialien von IT-Experten uud auch die Prüfungsfragen und Antworten fürGoogle Professional-Machine-Learning-Engineer Zertifizierungsprüfung. Mit unserem guten Ruf in der IT-Branche geben wir Ihnen 100% Garantie. Sie können versuchsweise die Examensübungen-und antworten für die Google Professional-Machine-Learning-Engineer Zertifizierungsprüfung teilweise als Probe umsonst herunterladen. Dann können Sie ganz beruhigt unsere Schulungsunterlagen kaufen.

Die Zertifizierungsprüfung ist in mehrere Abschnitte unterteilt, von denen jeder einen bestimmten Aspekt des maschinellen Lernens abdeckt. Die Abschnitte umfassen die Datenvorbereitung, den Modellbau, die Modellbereitstellung und das Monitoring. Jeder Abschnitt ist darauf ausgelegt, die Fähigkeit des Einzelnen zu testen, Konzepte des maschinellen Lernens in einer praktischen Umgebung anzuwenden. Das Prüfungsformat umfasst Multiple-Choice-Fragen, Fallstudien und praktische Übungen, die die Fähigkeit des Einzelnen messen, Konzepte des maschinellen Lernens auf realen Szenarien anzuwenden.

>> Professional-Machine-Learning-Engineer Probesfragen <<

Die seit kurzem aktuellsten Google Professional Machine Learning Engineer Prüfungsunterlagen, 100% Garantie für Ihen Erfolg in der Google Professional-Machine-Learning-Engineer Prüfungen!

Um die Google Professional-Machine-Learning-Engineer Zertifizierungsprüfung zu bestehen, wählen Sie doch unseren PrüfungFrage. Sie werden sicher nicht bereuen, dass Sie mit so wenigem Geld die Prüfung bestehen können. Unser PrüfungFrage wird Ihnen helfen, sich auf die Prüfung gut vorzubereiten und die Google Professional-Machine-Learning-Engineer Zertifizierungsprüfung (Google Professional Machine Learning Engineer) erfolgreich zu bestehen. Außerdem bieten wir Ihnen kostenlos einen einjährigen Update-Service.

Die Google Professional Machine Learning Engineer -Zertifizierung ist in der Branche hoch geschätzt und kann zu hervorragenden Karrieremöglichkeiten für Personen mit Fachkenntnissen in diesem Bereich führen. Diese Zertifizierung ist ein Beweis für die Fähigkeit eines Kandidaten, Modelle für maschinelles Lernen zu entwerfen, zu entwickeln und bereitzustellen, und es kann ein wertvolles Gut für alle sein, die eine Karriere im maschinellen Lernen oder in der Datenwissenschaft suchen. Darüber hinaus zeigt die Zertifizierung das Wissen eines Kandidaten in Google Cloud-Technologien und deren Fähigkeit, sie effektiv zu verwenden, um reale Probleme zu lösen.

Personen, die die Google Professional Machine Learning Engineer Zertifizierungsprüfung bestehen, erhalten ein Zertifikat, das ihre Kompetenz im Bereich des maschinellen Lernens bestätigt. Das Zertifikat wird von der Google Cloud Platform anerkannt und ist ein wertvolles Asset für Personen, die Karrierechancen im Bereich des maschinellen Lernens suchen. Die Zertifizierungsprüfung ist eine anspruchsvolle, aber lohnende Erfahrung, die Personen dabei helfen kann, ihre Karriere auf die nächste Stufe zu bringen.

Google Professional Machine Learning Engineer Professional-Machine-Learning-Engineer Prüfungsfragen mit Lösungen (Q170-Q175):

170. Frage
Your team is working on an NLP research project to predict political affiliation of authors based on articles they have written. You have a large training dataset that is structured like this:

You followed the standard 80%-10%-10% data distribution across the training, testing, and evaluation subsets. How should you distribute the training examples across the train-test-eval subsets while maintaining the 80-10-10 proportion?

Antwort: A

Begründung:
The best way to distribute the training examples across the train-test-eval subsets while maintaining the 80-
10-10 proportion is to use option C. This option ensures that each subset contains a balanced and representative sample of the different classes (Democrat and Republican) and the different authors. This way, the model can learn from a diverse and comprehensive set of articles and avoid overfitting or underfitting.
Option C also avoids the problem of data leakage, which occurs when the same author appears in more than one subset, potentially biasing the model and inflating its performance. Therefore, option C is the most suitable technique for this use case.

171. Frage
You are developing ML models with Al Platform for image segmentation on CT scans. You frequently update your model architectures based on the newest available research papers, and have to rerun training on the same dataset to benchmark their performance. You want to minimize computation costs and manual intervention while having version control for your code. What should you do?

A. Use the gcloud command-line tool to submit training jobs on Al Platform when you update your code
B. Use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository
C. Use Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job
D. Create an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor.

Antwort: B

Begründung:
Developing ML models with AI Platform for image segmentation on CT scans requires a lot of computation and experimentation, as image segmentation is a complex and challenging task that involves assigning a label to each pixel in an image. Image segmentation can be used for various medical applications, such as tumor detection, organ segmentation, or lesion localization1 To minimize the computation costs and manual intervention while having version control for the code, one should use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository. Cloud Build is a service that executes your builds on Google Cloud Platform infrastructure. Cloud Build can import source code from Cloud Source Repositories, Cloud Storage, GitHub, or Bitbucket, execute a build to your specifications, and produce artifacts such as Docker containers or Java archives2 Cloud Build allows you to set up automated triggers that start a build when changes are pushed to a source code repository. You can configure triggers to filter the changes based on the branch, tag, or file path3 Cloud Source Repositories is a service that provides fully managed private Git repositories on Google Cloud Platform. Cloud Source Repositories allows you to store, manage, and track your code using the Git version control system. You can also use Cloud Source Repositories to connect to other Google Cloud services, such as Cloud Build, Cloud Functions, or Cloud Run4 To use Cloud Build linked with Cloud Source Repositories to trigger retraining when new code is pushed to the repository, you need to do the following steps:
* Create a Cloud Source Repository for your code, and push your code to the repository. You can use the Cloud SDK, Cloud Console, or Cloud Source Repositories API to create and manage your repository5
* Create a Cloud Build trigger for your repository, and specify the build configuration and the trigger settings. You can use the Cloud SDK, Cloud Console, or Cloud Build API to create and manage your trigger.
* Specify the steps of the build in a YAML or JSON file, such as installing the dependencies, running the tests, building the container image, and submitting the training job to AI Platform. You can also use the Cloud Build predefined or custom build steps to simplify your build configuration.
* Push your new code to the repository, and the trigger will start the build automatically. You can monitor the status and logs of the build using the Cloud SDK, Cloud Console, or Cloud Build API.
The other options are not as easy or feasible. Using Cloud Functions to identify changes to your code in Cloud Storage and trigger a retraining job is not ideal, as Cloud Functions has limitations on the memory, CPU, and execution time, and does not provide a user interface for managing and tracking your builds. Using the gcloud command-line tool to submit training jobs on AI Platform when you update your code is not optimal, as it requires manual intervention and does not leverage the benefits of Cloud Build and its integration with Cloud Source Repositories. Creating an automated workflow in Cloud Composer that runs daily and looks for changes in code in Cloud Storage using a sensor is not relevant, as Cloud Composer is mainly designed for orchestrating complex workflows across multiple systems, and does not provide a version control system for your code.
References: 1: Image segmentation 2: Cloud Build overview 3: Creating and managing build triggers 4: Cloud Source Repositories overview 5: Quickstart: Create a repository : [Quickstart: Create a build trigger] :
[Configuring builds] : [Viewing build results]

172. Frage
You are training an object detection model using a Cloud TPU v2. Training time is taking longer than expected. Based on this simplified trace obtained with a Cloud TPU profile, what action should you take to decrease training time in a cost-efficient way?

A. Move from Cloud TPU v2 to Cloud TPU v3 and increase batch size.
B. Rewrite your input function using parallel reads, parallel processing, and prefetch.
C. Rewrite your input function to resize and reshape the input images.
D. Move from Cloud TPU v2 to 8 NVIDIA V100 GPUs and increase batch size.

Antwort: B

Begründung:
The trace in the question shows that the training time is taking longer than expected. This is likely due to the input function not being optimized. To decrease training time in a cost-efficient way, the best option is to rewrite the input function using parallel reads, parallel processing, and prefetch. This will allow the model to process the data more efficiently and decrease training time. References:
* [Cloud TPU Performance Guide]
* [Data input pipeline performance guide]

173. Frage
You are developing an ML model that uses sliced frames from video feed and creates bounding boxes around specific objects. You want to automate the following steps in your training pipeline: ingestion and preprocessing of data in Cloud Storage, followed by training and hyperparameter tuning of the object model using Vertex AI jobs, and finally deploying the model to an endpoint. You want to orchestrate the entire pipeline with minimal cluster management. What approach should you use?

A. Use Kubeflow Pipelines on Google Kubernetes Engine.
B. Use Vertex AI Pipelines with Kubeflow Pipelines SDK.
C. Use Vertex AI Pipelines with TensorFlow Extended (TFX) SDK.
D. Use Cloud Composer for the orchestration.

Antwort: C

Begründung:
Option A is incorrect because using Kubeflow Pipelines on Google Kubernetes Engine is not the most convenient way to orchestrate the entire pipeline with minimal cluster management. Kubeflow Pipelines is an open-source platform that allows you to build, run, and manage ML pipelines using containers1. Google Kubernetes Engine is a service that allows you to create and manage clusters of virtual machines that run Kubernetes, an open-source system for orchestrating containerized applications2. However, this option requires more effort and resources than option B, as it involves creating and configuring the clusters, installing and maintaining Kubeflow Pipelines, and writing and running the pipeline code.
Option B is correct because using Vertex AI Pipelines with TensorFlow Extended (TFX) SDK is the best way to orchestrate the entire pipeline with minimal cluster management. Vertex AI Pipelines is a service that allows you to create and run scalable and portable ML pipelines on Google Cloud3. TensorFlow Extended (TFX) is a framework that provides a set of components and libraries for building production-ready ML pipelines using TensorFlow4. You can use Vertex AI Pipelines with TFX SDK to ingest and preprocess the data in Cloud Storage, train and tune the object model using Vertex AI jobs, and deploy the model to an endpoint, using predefined or custom components. Vertex AI Pipelines handles the underlying infrastructure and orchestration for you, so you don't need to worry about cluster management or scalability.
Option C is incorrect because using Vertex AI Pipelines with Kubeflow Pipelines SDK is not the most suitable way to orchestrate the entire pipeline with minimal cluster management. Kubeflow Pipelines SDK is a library that allows you to build and run ML pipelines using Kubeflow Pipelines5. You can use Vertex AI Pipelines with Kubeflow Pipelines SDK to create and run ML pipelines on Google Cloud, using containers. However, this option is less convenient and consistent than option B, as it requires you to use different APIs and tools for different steps of the pipeline, such as Vertex AI SDK for training and deployment, and Kubeflow Pipelines SDK for ingestion and preprocessing. Moreover, this option does not leverage the benefits of TFX, such as the standard components, the metadata store, or the ML Metadata library.
Option D is incorrect because using Cloud Composer for the orchestration is not the most efficient way to orchestrate the entire pipeline with minimal cluster management. Cloud Composer is a service that allows you to create and run workflows using Apache Airflow, an open-source platform for orchestrating complex tasks. You can use Cloud Composer to orchestrate the entire pipeline, by creating and managing DAGs (directed acyclic graphs) that define the dependencies and order of the tasks. However, this option is more complex and costly than option B, as it involves creating and configuring the environments, installing and maintaining Airflow, and writing and running the DAGs.
Reference:
Kubeflow Pipelines documentation
Google Kubernetes Engine documentation
Vertex AI Pipelines documentation
TensorFlow Extended documentation
Kubeflow Pipelines SDK documentation
[Cloud Composer documentation]
[Vertex AI documentation]
[Cloud Storage documentation]
[TensorFlow documentation]

174. Frage
You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?

A. Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.
B. Write a Cloud Functions script that launches a training and deploying job on Ai Platform that is triggered by Cloud Scheduler
C. Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model
D. Use a model trained and deployed on BigQuery ML and trigger retraining with the scheduled query feature in BigQuery

Antwort: A

Begründung:
The end-to-end architecture of the predictive model for estimating delay times for multiple transportation routes should be configured using Kubeflow Pipelines. Kubeflow Pipelines is a platform for building and deploying scalable, portable, and reusable machine learning pipelines on Kubernetes. Kubeflow Pipelines allows you to orchestrate your multi-step workflow from data preparation, model training, model evaluation, model deployment, and model serving. Kubeflow Pipelines also provides a user interface for managing and tracking your pipeline runs, experiments, and artifacts1 Using Kubeflow Pipelines has several advantages for this use case:
Full automation: You can define your pipeline as a Python script that specifies the steps and dependencies of your workflow, and use the Kubeflow Pipelines SDK to compile and upload your pipeline to the Kubeflow Pipelines service. You can also use the Kubeflow Pipelines UI to create, run, and monitor your pipeline2 Scalability: You can leverage the power of Kubernetes to scale your pipeline components horizontally and vertically, and use distributed training frameworks such as TensorFlow or PyTorch to train your model on multiple nodes or GPUs3 Portability: You can package your pipeline components as Docker containers that can run on any Kubernetes cluster, and use the Kubeflow Pipelines SDK to export and import your pipeline packages across different environments4 Reusability: You can reuse your pipeline components across different pipelines, and share your components with other users through the Kubeflow Pipelines Component Store. You can also use pre-built components from the Kubeflow Pipelines library or other sources5 Schedulability: You can use the Kubeflow Pipelines UI or the Kubeflow Pipelines SDK to schedule recurring pipeline runs based on cron expressions or intervals. For example, you can schedule your pipeline to run every month to retrain your model on the latest data.
The other options are not as suitable for this use case. Using a model trained and deployed on BigQuery ML is not recommended, as BigQuery ML is mainly designed for simple and quick machine learning tasks on large-scale data, and does not support complex models or custom code. Writing a Cloud Functions script that launches a training and deploying job on AI Platform is not ideal, as Cloud Functions has limitations on the memory, CPU, and execution time, and does not provide a user interface for managing and tracking your pipeline. Using Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model is not optimal, as Dataflow is mainly designed for data processing and streaming analytics, and does not support model serving or monitoring.

175. Frage
......

Professional-Machine-Learning-Engineer Kostenlos Downloden: https://www.pruefungfrage.de/Professional-Machine-Learning-Engineer-dumps-deutsch.html

BONUS!!! Laden Sie die vollständige Version der PrüfungFrage Professional-Machine-Learning-Engineer Prüfungsfragen kostenlos herunter: https://drive.google.com/open?id=1B5VrMuo77kQvNbEpTR7xLPsrLgkFYmJt