Neueste Google Professional Machine Learning Engineer Prüfung pdf & Professional-Machine-Learning-Engineer Prüfung Torrent

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Mit einem Google Professional-Machine-Learning-Engineer Zertifikat kann der Berufstätige in der IT-Branche bessere berufliche Aufstiegschancen haben. Das Google Professional-Machine-Learning-Engineer Zertifikat ebnet den Berufstätigen in der IT-Branche den Weg zur erfolgreichen Karriere!

Google Professional-Machine-Learning-Engineer Exam Overview:

Certification Vendor:	Google Cloud
Exam Name:	Google Cloud Professional Machine Learning Engineer Certification Exam
Exam Number:	Professional-Machine-Learning-Engineer
Passing Score:	Not officially published, approximately 70%
Certificate Validity Period:	2 years
Available Languages:	English, Japanese
Exam Price:	$200 USD (plus tax where applicable)
Real Exam Qty:	50-60
Related Certifications:	Google Cloud Professional Cloud Architect Google Cloud Professional Data Engineer
Exam Format:	Multiple select, Multiple choice
Exam Duration:	120 minutes
Recommended Training:	Official Exam Guide Google Cloud Skills Boost - Professional Machine Learning Engineer Learning Path
Exam Registration:	Google Cloud Certification Registration
Sample Questions:	Google Professional-Machine-Learning-Engineer Sample Questions
Exam Way:	Online-proctored remote exam or onsite-proctored exam at authorized test centers
Pre Condition:	No mandatory prerequisites; recommended 3+ years industry experience including 1+ year designing/managing Google Cloud solutions
Official Syllabus URL:	https://cloud.google.com/learn/certification/machine-learning-engineer

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Professional-Machine-Learning-Engineer Zertifizierung - Professional-Machine-Learning-Engineer Originale Fragen

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Die Zertifizierungsprüfung für maschinelles Lernen von Google Professional Lernen ist eine professionelle Zertifizierung, die das Wissen und die Fähigkeiten von Einzelpersonen im Bereich des maschinellen Lernens testet. Die Prüfung soll die Kandidatenkenntnisse in verschiedenen Aspekten des maschinellen Lernens bewerten, einschließlich Datenverarbeitung, Modellierung und Bereitstellung. Die Zertifizierung wird von Google Cloud, einer Tochtergesellschaft von Google, angeboten, die Unternehmen und Einzelpersonen Cloud -Computerdienste anbietet.

Google Professional Machine Learning Engineer Professional-Machine-Learning-Engineer Prüfungsfragen mit Lösungen (Q295-Q300):

295. Frage
You are creating a model training pipeline to predict sentiment scores from text-based product reviews. You want to have control over how the model parameters are tuned, and you will deploy the model to an endpoint after it has been trained You will use Vertex Al Pipelines to run the pipeline You need to decide which Google Cloud pipeline components to use What components should you choose?

Antwort: B

Begründung:
According to the web search results, Vertex AI Pipelines is a serverless orchestrator for running ML pipelines, using either the KFP SDK or TFX1. Vertex AI Pipelines provides a set of prebuilt components that can be used to perform common ML tasks, such as training, evaluation, deployment, and more2. Vertex AI ModelEvaluationOp and ModelDeployOp are two such components that can be used to evaluate and deploy a model to an endpoint for online inference3. However, Vertex AI Pipelines does not provide a prebuilt component for hyperparameter tuning. Therefore, to have control over how the model parameters are tuned, you need to use a custom component that calls the Vertex AI HyperparameterTuningJob service4. Therefore, option A is the best way to decide which Google Cloud pipeline components to use for the given use case, as it includes a custom component for hyperparameter tuning, and prebuilt components for model evaluation and deployment. The other options are not relevant or optimal for this scenario. References:
* Vertex AI Pipelines
* Google Cloud Pipeline Components
* Vertex AI ModelEvaluationOp and ModelDeployOp
* Vertex AI HyperparameterTuningJob
* Google Professional Machine Learning Certification Exam 2023
* Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

296. Frage
During batch training of a neural network, you notice that there is an oscillation in the loss. How should you adjust your model to ensure that it converges?

A. Decrease the size of the training batch
B. Increase the learning rate hyperparameter
C. Decrease the learning rate hyperparameter
D. Increase the size of the training batch

Antwort: B

Begründung:
https://developers.google.com/machine-learning/crash-course/introduction-to-neural-networks/playground-exercises

297. Frage
You work at an ecommerce startup. You need to create a customer churn prediction model. Your company's recent sales records are stored in a BigQuery table. You want to understand how your initial model is making predictions. You also want to iterate on the model as quickly as possible while minimizing cost. How should you build your first model?

A. Export the data to a Cloud Storage bucket. Create a tf.data.Dataset to read the data from Cloud Storage. Implement a deep neural network in TensorFlow.
B. Export the data to a Cloud Storage bucket. Load the data into a pandas DataFrame on Vertex AI Workbench and train a logistic regression model with scikit-learn.
C. Create a tf.data.Dataset by using the TensorFlow BigQueryClient. Implement a deep neural network in TensorFlow.
D. Prepare the data in BigQuery and associate the data with a Vertex AI dataset. Create an AutoMLTabularTrainingJob to tram a classification model.

Antwort: D

298. Frage
You developed a Python module by using Keras to train a regression model. You developed two model architectures, linear regression and deep neural network (DNN), within the same module.
You are using the training_methodargument to select one of the two methods, and you are using the learning_rateand num_hidden_layersarguments in the DNN. You plan to use Vertex AI's hypertuning service with a budget to perform 100 trials. You want to identify the model architecture and hyperparameter values that minimize training loss and maximize model performance. What should you do?

A. Run one hypertuning job for 100 trials. Set num_hidden_layersas a conditional hyperparameter based on its parent hyperparameter training_method, and set learning_rateas a non-conditional hyperparameter.
B. Run two separate hypertuning jobs, a linear regression job for 50 trials, and a DNN job for 50 trials. Compare their final performance on a common validation set, and select the set of hyperparameters with the least training loss.
C. Run one hypertuning job for 100 trials. Set num_hidden_layersand learning_rateas conditional hyperparameters based on their parent hyperparameter training_method.
D. Run one hypertuning job with training_methodas the hyperparameter for 50 trials. Select the architecture with the lowest training loss, and further hypertune it and its corresponding hyperparameters tor 50 trials.

Antwort: A

Begründung:
This approach allows you to explore both model architectures (linear regression and DNN) within a single hypertuning job while effectively managing hyperparameters. By setting num_hidden_layersas a conditional hyperparameter based on training_method, you ensure that the DNN-specific hyperparameters are only considered when the DNN architecture is selected. The learning_ratecan be tuned across both architectures as a non-conditional hyperparameter. This strategy maximizes the use of the 100 trials to identify the optimal architecture and hyperparameters while minimizing training loss and maximizing model performance.

299. Frage
You work as an ML engineer at a social media company, and you are developing a visual filter for users' profile photos. This requires you to train an ML model to detect bounding boxes around human faces. You want to use this filter in your company's iOS-based mobile phone application. You want to minimize code development and want the model to be optimized for inference on mobile phones. What should you do?

A. Train a model using AutoML Vision and use the "export for Coral" option.
B. Train a custom TensorFlow model and convert it to TensorFlow Lite (TFLite).
C. Train a model using AutoML Vision and use the "export for Core ML" option.
D. Train a model using AutoML Vision and use the "export for TensorFlow.js" option.

Antwort: C

Begründung:
AutoML Vision is a Google Cloud service that allows you to train custom ML models for image classification, object detection, and segmentation without writing any code. You can use AutoML Visionto upload your training data, label it, and train a model using a graphical user interface. You can also evaluate the model's performance and export it for deployment. One of the export options is Core ML, which is a framework that lets you integrate ML models into iOS applications. Core ML optimizes the model for on-device performance, power efficiency, and minimal memory footprint. By using AutoML Vision and Core ML, you can minimize code development and have a model that is optimized for inference on mobile phones.
References:
* AutoML Vision documentation
* Core ML documentation

300. Frage
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