Tony White Tony White's صفحة الملف الشخصي

Tony White Tony White

0 دورة ملتحَق بها • 0 اكتملت الدورة

سيرة شخصية

Die neuesten Professional-Machine-Learning-Engineer echte Prüfungsfragen, Google Professional-Machine-Learning-Engineer originale fragen

ExamFragen ist führend in der neuesten Google Professional-Machine-Learning-Engineer Zertifizierungsprüfung und Prüfungsvorbereitung. Unsere Ressourcen werden ständig überarbeitet und aktualisiert mit einer engenVerknüpfung. Wenn Sie sich heute auf die Google Professional-Machine-Learning-Engineer Zertifizierungsprüfung vorbereiten, sollen Sie bald die neueste Schulung beginnen und die nächste Prüfungsfragen bestehen. Weil die Mehrheit unserer Fragen monatlich aktualisiert ist, werden Sie die besten Ressourcen mit marktfrischer Qualität und Zuverlässigkeit bekommen.

Die neuesten Schulungsunterlagen zur Google Professional-Machine-Learning-Engineer (Google Professional Machine Learning Engineer) Zertifizierungsprüfung von ExamFragen sind von den Expertenteams bearbeitet, die vielen beim Verwirklichen ihres Traums verhelfen. In der konkurrenzfähigen Gesellschaft muss man die Fachleute seine eigenen Kenntinisse und Technikniveau unter Beweis stellen, um seine Position zu verstärken. Durch die Google Professional-Machine-Learning-Engineer Zertifizierungsprüfung kann man seine Fähigkeiten beweisen. Mit dem Google Professional-Machine-Learning-Engineer Zertifikat werden große Veränderungen in Ihrer Arbeit stattfinden. Ihr Gehalt wird erhöht und Sie werden sicher befördert.

>> Professional-Machine-Learning-Engineer Antworten <<

Professional-Machine-Learning-Engineer Kostenlos Downloden, Professional-Machine-Learning-Engineer Online Prüfungen

Wenn Sie die schwierige Google Professional-Machine-Learning-Engineer Zertifizierungsprüfung bestehen wollen, ist es unmöglich für Sie bei der Vorbereitung keine richtige Schulungsunterlagen benutzen. Wenn Sie die ausgezeichnete Lernhilfe finden wollen, sollen Sie an ExamFragen diese Prüfungsunterlagen suchen. Wir ExamFragen haben sehr guten Ruf und haben viele ausgezeichnete Dumps zur Google Professional-Machine-Learning-Engineer Prüfung. Und wir bieten kostenlose Demo aller verschieden Dumps. Wenn Sie suchen, ob ExamFragen Dumps für Sie geeignet sind, können Sie zuerst die Demo herunterladen und probieren.

Google Professional Machine Learning Engineer Professional-Machine-Learning-Engineer Prüfungsfragen mit Lösungen (Q116-Q121):

116. Frage
You work at a bank You have a custom tabular ML model that was provided by the bank's vendor. The training data is not available due to its sensitivity. The model is packaged as a Vertex Al Model serving container which accepts a string as input for each prediction instance. In each string the feature values are separated by commas. You want to deploy this model to production for online predictions, and monitor the feature distribution over time with minimal effort What should you do?

A. 1 Refactor the serving container to accept key-value pairs as input format.
2. Upload the model to Vertex Al Model Registry and deploy the model to a Vertex Al endpoint.
3. Create a Vertex Al Model Monitoring job with feature drift detection as the monitoring objective.
B. 1 Refactor the serving container to accept key-value pairs as input format.
2 Upload the model to Vertex Al Model Registry and deploy the model to a Vertex Al endpoint.
3. Create a Vertex Al Model Monitoring job with feature skew detection as the monitoring objective.
C. 1 Upload the model to Vertex Al Model Registry and deploy the model to a Vertex Al endpoint.
2 Create a Vertex Al Model Monitoring job with feature skew detection as the monitoring objective and provide an instance schema.
D. 1 Upload the model to Vertex Al Model Registry and deploy the model to a Vertex Ai endpoint.
2. Create a Vertex Al Model Monitoring job with feature drift detection as the monitoring objective, and provide an instance schema.

Antwort: D

Begründung:
The best option for deploying a custom tabular ML model to production for online predictions, and monitoring the feature distribution over time with minimal effort, using a model that was provided by the bank's vendor, the training data is not available due to its sensitivity, and the model is packaged as a Vertex AI Model serving container which accepts a string as input for each prediction instance, is to upload the model to Vertex AI Model Registry and deploy the model to a Vertex AI endpoint, create a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, and provide an instance schema. This option allows you to leverage the power and simplicity of Vertex AI to serve and monitor your model with minimal code and configuration. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can deploy a trained model to an online prediction endpoint, which can provide low-latency predictions for individual instances. Vertex AI can also provide various tools and services for data analysis, model development, model deployment, model monitoring, and model governance. A Vertex AI Model Registry is a resource that can store and manage your models on Vertex AI. A Vertex AI Model Registry can help you organize and track your models, and access various model information, such as model name, model description, and model labels. A Vertex AI Model serving container is a resource that can run your custom model code on Vertex AI. A Vertex AI Model serving container can help you package your model code and dependencies into a container image, and deploy the container image to an online prediction endpoint. A Vertex AI Model serving container can accept various input formats, such as JSON, CSV, or TFRecord. A string input format is a type of input format that accepts a string as input for each prediction instance. A string input format can help you encode your feature values into a single string, and separate them by commas. By uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, you can serve your model for online predictions with minimal code and configuration. You can use the Vertex AI API or the gcloud command-line tool to upload the model to Vertex AI Model Registry, and provide the model name, model description, and model labels. You can also use the Vertex AI API or the gcloud command-line tool to deploy the model to a Vertex AI endpoint, and provide the endpoint name, endpoint description, endpoint labels, and endpoint resources. A Vertex AI Model Monitoring job is a resource that can monitor the performance and quality of your deployed models on Vertex AI. A Vertex AI Model Monitoring job can help you detect and diagnose issues with your models, such as data drift, prediction drift, training/serving skew, or model staleness. Feature drift is a type of model monitoring metric that measures the difference between the distributions of the features used to train the model and the features used to serve the model over time. Feature drift can indicate that the online data is changing over time, and the model performance is degrading. By creating a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, and providing an instance schema, you can monitor the feature distribution over time with minimal effort. You can use the Vertex AI API or the gcloud command-line tool to create a Vertex AI Model Monitoring job, and provide the monitoring objective, the monitoring frequency, the alerting threshold, and the notification channel. You can also provide an instance schema, which is a JSON file that describes the features and their types in the prediction input data. An instance schema can help Vertex AI Model Monitoring parse and analyze the string input format, and calculate the feature distributions and distance scores1.
The other options are not as good as option A, for the following reasons:
Option B: Uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective, and providing an instance schema would not help you monitor the changes in the online data over time, and could cause errors or poor performance. Feature skew is a type of model monitoring metric that measures the difference between the distributions of the features used to train the model and the features used to serve the model at a given point in time. Feature skew can indicate that the model is not trained on the representative data, or that the data is changing over time. By creating a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective, and providing an instance schema, you can monitor the feature distribution at a given point in time with minimal effort. However, uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective, and providing an instance schema would not help you monitor the changes in the online data over time, and could cause errors or poor performance. You would need to use the Vertex AI API or the gcloud command-line tool to upload the model to Vertex AI Model Registry, deploy the model to a Vertex AI endpoint, create a Vertex AI Model Monitoring job, and provide an instance schema. Moreover, this option would not monitor the feature drift, which is a more direct and relevant metric for measuring the changes in the online data over time, and the model performance and quality1.
Option C: Refactoring the serving container to accept key-value pairs as input format, uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective would require more skills and steps than uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, and providing an instance schema. A key-value pair input format is a type of input format that accepts a key-value pair as input for each prediction instance. A key-value pair input format can help you specify the feature names and values in a JSON object, and separate them by colons. By refactoring the serving container to accept key-value pairs as input format, uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, you can serve and monitor your model with minimal code and configuration. You can write code to refactor the serving container to accept key-value pairs as input format, and use the Vertex AI API or the gcloud command-line tool to upload the model to Vertex AI Model Registry, deploy the model to a Vertex AI endpoint, and create a Vertex AI Model Monitoring job. However, refactoring the serving container to accept key-value pairs as input format, uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective would require more skills and steps than uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, and providing an instance schema. You would need to write code, refactor the serving container, upload the model to Vertex AI Model Registry, deploy the model to a Vertex AI endpoint, and create a Vertex AI Model Monitoring job. Moreover, this option would not use the instance schema, which is a JSON file that can help Vertex AI Model Monitoring parse and analyze the string input format, and calculate the feature distributions and distance scores1.
Option D: Refactoring the serving container to accept key-value pairs as input format, uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective would require more skills and steps than uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, and providing an instance schema, and would not help you monitor the changes in the online data over time, and could cause errors or poor performance. Feature skew is a type of model monitoring metric that measures the difference between the distributions of the features used to train the model and the features used to serve the model at a given point in time. Feature skew can indicate that the model is not trained on the representative data, or that the data is changing over time. By creating a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective, you can monitor the feature distribution at a given point in time with minimal effort. However, refactoring the serving container to accept key-value pairs as input format, uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature skew detection as the monitoring objective would require more skills and steps than uploading the model to Vertex AI Model Registry and deploying the model to a Vertex AI endpoint, creating a Vertex AI Model Monitoring job with feature drift detection as the monitoring objective, and providing an instance schema, and would not help you monitor the changes in the online data over time, and could cause errors or poor performance. You would need to write code, refactor the serving container, upload the model to Vertex AI Model Registry, deploy the model to a Vertex AI endpoint, and create a Vertex AI Model Monitoring job. Moreover, this option would not monitor the feature drift, which is a more direct and relevant metric for measuring the changes in the online data over time, and the model performance and quality1.
Reference:
Using Model Monitoring | Vertex AI | Google Cloud

117. Frage
You need to execute a batch prediction on 100 million records in a BigQuery table with a custom TensorFlow DNN regressor model, and then store the predicted results in a BigQuery table. You want to minimize the effort required to build this inference pipeline. What should you do?

A. Create a Dataflow pipeline to convert the data in BigQuery to TFRecords. Run a batch inference on Vertex AI Prediction, and write the results to BigQuery.
B. Use the TensorFlow BigQuery reader to load the data, and use the BigQuery API to write the results to BigQuery.
C. Load the TensorFlow SavedModel in a Dataflow pipeline. Use the BigQuery I/O connector with a custom function to perform the inference within the pipeline, and write the results to BigQuery.
D. Import the TensorFlow model with BigQuery ML, and run the ml.predict function.

Antwort: D

Begründung:
Option A is correct because importing the TensorFlow model with BigQuery ML, and running the ml.predict function is the easiest way to execute a batch prediction on a large BigQuery table with a custom TensorFlow model, and store the predicted results in another BigQuery table. BigQuery ML allows you to import TensorFlow models that are stored in Cloud Storage, and use them for prediction with SQL queries1. The ml.predict function returns a table with the predicted values, which can be saved to another BigQuery table2.
Option B is incorrect because using the TensorFlow BigQuery reader to load the data, and using the BigQuery API to write the results to BigQuery requires more effort to build the inference pipeline than option A. The TensorFlow BigQuery reader is a way to read data from BigQuery into TensorFlow datasets, which can be used for training or prediction3. However, this option also requires writing code to load the TensorFlow model, run the prediction, and use the BigQuery API to write the results back to BigQuery4.
Option C is incorrect because creating a Dataflow pipeline to convert the data in BigQuery to TFRecords, running a batch inference on Vertex AI Prediction, and writing the results to BigQuery requires more effort to build the inference pipeline than option A. Dataflow is a service for creating and running data processing pipelines, such as ETL (extract, transform, load) or batch processing5. Vertex AI Prediction is a service for deploying and serving ML models for online or batch prediction. However, this option also requires writing code to create the Dataflow pipeline, convert the data to TFRecords, run the batch inference, and write the results to BigQuery.
Option D is incorrect because loading the TensorFlow SavedModel in a Dataflow pipeline, using the BigQuery I/O connector with a custom function to perform the inference within the pipeline, and writing the results to BigQuery requires more effort to build the inference pipeline than option A. The BigQuery I/O connector is a way to read and write data from BigQuery within a Dataflow pipeline. However, this option also requires writing code to load the TensorFlow SavedModel, create the custom function for inference, and write the results to BigQuery.
Reference:
Importing models into BigQuery ML
Using imported models for prediction
TensorFlow BigQuery reader
BigQuery API
Dataflow overview
[Vertex AI Prediction overview]
[Batch prediction with Dataflow]
[BigQuery I/O connector]
[Using TensorFlow models in Dataflow]

118. Frage
You have developed a fraud detection model for a large financial institution using Vertex AI. The model achieves high accuracy, but stakeholders are concerned about potential bias based on customer demographics. You have been asked to provide insights into the model's decision-making process and identify any fairness issues. What should you do?

A. Compile a dataset of unfair predictions. Use Vertex AI Vector Search to identify similar data points in the model's predictions. Report these data points to the stakeholders.
B. Use feature attribution in Vertex AI to analyze model predictions and the impact of each feature on the model's predictions.
C. Enable Vertex AI Model Monitoring to detect training-serving skew. Configure an alert to send an email when the skew or drift for a model's feature exceeds a predefined threshold. Retrain the model by appending new data to existing training data.
D. Create feature groups using Vertex AI Feature Store to segregate customer demographic features and non-demographic features. Retrain the model using only non-demographic features.

Antwort: B

Begründung:
Feature attribution helps to determine how each feature influences predictions, essential for identifying bias. Vertex AI's built-in explainability tools provide insights without altering the model's feature space. Model monitoring (Option A) detects distributional drift rather than feature influence. Options B and D do not directly address the request to explain model decisions or provide fairness insights.

119. Frage
You are developing a Kubeflow pipeline on Google Kubernetes Engine. The first step in the pipeline is to issue a query against BigQuery. You plan to use the results of that query as the input to the next step in your pipeline. You want to achieve this in the easiest way possible. What should you do?

A. Use the BigQuery console to execute your query and then save the query results Into a new BigQuery table.
B. Write a Python script that uses the BigQuery API to execute queries against BigQuery Execute this script as the first step in your Kubeflow pipeline
C. Locate the Kubeflow Pipelines repository on GitHub Find the BigQuery Query Component, copy that component's URL, and use it to load the component into your pipeline. Use the component to execute queries against BigQuery
D. Use the Kubeflow Pipelines domain-specific language to create a custom component that uses the Python BigQuery client library to execute queries

Antwort: A

120. Frage
You have a demand forecasting pipeline in production that uses Dataflow to preprocess raw data prior to model training and prediction. During preprocessing, you employ Z-score normalization on data stored in BigQuery and write it back to BigQuery. New training data is added every week. You want to make the process more efficient by minimizing computation time and manual intervention. What should you do?

A. Normalize the data using Google Kubernetes Engine
B. Use the normalizer_fn argument in TensorFlow's Feature Column API
C. Normalize the data with Apache Spark using the Dataproc connector for BigQuery
D. Translate the normalization algorithm into SQL for use with BigQuery

Antwort: D

Begründung:
Z-score normalization is a technique that transforms the values of a numeric variable into standardized units, such that the mean is zero and the standard deviation is one. Z-score normalization can help to compare variables with different scales and ranges, and to reduce the effect of outliers and skewness. The formula for z-score normalization is:
z = (x - mu) / sigma
where x is the original value, mu is the mean of the variable, and sigma is the standard deviation of the variable.
Dataflow is a service that allows you to create and run data processing pipelines on Google Cloud. You can use Dataflow to preprocess raw data prior to model training and prediction, such as applying z-score normalization on data stored in BigQuery. However, using Dataflow for this task may not be the most efficient option, as it involves reading and writing data from and to BigQuery, which can be time-consuming and costly. Moreover, using Dataflow requires manual intervention to update the pipeline whenever new training data is added.
A more efficient way to perform z-score normalization on data stored in BigQuery is to translate the normalization algorithm into SQL and use it with BigQuery. BigQuery is a service that allows you to analyze large-scale and complex data using SQL queries. You can use BigQuery to perform z-score normalization on your data using SQL functions such as AVG(), STDDEV_POP(), and OVER(). For example, the following SQL query can normalize the values of a column called temperature in a table called weather:
SELECT (temperature - AVG(temperature) OVER ()) / STDDEV_POP(temperature) OVER () AS normalized_temperature FROM weather; By using SQL to perform z-score normalization on BigQuery, you can make the process more efficient by minimizing computation time and manual intervention. You can also leverage the scalability and performance of BigQuery to handle large and complex datasets. Therefore, translating the normalization algorithm into SQL for use with BigQuery is the best option for this use case.

121. Frage
......

Wenn Sie noch viel wertvolle Zeit und Energie für die Vorbereitung der Google Professional-Machine-Learning-Engineer Zertifizierungsprüfung benutzen und nicht wissen, wie man mühlos und effizient die Google Professional-Machine-Learning-Engineer Zertifizierungsprüfung bestehen kann, bieten jetzt ExamFragen Ihnen eine effektive Methode, um die Google Professional-Machine-Learning-Engineer Zertifizierungsprüfung zu bestehen. Mit ExamFragen würden Sie bessere Resultate bei weniger Einsatz erzielen.

Professional-Machine-Learning-Engineer Kostenlos Downloden: https://www.examfragen.de/Professional-Machine-Learning-Engineer-pruefung-fragen.html

Google Professional-Machine-Learning-Engineer Antworten In diesem Mall stehen Sie nicht weit hinter den anderen, Professional-Machine-Learning-Engineer----die Frucht der langzeitigen mühsamen Arbeit, Professional-Machine-Learning-Engineer PDF: Die von uns von Angfang an angebotene Professional-Machine-Learning-Engineer PDF Version ist immer die Beliebteste, Trotz der harten Wettbewerb können Sie sich auch abheben, falls Sie das Google Professional-Machine-Learning-Engineer-Zertifikat erfolgreich erhalten, Die praktische Google Professional-Machine-Learning-Engineer Trainings-Dumps werden aus vielen Fragenanalysen bearbeitet und verfeinert, was die echte Professional-Machine-Learning-Engineer Prüfung entspricht und für Sie wirklich vertrauenswürdig ist.

Sie prägten den Begriff industrielles Internet" um ihre Bemühungen und Märkte Professional-Machine-Learning-Engineer zu beschreiben, Damit stand ich natürlich ganz oben auf der Abschussliste, aber solange ich durchhielt, waren wir den Volturi mehr als ebenbürtig.

Professional-Machine-Learning-Engineer Pass4sure Dumps & Professional-Machine-Learning-Engineer Sichere Praxis Dumps

In diesem Mall stehen Sie nicht weit hinter den anderen, Professional-Machine-Learning-Engineer----die Frucht der langzeitigen mühsamen Arbeit, Professional-Machine-Learning-Engineer PDF: Die von uns von Angfang an angebotene Professional-Machine-Learning-Engineer PDF Version ist immer die Beliebteste.

Trotz der harten Wettbewerb können Sie sich auch abheben, falls Sie das Google Professional-Machine-Learning-Engineer-Zertifikat erfolgreich erhalten, Die praktische Google Professional-Machine-Learning-Engineer Trainings-Dumps werden aus vielen Fragenanalysen bearbeitet und verfeinert, was die echte Professional-Machine-Learning-Engineer Prüfung entspricht und für Sie wirklich vertrauenswürdig ist.