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The Google Professional-Machine-Learning-Engineer exam covers a wide range of topics, including data preparation, model development, model deployment, and monitoring and maintenance of machine learning solutions. It is designed to test the knowledge and skills required to design, implement, and maintain machine learning solutions using Google Cloud. Professional-Machine-Learning-Engineer Exam is intended for professionals who have a strong background in machine learning, data science, or related fields, and who are looking to demonstrate their expertise to potential employers.

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To earn this certification, candidates must pass a rigorous exam that covers a wide range of topics related to machine learning and cloud computing. Professional-Machine-Learning-Engineer Exam consists of multiple-choice and scenario-based questions, and candidates are given two and a half hours to complete the exam. Professional-Machine-Learning-Engineer exam is administered online and can be taken from anywhere in the world. Upon passing the exam, candidates will receive a digital badge that they can display on their LinkedIn profile, resume, or website, indicating that they have demonstrated proficiency in the field of machine learning and the Google Cloud Platform. Google Professional Machine Learning Engineer certification is recognized by industry professionals and can help individuals advance their careers in the field of machine learning and cloud computing.

Google Professional Machine Learning Engineer Sample Questions (Q115-Q120):

NEW QUESTION # 115
You developed a Vertex Al ML pipeline that consists of preprocessing and training steps and each set of steps runs on a separate custom Docker image Your organization uses GitHub and GitHub Actions as CI/CD to run unit and integration tests You need to automate the model retraining workflow so that it can be initiated both manually and when a new version of the code is merged in the main branch You want to minimize the steps required to build the workflow while also allowing for maximum flexibility How should you configure the CI/CD workflow?

A. Trigger GitHub Actions to run the tests build custom Docker images push the images to Artifact Registry, and launch the pipeline in Vertex Al Pipelines.
B. Trigger GitHub Actions to run the tests launch a job on Cloud Run to build custom Docker images push the images to Artifact Registry and launch the pipeline in Vertex Al Pipelines.
C. Trigger GitHub Actions to run the tests launch a Cloud Build workflow to build custom Dicker images, push the images to Artifact Registry, and launch the pipeline in Vertex Al Pipelines.
D. Trigger a Cloud Build workflow to run tests build custom Docker images, push the images to Artifact Registry and launch the pipeline in Vertex Al Pipelines.

Answer: A

NEW QUESTION # 116
You are developing an ML model to identify your company s products in images. You have access to over one million images in a Cloud Storage bucket. You plan to experiment with different TensorFlow models by using Vertex Al Training You need to read images at scale during training while minimizing data I/O bottlenecks What should you do?

A. Load the images directly into the Vertex Al compute nodes by using Cloud Storage FUSE Read the images by using the tf .data.Dataset.from_tensor_slices function.
B. Create a Vertex Al managed dataset from your image data Access the aip_training_data_uri environment variable to read the images by using the tf. data. Dataset. Iist_flies function.
C. Store the URLs of the images in a CSV file Read the file by using the tf.data.experomental.CsvDataset function.
D. Convert the images to TFRecords and store them in a Cloud Storage bucket Read the TFRecords by using the tf. ciata.TFRecordDataset function.

Answer: D

Explanation:
TFRecords are a binary file format that can store large amounts of data efficiently. By converting the images to TFRecords and storing them in a Cloud Storage bucket, you can reduce the data size and improve the data transfer speed. You can then read the TFRecords by using the tf.data.TFRecordDataset function, which creates a dataset of tensors from the TFRecord files. This way, you can read images at scale during training while minimizing data I/O bottlenecks. References:
* TFRecord documentation
* tf.data.TFRecordDataset documentation
* Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate

NEW QUESTION # 117
You have developed a BigQuery ML model that predicts customer churn and deployed the model to Vertex Al Endpoints. You want to automate the retraining of your model by using minimal additional code when model feature values change. You also want to minimize the number of times that your model is retrained to reduce training costs. What should you do?

A. 1. Create a Vertex Al Model Monitoring job configured to monitor training/serving skew
2. Configure alert monitoring to publish a message to a Pub/Sub queue when a monitoring alert is detected
3. Use a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery.
B. 1. Enable request-response logging on Vertex Al Endpoints.
2 Schedule a TensorFlow Data Validation job to monitor prediction drift
3. Execute model retraining if there is significant distance between the distributions.
C. 1 Create a Vertex Al Model Monitoring job configured to monitor prediction drift.
2. Configure alert monitoring to publish a message to a Pub/Sub queue when a monitonng alert is detected.
3. Use a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery
D. 1. Enable request-response logging on Vertex Al Endpoints
2. Schedule a TensorFlow Data Validation job to monitor training/serving skew
3. Execute model retraining if there is significant distance between the distributions

Answer: C

Explanation:
The best option for automating the retraining of your model by using minimal additional code when model feature values change, and minimizing the number of times that your model is retrained to reduce training costs, is to create a Vertex AI Model Monitoring job configured to monitor prediction drift, configure alert monitoring to publish a message to a Pub/Sub queue when a monitoring alert is detected, and use a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery. This option allows you to leverage the power and simplicity of Vertex AI, Pub/Sub, and Cloud Functions to monitor your model performance and retrain your model when needed. 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 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. Prediction drift is a type of model monitoring metric that measures the difference between the distributions of the predictions generated by the model on the training data and the predictions generated by the model on the online data. Prediction drift can indicate that the model performance is degrading, or that the online data is changing over time. By creating a Vertex AI Model Monitoring job configured to monitor prediction drift, you can track the changes in the model predictions, and compare them with the expected predictions. Alert monitoring is a feature of Vertex AI Model Monitoring that can notify you when a monitoring metric exceeds a predefined threshold. Alert monitoring can help you set up rules and conditions for triggering alerts, and choose the notification channel for receiving alerts. Pub/Sub is a service that can provide reliable and scalable messaging and event streaming on Google Cloud. Pub/Sub can help you publish and subscribe to messages, and deliver them to various Google Cloud services, such as Cloud Functions. A Pub/Sub queue is a resource that can hold messages that are published to a Pub/Sub topic. A Pub/Sub queue can help you store and manage messages, and ensure that they are delivered to the subscribers. By configuring alert monitoring to publish a message to a Pub/Sub queue when a monitoring alert is detected, you can send a notification to a Pub/Sub topic, and trigger a downstream action based on the alert. Cloud Functions is a service that can run your stateless code in response to events on Google Cloud. Cloud Functions can help you create and execute functions without provisioning or managing servers, and pay only for the resources you use. A Cloud Function is a resource that can execute a piece of code in response to an event, such as a Pub/Sub message. A Cloud Function can help you perform various tasks, such as data processing, data transformation, or data analysis. BigQuery is a service that can store and query large-scale data on Google Cloud. BigQuery can help you analyze your data by using SQL queries, and perform various tasks, such as data exploration, data transformation, or data visualization. BigQuery ML is a feature of BigQuery that can create and execute machine learning models in BigQuery by using SQL queries.
BigQuery ML can help you build and train various types of models, such as linear regression, logistic regression, k-means clustering, matrix factorization, and deep neural networks. By using a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery, you can automate the retraining of your model by using minimal additional code when model feature values change. You can write a Cloud Function that listens to the Pub/Sub queue, and executes a SQL query to retrain your model in BigQuery ML when a prediction drift alert is received. By retraining your model in BigQuery ML, you can update your model parameters and improve your model performance and accuracy1.
The other options are not as good as option C, for the following reasons:
* Option A: Enabling request-response logging on Vertex AI Endpoints, scheduling a TensorFlow Data Validation job to monitor prediction drift, and executing model retraining if there is significant distance between the distributions would require more skills and steps than creating a Vertex AI Model Monitoring job configured to monitor prediction drift, configuring alert monitoring to publish a message to a Pub/Sub queue when a monitoring alert is detected, and using a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery. Request-response logging is a feature of Vertex AI Endpoints that can record the requests and responses that are sent to and from the online prediction endpoint. Request-response logging can help you collect and analyze the online prediction data, and troubleshoot any issues with your model. TensorFlow Data Validation is a tool that can analyze and validate your data for machine learning. TensorFlow Data Validation can help you explore, understand, and clean your data, and detect various data issues, such as data drift, data skew, or data anomalies.
Prediction drift is a type of data issue that measures the difference between the distributions of the predictions generated by the model on the training data and the predictions generated by the model on the online data. Prediction drift can indicate that the model performance is degrading, or that the online data is changing over time. By enabling request-response logging on Vertex AI Endpoints, and scheduling a TensorFlow Data Validation job to monitor prediction drift, you can collect and analyze the online prediction data, and compare the distributions of the predictions. However, enabling request-response logging on Vertex AI Endpoints, scheduling a TensorFlow Data Validation job to monitor prediction drift, and executing model retraining if there is significant distance between the distributions would require more skills and steps than creating a Vertex AI Model Monitoring job configured to monitor prediction drift, configuring alert monitoring to publish a message to a Pub/Sub queue when a monitoring alert is detected, and using a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery. You would need to write code, enable and configure the request-response logging, create and run the TensorFlow Data Validation job, define and measure the distance between the distributions, and execute the model retraining. Moreover, this option would not automate the retraining of your model, as you would need to manually check the prediction drift and trigger the retraining2.
* Option B: Enabling request-response logging on Vertex AI Endpoints, scheduling a TensorFlow Data Validation job to monitor training/serving skew, and executing model retraining if there is significant distance between the distributions would not help you monitor the changes in the model feature values, and could cause errors or poor performance. Training/serving skew is a type of data issue that measures
* the difference between the distributions of the features used to train the model and the features used to serve the model. Training/serving skew can indicate that the model is not trained on the representative data, or that the data is changing over time. By enabling request-response logging on Vertex AI Endpoints, and scheduling a TensorFlow Data Validation job to monitor training/serving skew, you can collect and analyze the online prediction data, and compare the distributions of the features. However, enabling request-response logging on Vertex AI Endpoints, scheduling a TensorFlow Data Validation job to monitor training/serving skew, and executing model retraining if there is significant distance between the distributions would not help you monitor the changes in the model feature values, and could cause errors or poor performance. You would need to write code, enable and configure the request-response logging, create and run the TensorFlow Data Validation job, define and measure the distance between the distributions, and execute the model retraining. Moreover, this option would not monitor the prediction drift, which is a more direct and relevant metric for measuring the model performance and quality2.
* Option D: Creating a Vertex AI Model Monitoring job configured to monitor training/serving skew, configuring alert monitoring to publish a message to a Pub/Sub queue when a monitoring alert is detected, and using a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery would not help you monitor the changes in the model feature values, and could cause errors or poor performance. Training/serving skew is a type of data issue that measures the difference between the distributions of the features used to train the model and the features used to serve the model.
Training/serving 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 configured to monitor training/serving skew, you can track the changes in the model features, and compare them with the expected features. However, creating a Vertex AI Model Monitoring job configured to monitor training/serving skew, configuring alert monitoring to publish a message to a Pub/Sub queue when a monitoring alert is detected, and using a Cloud Function to monitor the Pub/Sub queue, and trigger retraining in BigQuery would not help you monitor the changes in the model feature values, and could cause errors or poor performance. You would need to write code, create and configure the Vertex AI Model Monitoring job, configure the alert monitoring, create and configure the Pub/Sub queue, and write a Cloud Function to trigger the retraining. Moreover, this option would not monitor the prediction drift, which is a more direct and relevant metric for measuring the model performance and quality1.
References:
* Preparing for Google Cloud Certification: Machine Learning Engineer, Course 3: Production ML Systems, Week 4: ML Governance
* Google Cloud Professional Machine Learning Engineer Exam Guide, Section 3: Scaling ML models in production

NEW QUESTION # 118
You need to train a computer vision model that predicts the type of government ID present in a given image using a GPU-powered virtual machine on Compute Engine. You use the following parameters:
* Optimizer: SGD
* Image shape = 224x224
* Batch size = 64
* Epochs = 10
* Verbose = 2
During training you encounter the following error: ResourceExhaustedError: out of Memory (oom) when allocating tensor. What should you do?

A. Change the learning rate
B. Reduce the batch size
C. Reduce the image shape
D. Change the optimizer

Answer: B

NEW QUESTION # 119
You need to train a ControlNet model with Stable Diffusion XL for an image editing use case. You want to train this model as quickly as possible. Which hardware configuration should you choose to train your model?

A. Configure one a2-highgpu-1g instance with an NVIDIA A100 GPU with 80 GB of RAM. Use float32 precision during model training.
B. Configure four n1-standard-16 instances, each with one NVIDIA Tesla T4 GPU with 16 GB of RAM.Use float16 quantization during model training.
C. Configure one a2-highgpu-1g instance with an NVIDIA A100 GPU with 80 GB of RAM. Use bfloat16 quantization during model training.
D. Configure four n1-standard-16 instances, each with one NVIDIA Tesla T4 GPU with 16 GB of RAM.
Use float32 precision during model training.

Answer: A

Explanation:
NVIDIA A100 GPUs are optimized for training complex models like Stable Diffusion XL. Using float32 precision ensures high model accuracy during training, whereas float16 or bfloat16 may cause lower precision in gradients, especially important for image editing. Distributing across multiple instances with T4 GPUs (Options C and D) would not speed up the process effectively due to lower power and more complex setup requirements.

NEW QUESTION # 120
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