Free Google Professional-Machine-Learning-Engineer Test Questions | Professional-Machine-Learning-Engineer Test Prep

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

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

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Google Professional Machine Learning Engineer Sample Questions (Q288-Q293):

NEW QUESTION # 288
You have been asked to build a model using a dataset that is stored in a medium-sized (~10 GB) BigQuery table. You need to quickly determine whether this data is suitable for model development. You want to create a one-time report that includes both informative visualizations of data distributions and more sophisticated statistical analyses to share with other ML engineers on your team. You require maximum flexibility to create your report. What should you do?

A. Use the Google Data Studio to create the report.
B. Use the output from TensorFlow Data Validation on Dataflow to generate the report.
C. Use Dataprep to create the report.
D. Use Vertex AI Workbench user-managed notebooks to generate the report.

Answer: D

Explanation:
While Google Data Studio (Option B) is a powerful data visualization and reporting tool, it might not provide the same level of flexibility and sophistication for statistical analyses compared to a notebook environment.

NEW QUESTION # 289
You need to design a customized deep neural network in Keras that will predict customer purchases based on their purchase history. You want to explore model performance using multiple model architectures, store training data, and be able to compare the evaluation metrics in the same dashboard.
What should you do?

A. Run multiple training jobs on AI Platform with similar job names.
B. Create an experiment in Kubeflow Pipelines to organize multiple runs.
C. Automate multiple training runs using Cloud Composer.
D. Create multiple models using AutoML Tables.

Answer: B

Explanation:
With Kubeflow Pipelines, you can create experiments that help you keep track of multiple training runs with different model architectures and hyperparameters.
https://www.kubeflow.org/docs/about/use-cases/

NEW QUESTION # 290
You created a model that uses BigQuery ML to perform linear regression. You need to retrain the model on the cumulative data collected every week. You want to minimize the development effort and the scheduling cost. What should you do?

A. Use Cloud Scheduler to trigger a Cloud Function every week that runs the query for retraining the model.
B. Create a pipeline in Vertex Al Pipelines that executes the retraining query and use the Cloud Scheduler API to run the query weekly.
C. Use the BigQuery API Connector and Cloud Scheduler to trigger. Workflows every week that retrains the model.
D. Use BigQuerys scheduling service to run the model retraining query periodically.

Answer: B

NEW QUESTION # 291
You work with a team of researchers to develop state-of-the-art algorithms for financial analysis.
Your team develops and debugs complex models in TensorFlow. You want to maintain the ease of debugging while also reducing the model training time. How should you set up your training environment?

A. Configure a v3-8 TPU VM. SSH into the VM to train and debug the model.
B. Configure a v3-8 TPU node. Use Cloud Shell to SSH into the Host VM to train and debug the model.
C. Configure a n1 -standard-4 VM with 4 NVIDIA P100 GPUs. SSH into the VM and use ParameterServerStraregv to train the model.
D. Configure a n1-standard-4 VM with 4 NVIDIA P100 GPUs. SSH into the VM and use MultiWorkerMirroredStrategy to train the model.

Answer: D

NEW QUESTION # 292
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. 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.
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 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
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

Answer: C

Explanation:
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. Reference:
[Firebase Cloud Messaging]
[Firebase Cloud Messaging: Send messages to specific devices]

NEW QUESTION # 293
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