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

Section	Objectives
Deployment and operations	- Monitoring and maintenance 1. Retraining and lifecycle management 2. Monitor model drift and performance - Model deployment 1. Batch and online prediction systems 2. Deploy models using Vertex AI endpoints
ML model development	- Evaluation 1. Model validation strategies 2. Evaluate model performance metrics - Model training and tuning 1. Train models using TensorFlow / Vertex AI 2. Hyperparameter tuning and optimization
Designing ML solutions	- Framing ML problems 1. Define success metrics and evaluation criteria 2. Translate business problems into ML tasks - ML architecture design 1. Select appropriate ML models and approaches 2. Design scalable ML systems on GCP
ML pipeline automation and orchestration	- Pipeline design 1. Build end-to-end ML pipelines 2. Use Vertex AI Pipelines
Data preparation and processing	- Feature engineering 1. Transform and preprocess datasets 2. Feature selection and representation techniques - Data ingestion and pipelines 1. Build data pipelines for training and serving 2. Use BigQuery and data processing services

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Google Professional Machine Learning Engineer Sample Questions (Q146-Q151):

NEW QUESTION # 146
You work for a large retailer and you need to build a model to predict customer churn. The company has a dataset of historical customer data, including customer demographics, purchase history, and website activity. You need to create the model in BigQuery ML and thoroughly evaluate its performance. What should you do?

A. Create a logistic regression model in BigQuery ML Use the ml.confusion_matrix function to evaluate the model performance.
B. Create a logistic regression model in BigQuery ML and register the model in Vertex Al Model Registry. Evaluate the model performance in Vertex Al.
C. Create a linear regression model in BigQuery ML and register the model in Vertex Al Model Registry Evaluate the model performance in Vertex Al.
D. Create a linear regression model in BigQuery ML Use the ml. evaluate function to evaluate the model performance.

Answer: B

NEW QUESTION # 147
You work on a growing team of more than 50 data scientists who all use AI Platform. You are designing a strategy to organize your jobs, models, and versions in a clean and scalable way.
Which strategy should you choose?

A. Set up a BigQuery sink for Cloud Logging logs that is appropriately filtered to capture information about AI Platform resource usage. In BigQuery, create a SQL view that maps users to the resources they are using
B. Use labels to organize resources into descriptive categories.
Apply a label to each created resource so that users can filter the results by label when viewing or monitoring the resources.
C. Separate each data scientist's work into a different project to ensure that the jobs, models, and versions created by each data scientist are accessible only to that user.
D. Set up restrictive IAM permissions on the AI Platform notebooks so that only a single user or group can access a given instance.

Answer: B

Explanation:
As IAM roles are given to the entire AI Notebook resource, not to a specific instance.

NEW QUESTION # 148
You need to deploy a scikit-learn classification model to production. The model must be able to serve requests
24/7 and you expect millions of requests per second to the production application from 8 am to 7 pm. You need to minimize the cost of deployment What should you do?

A. Deploy an online Vertex Al prediction endpoint with one GPU per replica Set the max replica count to
1.
B. Deploy an online Vertex Al prediction endpoint Set the max replica count to 100
C. Deploy an online Vertex Al prediction endpoint Set the max replica count to 1
D. Deploy an online Vertex Al prediction endpoint with one GPU per replica Set the max replica count to
100.

Answer: B

Explanation:
The best option for deploying a scikit-learn classification model to production is to deploy an online Vertex AI prediction endpoint and set the max replica count to 100. This option allows you to leverage the power and scalability of Google Cloud to serve requests 24/7 and handle millions of requests per second. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can deploy a trained scikit-learn model to an online prediction endpoint, which can provide low-latency predictions for individual instances. An online prediction endpoint consists of one or more replicas, which are copies of the model that run on virtual machines. The max replica count is a parameter that determines the maximum number of replicas that can be created for the endpoint. By setting the max replica count to 100, you can enable the endpoint to scale up to 100 replicas when the traffic increases, and scale down to zero replicas when the traffic decreases. This can help minimize the cost of deployment, as you only pay for the resources that you use. Moreover, you can use the autoscaling algorithm option to optimize the scaling behavior of the endpoint based on the latency and utilization metrics1.
The other options are not as good as option B, for the following reasons:
* Option A: Deploying an online Vertex AI prediction endpoint and setting the max replica count to 1 would not be able to serve requests 24/7 and handle millions of requests per second. Setting the max replica count to 1 would limit the endpoint to only one replica, which can cause performance issues and service disruptions when the traffic increases. Moreover, setting the max replica count to 1 would prevent the endpoint from scaling down to zero replicas when the traffic decreases, which can increase the cost of deployment, as you pay for the resources that you do not use1.
* Option C: Deploying an online Vertex AI prediction endpoint with one GPU per replica and setting the max replica count to 1 would not be able to serve requests 24/7 and handle millions of requests per second, and would increase the cost of deployment. Adding a GPU to each replica would increase the
* computational power of the endpoint, but it would also increase the cost of deployment, as GPUs are more expensive than CPUs. Moreover, setting the max replica count to 1 would limit the endpoint to only one replica, which can cause performance issues and service disruptions when the traffic increases, and prevent the endpoint from scaling down to zero replicas when the traffic decreases1. Furthermore, scikit-learn models do not benefit from GPUs, as scikit-learn is not optimized for GPU acceleration2.
* Option D: Deploying an online Vertex AI prediction endpoint with one GPU per replica and setting the max replica count to 100 would be able to serve requests 24/7 and handle millions of requests per second, but it would increase the cost of deployment. Adding a GPU to each replica would increase the computational power of the endpoint, but it would also increase the cost of deployment, as GPUs are more expensive than CPUs. Setting the max replica count to 100 would enable the endpoint to scale up to 100 replicas when the traffic increases, and scale down to zero replicas when the traffic decreases, which can help minimize the cost of deployment. However, scikit-learn models do not benefit from GPUs, as scikit-learn is not optimized for GPU acceleration2. Therefore, using GPUs for scikit-learn models would be unnecessary and wasteful.
References:
* Preparing for Google Cloud Certification: Machine Learning Engineer, Course 3: Production ML Systems, Week 2: Serving ML Predictions
* Google Cloud Professional Machine Learning Engineer Exam Guide, Section 3: Scaling ML models in production, 3.1 Deploying ML models to production
* Official Google Cloud Certified Professional Machine Learning Engineer Study Guide, Chapter 6:
Production ML Systems, Section 6.2: Serving ML Predictions
* Online prediction
* Scaling online prediction
* scikit-learn FAQ

NEW QUESTION # 149
You are training a deep learning model for semantic image segmentation with reduced training time. While using a Deep Learning VM Image, you receive the following error: The resource 'projects/deeplearning-platforn/zones/europe-west4-c/acceleratorTypes/nvidia-tesla-k80' was not found. What should you do?

A. Ensure that the selected GPU has enough GPU memory for the workload.
B. Ensure that you have GPU quota in the selected region.
C. Ensure that the required GPU is available in the selected region.
D. Ensure that you have preemptible GPU quota in the selected region.

Answer: B

NEW QUESTION # 150
Your company manages an ecommerce website. You developed an ML model that recommends additional products to users in near real time based on items currently in the user's cart. The workflow will include the following processes.
1 The website will send a Pub/Sub message with the relevant data and then receive a message with the prediction from Pub/Sub.
2 Predictions will be stored in BigQuery
3. The model will be stored in a Cloud Storage bucket and will be updated frequently You want to minimize prediction latency and the effort required to update the model How should you reconfigure the architecture?

A. Write a Cloud Function that loads the model into memory for prediction Configure the function to be triggered when messages are sent to Pub/Sub.
B. Create a pipeline in Vertex Al Pipelines that performs preprocessing, prediction and postprocessing Configure the pipeline to be triggered by a Cloud Function when messages are sent to Pub/Sub.
C. Expose the model as a Vertex Al endpoint Write a custom DoFn in a Dataflow job that calls the endpoint for prediction.
D. Use the Runlnference API with watchFilePatterr. in a Dataflow job that wraps around the model and serves predictions.

Answer: D

Explanation:
According to the web search results, RunInference API1 is a feature of Apache Beam that enables you to run models as part of your pipeline in a way that is optimized for machine learning inference. RunInference API supports features like batching, caching, and model reloading. RunInference API can be used with various frameworks, such as TensorFlow, PyTorch, Sklearn, XGBoost, ONNX, and TensorRT1. Dataflow2 is a fully managed service for running Apache Beam pipelines on Google Cloud. Dataflow handles the provisioning and management of the compute resources, as well as the optimization and execution of the pipelines. Therefore, option D is the best way to reconfigure the architecture for the given use case, as it allows you to use the RunInference API with watchFilePattern in a Dataflow job that wraps around the model and serves predictions. This way, you can minimize prediction latency and the effort required to update the model, as the RunInference API will automatically reload the model from the Cloud Storage bucket whenever there is a change in the model file1. The other options are not relevant or optimal for this scenario. Reference:
RunInference API
Dataflow
Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

NEW QUESTION # 151
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