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Google Professional Machine Learning Engineer exam is designed to test the knowledge and skills of professionals who are working in the field of machine learning. Professional-Machine-Learning-Engineer exam is considered to be one of the most challenging and comprehensive exams in the field of machine learning. Professional-Machine-Learning-Engineer exam is designed to test the candidate's ability to design, build, and deploy machine learning models using Google Cloud technologies.

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.

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Google Professional Machine Learning Engineer Sample Questions (Q12-Q17):

NEW QUESTION # 12
You have deployed multiple versions of an image classification model on Al Platform. You want to monitor the performance of the model versions overtime. How should you perform this comparison?

• A. Compare the receiver operating characteristic (ROC) curve for each model using the What-lf Tool
• B. Compare the loss performance for each model on a held-out dataset.
• C. Compare the loss performance for each model on the validation data
• D. Compare the mean average precision across the models using the Continuous Evaluation feature

Answer: D

Explanation:
The performance of an image classification model can be measured by various metrics, such as accuracy, precision, recall, F1-score, and mean average precision (mAP). These metrics can be calculated based on the confusion matrix, which compares the predicted labels and the true labels of the images1 One of the best ways to monitor the performance of multiple versions of an image classification model on AI Platform is to compare the mean average precision across the models using the Continuous Evaluation feature.
Mean average precision is a metric that summarizes the precision and recall of a model across different confidence thresholds and classes. Mean average precision is especially useful for multi-class and multi-label image classification problems, where the model has to assign one or more labels to each image from a set of possible labels. Mean average precision can range from 0 to 1, where a higher value indicates a better performance2 Continuous Evaluation is a feature of AI Platform that allows you to automatically evaluate the performance of your deployed models using online prediction requests and responses. Continuous Evaluation can help you monitor the quality and consistency of your models over time, and detect any issues or anomalies that may affect the model performance. Continuous Evaluation can also provide various evaluation metrics and visualizations, such as accuracy, precision, recall, F1-score, ROC curve, and confusion matrix, for different types of models, such as classification, regression, and object detection3 To compare the mean average precision across the models using the Continuous Evaluation feature, you need to do the following steps:
* Enable the online prediction logging for each model version that you want to evaluate. This will allow AI Platform to collect the prediction requests and responses from your models and store them in BigQuery4
* Create an evaluation job for each model version that you want to evaluate. This will allow AI Platform to compare the predicted labels and the true labels of the images, and calculate the evaluation metrics, such as mean average precision. You need to specify the BigQuery table that contains the prediction logs, the data schema, the label column, and the evaluation interval.
* View the evaluation results for each model version on the AI Platform Models page in the Google Cloud console. You can see the mean average precision and other metrics for each model version over time, and compare them using charts and tables. You can also filter the results by different classes and confidence thresholds.
The other options are not as effective or feasible. Comparing the loss performance for each model on a held-out dataset or on the validation data is not a good idea, as the loss function may not reflect the actual performance of the model on the online prediction data, and may vary depending on the choice of the loss function and the optimization algorithm. Comparing the receiver operating characteristic (ROC) curve for each model using the What-If Tool is not possible, as the What-If Tool does not support image data or multi-class classification problems.
References: 1: Confusion matrix 2: Mean average precision 3: Continuous Evaluation overview 4: Configure online prediction logging : [Create an evaluation job] : [View evaluation results] : [What-If Tool overview]

 

NEW QUESTION # 13
You have trained a deep neural network model on Google Cloud. The model has low loss on the training data, but is performing worse on the validation dat a. You want the model to be resilient to overfitting. Which strategy should you use when retraining the model?

• A. Run a hyperparameter tuning job on Al Platform to optimize for the L2 regularization and dropout parameters
• B. Apply a dropout parameter of 0 2, and decrease the learning rate by a factor of 10
• C. Run a hyperparameter tuning job on Al Platform to optimize for the learning rate, and increase the number of neurons by a factor of 2.
• D. Apply a 12 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.

Answer: B

 

NEW QUESTION # 14
You are analyzing customer data for a healthcare organization that is stored in Cloud Storage. The data contains personally identifiable information (PII) You need to perform data exploration and preprocessing while ensuring the security and privacy of sensitive fields What should you do?

• A. Use the Cloud Data Loss Prevention (DLP) API to de-identify the PI! before performing data exploration and preprocessing.
• B. Use a VM inside a VPC Service Controls security perimeter to perform data exploration and preprocessing.
• C. Use Google-managed encryption keys to encrypt the Pll data at rest, and decrypt the Pll data during data exploration and preprocessing.
• D. Use customer-managed encryption keys (CMEK) to encrypt the Pll data at rest and decrypt the Pll data during data exploration and preprocessing.

Answer: A

Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to "design, build, and productionalize ML models to solve business challenges using Google Cloud technologies". Cloud Data Loss Prevention (DLP) API2 is a service that provides programmatic access to a powerful detection engine for personally identifiable information and other privacy-sensitive data in unstructured data streams, such as text blocks and images. Cloud DLP API helps you discover, classify, and protect your sensitive data by using techniques such as de-identification, masking, tokenization, and bucketing. You can use Cloud DLP API to de-identify the PII data before performing data exploration andpreprocessing, and retain the data utility for ML purposes. Therefore, option A is the best way to perform data exploration and preprocessing while ensuring the security and privacy of sensitive fields. The other options are not relevant or optimal for this scenario.
References:
* Professional ML Engineer Exam Guide
* Cloud Data Loss Prevention (DLP) API
* Google Professional Machine Learning Certification Exam 2023
* Latest Google Professional Machine Learning Engineer Actual Free Exam Questions

 

NEW QUESTION # 15
While running a model training pipeline on Vertex Al, you discover that the evaluation step is failing because of an out-of-memory error. You are currently using TensorFlow Model Analysis (TFMA) with a standard Evaluator TensorFlow Extended (TFX) pipeline component for the evaluation step. You want to stabilize the pipeline without downgrading the evaluation quality while minimizing infrastructure overhead. What should you do?

• A. Move the evaluation step out of your pipeline and run it on custom Compute Engine VMs with sufficient memory.
• B. Include the flag -runner=DataflowRunner in beam_pipeline_args to run the evaluation step on Dataflow.
• C. Migrate your pipeline to Kubeflow hosted on Google Kubernetes Engine, and specify the appropriate node parameters for the evaluation step.
• D. Add tfma.MetricsSpec () to limit the number of metrics in the evaluation step.

Answer: B

Explanation:
The best option to stabilize the pipeline without downgrading the evaluation quality while minimizing infrastructure overhead is to use Dataflow as the runner for the evaluation step. Dataflow is a fully managed service for executing Apache Beam pipelines that can scale up and down according to the workload. Dataflow can handle large-scale, distributed data processing tasks such as model evaluation, and it can also integrate with Vertex AI Pipelines and TensorFlow Extended (TFX). By using the flag -runner=DataflowRunner in beam_pipeline_args, you can instruct the Evaluator component to run the evaluation step on Dataflow, instead of using the default DirectRunner, which runs locally and may cause out-of-memory errors. Option A is incorrect because adding tfma.MetricsSpec() to limit the number of metrics in the evaluation step may downgrade the evaluation quality, as some important metrics may be omitted.
Moreover, reducing the number of metrics may not solve the out-of-memory error, as the evaluation step may still consume a lot of memory depending on the size and complexity of the data and the model. Option B is incorrect because migrating the pipeline to Kubeflow hosted on Google Kubernetes Engine (GKE) may increase the infrastructure overhead, as you need to provision, manage, and monitor the GKE cluster yourself.
Moreover, you need to specify the appropriate node parameters for the evaluation step, which may require trial and error to find the optimal configuration. Option D is incorrect because moving the evaluation step out of the pipeline and running it on custom Compute Engine VMs may also increase the infrastructure overhead, as you need to create, configure, and delete the VMs yourself. Moreover, you need to ensure that the VMs have sufficientmemory for the evaluation step, which may require trial and error to find the optimal machine type. References:
* Dataflow documentation
* Using DataflowRunner
* Evaluator component documentation
* Configuring the Evaluator component

 

NEW QUESTION # 16
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 Set the max replica count to 100
• B. Deploy an online Vertex Al prediction endpoint with one GPU per replica Set the max replica count to
  1.
• C. Deploy an online Vertex Al prediction endpoint with one GPU per replica Set the max replica count to
  100.
• D. Deploy an online Vertex Al prediction endpoint Set the max replica count to 1

Answer: A

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 zeroreplicas 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 # 17
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