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How to Prepare For Professional Machine Learning Engineer - Google

Preparation Guide for Professional Machine Learning Engineer - Google

Introduction for Professional Machine Learning Engineer - Google

A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer is proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation and needs familiarity with application development, infrastructure management, data engineering, and security.

The Professional Machine Learning Engineer exam assesses your ability to:

• Frame ML problems
• Prepare and process data
• Monitor, optimize, and maintain ML solutions
• Architect ML solutions

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Google Professional Machine Learning Engineer Sample Questions (Q33-Q38):

NEW QUESTION # 33
You are an ML engineer at a regulated insurance company. You are asked to develop an insurance approval model that accepts or rejects insurance applications from potential customers. What factors should you consider before building the model?

• A. Federated learning, reproducibility, and explainability
• B. Redaction, reproducibility, and explainability
• C. Differential privacy federated learning, and explainability
• D. Traceability, reproducibility, and explainability

Answer: D

Explanation:
https://www.oecd.org/finance/Impact-Big-Data-AI-in-the-Insurance-Sector.pdf
https://medium.com/artefact-engineering-and-data-science/including-ethics-best-practices-in-your-data-science-project-from-day-one-c15b26c2bf99

NEW QUESTION # 34
You are developing models to classify customer support emails. You created models with TensorFlow Estimators using small datasets on your on-premises system, but you now need to train the models using large datasets to ensure high performance. You will port your models to Google Cloud and want to minimize code refactoring and infrastructure overhead for easier migration from on-prem to cloud. What should you do?

• A. Create a cluster on Dataproc for training
• B. Create a Managed Instance Group with autoscaling
• C. Use Al Platform for distributed training
• D. Use Kubeflow Pipelines to train on a Google Kubernetes Engine cluster.

Answer: C

Explanation:
Explanation:

NEW QUESTION # 35
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. 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.
• C. Set up restrictive IAM permissions on the AI Platform notebooks so that only a single user or group can access a given instance.
• D. 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.

Answer: D

Explanation:
https://cloud.google.com/ai-platform/prediction/docs/resource-labels#overview_of_labels You can add labels to your AI Platform Prediction jobs, models, and model versions, then use those labels to organize resources into categories when viewing or monitoring the resources.

NEW QUESTION # 36
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training. The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs.
What does the Specialist need to do?

• A. Set the GPU flag in the Amazon SageMaker CreateTrainingJob request body.
• B. Organize the Docker container's file structure to execute on GPU instances.
• C. Bundle the NVIDIA drivers with the Docker image.
• D. Build the Docker container to be NVIDIA-Docker compatible.

Answer: C

NEW QUESTION # 37
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 learning rate, and increase the number of neurons by a factor of 2.
• B. Apply a dropout parameter of 0 2, and decrease the learning rate by a factor of 10
• C. Apply a 12 regularization parameter of 0.4, and decrease the learning rate by a factor of 10.
• D. Run a hyperparameter tuning job on Al Platform to optimize for the L2 regularization and dropout parameters

Answer: D

Explanation:
https://machinelearningmastery.com/introduction-to-regularization-to-reduce-overfitting-and-improve-generalization-error/

NEW QUESTION # 38
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