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Google Professional-Machine-Learning-Engineer Dumps Questions Answers

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Google Professional-Machine-Learning-Engineer Exam Dumps FAQs

The Google Professional Machine Learning Engineer Exam certifies your ability to build, evaluate, productionize, and optimize ML models using Google Cloud technologies. It assesses your knowledge of proven models, techniques, and responsible AI practices.
Professionals with at least 3 years of industry experience, including 1 or more years designing and managing solutions using Google Cloud, are ideal candidates for this certification.

The Google Professional-Machine-Learning-Engineer exam focuses on core competencies across the machine learning lifecycle on GCP, including:

  • Machine Learning Fundamentals (20%)
  • Data Management (20%)
  • Model Training (20%)
  • Model Evaluation and Tuning (15%)
  • Model Deployment and Monitoring (25%)
The Google Professional-Machine-Learning-Engineer exam format includes 50-60 multiple-choice and multiple-select questions.
The Google Professional-Machine-Learning-Engineer exam duration is 2 hours.
The Google Professional-Machine-Learning-Engineer certification is valid for three years.

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Google Professional Machine Learning Engineer Questions and Answers

Questions 1

You manage a team of data scientists who use a cloud-based backend system to submit training jobs. This system has become very difficult to administer, and you want to use a managed service instead. The data scientists you work with use many different frameworks, including Keras, PyTorch, theano, scikit-learn, and custom libraries. What should you do?



Use the Vertex AI Training to submit training jobs using any framework.


Configure Kubeflow to run on Google Kubernetes Engine and submit training jobs through TFJob.


Create a library of VM images on Compute Engine, and publish these images on a centralized repository.


Set up Slurm workload manager to receive jobs that can be scheduled to run on your cloud infrastructure.

Questions 2

You work for a public transportation company and need to build a model to estimate delay times for multiple transportation routes. Predictions are served directly to users in an app in real time. Because different seasons and population increases impact the data relevance, you will retrain the model every month. You want to follow Google-recommended best practices. How should you configure the end-to-end architecture of the predictive model?



Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.


Use a model trained and deployed on BigQuery ML and trigger retraining with the scheduled query feature in BigQuery


Write a Cloud Functions script that launches a training and deploying job on Ai Platform that is triggered by Cloud Scheduler


Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model

Questions 3

You work for a retail company. You have been asked to develop a model to predict whether a customer will purchase a product on a given day. Your team has processed the company's sales data, and created a table with the following rows:

• Customer_id

• Product_id

• Date

• Days_since_last_purchase (measured in days)

• Average_purchase_frequency (measured in 1/days)

• Purchase (binary class, if customer purchased product on the Date)

You need to interpret your models results for each individual prediction. What should you do?



Create a BigQuery table Use BigQuery ML to build a boosted tree classifier Inspect the partition rules of the trees to understand how each prediction flows through the trees.


Create a Vertex Al tabular dataset Train an AutoML model to predict customer purchases Deploy the model

to a Vertex Al endpoint and enable feature attributions Use the "explain" method to get feature attribution values for each individual prediction.


Create a BigQuery table Use BigQuery ML to build a logistic regression classification model Use the values of the coefficients of the model to interpret the feature importance with higher values corresponding to more importance.


Create a Vertex Al tabular dataset Train an AutoML model to predict customer purchases Deploy the model to a Vertex Al endpoint. At each prediction enable L1 regularization to detect non-informative features.

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