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Sharpen Your Knowledge with Google (Google Professional Machine Learning Engineer) Certification Sample Questions
CertsTime has provided you with a sample question set to elevate your knowledge about the Google Professional Machine Learning Engineer exam. With these updated sample questions, you can become quite familiar with the difficulty level and format of the real Google Professional Machine Learning Engineer certification test. Try our sample Google Professional Machine Learning Engineer certification practice exam to get a feel for the real exam environment. Our sample practice exam gives you a sense of reality and an idea of the questions on the actual Google Cloud Certified certification exam.
Our sample questions are similar to the Real Google Professional Machine Learning Engineer exam questions. The premium Google Professional Machine Learning Engineer certification practice exam gives you a golden opportunity to evaluate and strengthen your preparation with real-time scenario-based questions. Plus, by practicing real-time scenario-based questions, you will run into a variety of challenges that will push you to enhance your knowledge and skills.
Google Professional Machine Learning Engineer Sample Questions:
You are training an object detection model using a Cloud TPU v2. Training time is taking longer than expected. Based on this simplified trace obtained with a Cloud TPU profile, what action should you take to decrease training time in a cost-efficient way?
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?
You are an ML engineer responsible for designing and implementing training pipelines for ML models. You need to create an end-to-end training pipeline for a TensorFlow model. The TensorFlow model will be trained on several terabytes of structured dat
a. You need the pipeline to include data quality checks before training and model quality checks after training but prior to deployment. You want to minimize development time and the need for infrastructure maintenance. How should you build and orchestrate your training pipeline?
You are developing an ML model to predict house prices. While preparing the data, you discover that an important predictor variable, distance from the closest school, is often missing and does not have high variance. Every instance (row) in your data is important. How should you handle the missing data?
You recently built the first version of an image segmentation model for a self-driving car. After deploying the model, you observe a decrease in the area under the curve (AUC) metric. When analyzing the video recordings, you also discover that the model fails in highly congested traffic but works as expected when there is less traffic. What is the most likely reason for this result?
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