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Latest Google Professional Machine Learning Engineer Exam Questions With Free Update

Becoming a certified Professional Machine Learning Engineer can open up many career opportunities in the technology industry. However, passing the Google certification exam requires extensive preparation and practice. Certspots is a platform that provides the latest exam questions and updates for the Google Professional Machine Learning Engineer certification exam, enabling individuals to prepare effectively and confidently take the exam. With Certspots, individuals can increase their chances of passing the exam and advancing their careers in the field of machine learning.

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1. Your organization's call center has asked you to develop a model that analyzes customer sentiments in each call. The call center receives over one million calls daily, and data is stored in Cloud Storage. The data collected must not leave the region in which the call originated, and no Personally Identifiable Information (Pll) can be stored or analyzed. The data science team has a third-party tool for visualization and access which requires a SQL ANSI-2011 compliant interface. You need to select components for data processing and for analytics.

How should the data pipeline be designed?

2. You are building a model to predict daily temperatures. You split the data randomly and then transformed the training and test datasets. Temperature data for model training is uploaded hourly. During testing, your model performed with 97% accuracy; however, after deploying to production, the model's accuracy dropped to 66%.

How can you make your production model more accurate?

3. You are responsible for building a unified analytics environment across a variety of on-premises data marts. Your company is experiencing data quality and security challenges when integrating data across the servers, caused by the use of a wide range of disconnected tools and temporary solutions. You need a fully managed, cloud-native data integration service that will lower the total cost of work and reduce repetitive work. Some members on your team prefer a codeless interface for building Extract, Transform, Load (ETL) process.

Which service should you use?

4. Your company manages an application that aggregates news articles from many different online sources and sends them to users. You need to build a recommendation model that will suggest articles to readers that are similar to the articles they are currently reading.

Which approach should you use?

5. You are an ML engineer at a bank that has a mobile application. Management has asked you to build an ML-based biometric authentication for the app that verifies a customer's identity based on their fingerprint. Fingerprints are considered highly sensitive personal information and cannot be downloaded and stored into the bank databases.

Which learning strategy should you recommend to train and deploy this ML model?

6. Your data science team needs to rapidly experiment with various features, model architectures, and hyperparameters. They need to track the accuracy metrics for various experiments and use an API to query the metrics over time.

What should they use to track and report their experiments while minimizing manual effort?

7. You are an ML engineer at a manufacturing company. You need to build a model that identifies defects in products based on images of the product taken at the end of the assembly line. You want your model to preprocess the images with lower computation to quickly extract features of defects in products.

Which approach should you use to build the model?

8. You work as an ML engineer at a social media company, and you are developing a visual filter for users’ profile photos. This requires you to train an ML model to detect bounding boxes around human faces. You want to use this filter in your company’s iOS-based mobile phone application. You want to minimize code development and want the model to be optimized for inference on mobile phones.

What should you do?

9. You are the Director of Data Science at a large company, and your Data Science team has recently begun using the Kubeflow Pipelines SDK to orchestrate their training pipelines. Your team is struggling to integrate their custom Python code into the Kubeflow Pipelines SDK.

How should you instruct them to proceed in order to quickly integrate their code with the Kubeflow Pipelines SDK?

10. You are working on a system log anomaly detection model for a cybersecurity organization. You have developed the model using TensorFlow, and you plan to use it for real-time prediction. You need to create a Dataflow pipeline to ingest data via Pub/Sub and write the results to BigQuery. You want to minimize the serving latency as much as possible.

What should you do?



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