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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:

• Develop ML models
• Frame ML problems
• Automate & orchestrate ML pipelines
• Architect ML solutions
• Prepare and process data

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Prerequisites

The Google Professional Machine Learning Engineer certification exam has no formal prerequisites. However, it is pretty hard to pass this test without having solid practical background. The candidates are recommended to have at least three years of industry experience, involving about one year of experience in designing and managing solutions with the help of Google Cloud. The target individuals can take advantage of Google Cloud Free Tier to use the selected products free of charge and gain the real-world expertise. >> Professional-Machine-Learning-Engineer Downloadable PDF <<

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Google Professional Machine Learning Engineer Sample Questions (Q21-Q26):

NEW QUESTION # 21
You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?

• A. Use Al Platform Notebooks to run the classification model with pandas library
• B. Run a BigQuery ML task to perform logistic regression for the classification
• C. Use Al Platform to run the classification model job configured for hyperparameter tuning
• D. Configure AutoML Tables to perform the classification task

Answer: A
NEW QUESTION # 22
You are an ML engineer at a large grocery retailer with stores in multiple regions. You have been asked to create an inventory prediction model. Your models features include region, location, historical demand, and seasonal popularity. You want the algorithm to learn from new inventory data on a daily basis. Which algorithms should you use to build the model?

• A. Recurrent Neural Networks (RNN)
• B. Classification
• C. Convolutional Neural Networks (CNN)
• D. Reinforcement Learning

Answer: D
NEW QUESTION # 23
A large mobile network operating company is building a machine learning model to predict customers who are likely to unsubscribe from the service. The company plans to offer an incentive for these customers as the cost of churn is far greater than the cost of the incentive.
The model produces the following confusion matrix after evaluating on a test dataset of 100 customers:

Based on the model evaluation results, why is this a viable model for production?

• A. The precision of the model is 86%, which is less than the accuracy of the model.
• B. The model is 86% accurate and the cost incurred by the company as a result of false negatives is less than the false positives.
• C. The precision of the model is 86%, which is greater than the accuracy of the model.
• D. The model is 86% accurate and the cost incurred by the company as a result of false positives is less than the false negatives.

Answer: B
NEW QUESTION # 24
A technology startup is using complex deep neural networks and GPU compute to recommend the company's products to its existing customers based upon each customer's habits and interactions. The solution currently pulls each dataset from an Amazon S3 bucket before loading the data into a TensorFlow model pulled from the company's Git repository that runs locally. This job then runs for several hours while continually outputting its progress to the same S3 bucket. The job can be paused, restarted, and continued at any time in the event of a failure, and is run from a central queue.
Senior managers are concerned about the complexity of the solution's resource management and the costs involved in repeating the process regularly. They ask for the workload to be automated so it runs once a week, starting Monday and completing by the close of business Friday.
Which architecture should be used to scale the solution at the lowest cost?

• A. Implement the solution using AWS Deep Learning Containers, run the workload using AWS Fargate running on Spot Instances, and then schedule the task using the built-in task scheduler
• B. Implement the solution using a low-cost GPU-compatible Amazon EC2 instance and use the AWS Instance Scheduler to schedule the task
• C. Implement the solution using Amazon ECS running on Spot Instances and schedule the task using the ECS service scheduler
• D. Implement the solution using AWS Deep Learning Containers and run the container as a job using AWS Batch on a GPU-compatible Spot Instance

Answer: A
NEW QUESTION # 25
A Machine Learning Specialist is designing a system for improving sales for a company. The objective is to use the large amount of information the company has on users' behavior and product preferences to predict which products users would like based on the users' similarity to other users.
What should the Specialist do to meet this objective?

• A. Build a collaborative filtering recommendation engine with Apache Spark ML on Amazon EMR.
• B. Build a content-based filtering recommendation engine with Apache Spark ML on Amazon EMR
• C. Build a combinative filtering recommendation engine with Apache Spark ML on Amazon EMR
• D. Build a model-based filtering recommendation engine with Apache Spark ML on Amazon EMR

Answer: A Explanation:
Many developers want to implement the famous Amazon model that was used to power the "People who bought this also bought these items" feature on Amazon.com. This model is based on a method called Collaborative Filtering. It takes items such as movies, books, and products that were rated highly by a set of users and recommending them to other users who also gave them high ratings. This method works well in domains where explicit ratings or implicit user actions can be gathered and analyzed.
Reference: https://aws.amazon.com/blogs/big-data/building-a-recommendation-engine-with-spark-ml-on-amazon-emr-using-zeppelin/
NEW QUESTION # 26
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