Google Professional Machine Learning Engineer Exam Questions

Exam Name: Google Professional Machine Learning Engineer

Exam Code: Professional Machine Learning Engineer

Related Certification(s): Google Cloud Certified Certifications, Google Cloud Engineer Certifications

Certification Provider: Google

Actual Exam Duration: 120 Minutes

Number of Professional Machine Learning Engineer Practice Questions: 283 (updated: )

Expected Professional Machine Learning Engineer Exam Topics, as suggested by Google:
Topic 1: Architecting low-code AI solutions
This section of the exam measures the skills of Google Machine Learning Engineers and covers developing machine learning models using BigQuery ML. It includes selecting appropriate models for business problems, such as linear and binary classification, regression, time series, matrix factorization, boosted trees, and autoencoders. Additionally, it involves feature engineering or selection and generating predictions using BigQuery ML.
Topic 2: Collaborating within and across teams to manage data and models
It explores and processes organization-wide data including Apache Spark, Cloud Storage, Apache Hadoop, Cloud SQL, and Cloud Spanner. The topic also discusses using Jupyter Notebooks to model prototypes. Lastly, it discusses tracking and running ML experiments.
Topic 3: Scaling prototypes into ML models
This topic covers building and training models. It also focuses on opting for suitable hardware for training.
Topic 4: Serving and scaling models
This section deals with batch and online inference, using frameworks such as XGBoost, and managing features using Vertex AI.
Topic 5: Automating and orchestrating ML pipelines
This topic focuses on developing end-to-end ML pipelines, automation of model retraining, and lastly tracking and auditing metadata.
Topic 6: Monitoring ML solutions
It identifies risks to ML solutions. Moreover, the topic discusses monitoring, testing, and troubleshooting ML solutions.
Free Google Professional Machine Learning Engineer Exam Actual Questions
Note: Google Professional Machine Learning Engineer Premium Questions were last updated on

Q#1
You have a large corpus of written support cases that can be classified into 3 separate categories: Technical Support, Billing Support, or Other Issues. You need to quickly build, test, and deploy a service that will automatically classify future written requests into one of the categories. How should you configure the pipeline?

Q#2
You are training and deploying updated versions of a regression model with tabular data by using Vertex Al Pipelines. Vertex Al Training Vertex Al Experiments and Vertex Al Endpoints. The model is deployed in a Vertex Al endpoint and your users call the model by using the Vertex Al endpoint. You want to receive an email when the feature data distribution changes significantly, so you can retrigger the training pipeline and deploy an updated version of your model What should you do?

Q#3
You work with a team of researchers to develop state-of-the-art algorithms for financial analysis. Your team develops and debugs complex models in TensorFlow. You want to maintain the ease of debugging while also reducing the model training time. How should you set up your training environment?

Q#4
You are an ML engineer at a global shoe store. You manage the ML models for the company’s website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users. What should you do?

Q#5
You are training an LSTM-based model on Al Platform to summarize text using the following job submission script:

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You want to ensure that training time is minimized without significantly compromising the accuracy of your model. What should you do?