Free Professional Machine Learning Engineer Exam Questions and Answers

Last updated: September 14, 2026

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Google

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1. Question #1

   You work for an advertising company and want to understand the effectiveness of your company's latest advertising campaign. You have streamed 500 MB of campaign data into BigQuery. You want to query the table, and then manipulate the results of that query with a pandas dataframe in an Al Platform notebook. What should you do?

   • A. read_csv to ingest the file as a pandas dataframe
   • B. Export your table as a CSV file from BigQuery to Google Drive, and use the Google Drive API to ingest the file into your notebook instance
   • C. Use Al Platform Notebooks' BigQuery cell magic to query the data, and ingest the results as a pandas dataframe
   • D. Download your table from BigQuery as a local CSV file, and upload it to your Al Platform notebook instance Use panda
   Reveal Answer

2. Question #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?

   • A. Configure Kubeflow Pipelines to schedule your multi-step workflow from training to deploying your model.
   • B. Write a Cloud Functions script that launches a training and deploying job on Ai Platform that is triggered by Cloud Scheduler
   • C. Use a model trained and deployed on BigQuery ML and trigger retraining with the scheduled query feature in BigQuery
   • D. Use Cloud Composer to programmatically schedule a Dataflow job that executes the workflow from training to deploying your model
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3. Question #3

   Your organization wants to make its internal shuttle service route more efficient. The shuttles currently stop at all pick-up points across the city every 30 minutes between 7 am and 10 am. The development team has already built an application on Google Kubernetes Engine that requires users to confirm their presence and shuttle station one day in advance. What approach should you take?

   • A. 1. Build a reinforcement learning model with tree-based classification models that predict the presenceof passengers at shuttle stops as agents and a reward
   • B. 1. Build a tree-based regression model that predicts how many passengers will be picked up at each shuttle station.* 2. Dispatch an appropriately sized shuttle
   • C. 1. Build a tree-based classification model that predicts whether the shuttle should pick up passengers at each shuttle station.* 2. Dispatch an available shuttle
   • D. 1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed attendance at the given time under capacity constraints.* 2
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4. Question #4

   You work with a data engineering team that has developed a pipeline to clean your dataset and save it in a Cloud Storage bucket. You have created an ML model and want to use the data to refresh your model as soon as new data is available. As part of your CI/CD workflow, you want to automatically run a Kubeflow Pipelines training job on Google Kubernetes Engine (GKE). How should you architect this workflow?

   • A. Use App Engine to create a lightweight python client that continuously polls Cloud Storage for new files As soon as a file arrives, initiate the training job
   • B. Use a Pub/Sub-triggered Cloud Function to start the training job on a GKE cluster
   • C. Configure your pipeline with Dataflow, which saves the files in Cloud Storage After the file is saved, start the training job on a GKE cluster
   • D. Configure a Cloud Storage trigger to send a message to a Pub/Sub topic when a new file is available in a storage bucke
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5. Question #5

   You want to rebuild your ML pipeline for structured data on Google Cloud. You are using PySpark to conduct data transformations at scale, but your pipelines are taking over 12 hours to run. To speed up development and pipeline run time, you want to use a serverless tool and SQL syntax. You have already moved your raw data into Cloud Storage. How should you build the pipeline on Google Cloud while meeting the speed and processing requirements?

   • A. Ingest your data into BigQuery using BigQuery Load, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the
   • B. Use Data Fusion's GUI to build the transformation pipelines, and then write the data into BigQuery
   • C. Convert your PySpark into SparkSQL queries to transform the data and then run your pipeline on Dataproc to write the data into BigQuery.
   • D. Ingest your data into Cloud SQL convert your PySpark commands into SQL queries to transform the data, and then use federated queries from BigQuery for
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6. Question #6

   You have deployed multiple versions of an image classification model on Al Platform. You want to monitor the performance of the model versions overtime. How should you perform this comparison?

   • A. Compare the receiver operating characteristic (ROC) curve for each model using the What-lf Tool
   • B. Compare the mean average precision across the models using the Continuous Evaluation feature
   • C. Compare the loss performance for each model on the validation data
   • D. Compare the loss performance for each model on a held-out dataset.
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7. Question #7

   You work for an online retail company that is creating a visual search engine. You have set up an end-to-end ML pipeline on Google Cloud to classify whether an image contains your company's product. Expecting the release of new products in the near future, you configured a retraining functionality in the pipeline so that new data can be fed into your ML models. You also want to use Al Platform's continuous evaluation service to ensure that the models have high accuracy on your test data set. What should you do?

   • A. Extend your test dataset with images of the newer products when they are introduced to retraining
   • B. Update your test dataset with images of the newer products when your evaluation metrics drop below a pre-decided threshold.
   • C. Keep the original test dataset unchanged even if newer products are incorporated into retraining
   • D. Replace your test dataset with images of the newer products when they are introduced to retraining.
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8. Question #8

   You recently designed and built a custom neural network that uses critical dependencies specific to your organization's framework. You need to train the model using a managed training service on Google Cloud. However, the ML framework and related dependencies are not supported by Al Platform Training. Also, both your model and your data are too large to fit in memory on a single machine. Your ML framework of choice uses the scheduler, workers, and servers distribution structure. What should you do?

   • A. Build your custom container to run jobs on Al Platform Training
   • B. Use a built-in model available on Al Platform Training
   • C. Build your custom containers to run distributed training jobs on Al Platform Training
   • D. Reconfigure your code to a ML framework with dependencies that are supported by Al Platform Training
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9. Question #9

   As the lead ML Engineer for your company, you are responsible for building ML models to digitize scanned customer forms. You have developed a TensorFlow model that converts the scanned images into text and stores them in Cloud Storage. You need to use your ML model on the aggregated data collected at the end of each day with minimal manual intervention. What should you do?

   • A. Use the batch prediction functionality of Al Platform
   • B. Use Cloud Functions for prediction each time a new data point is ingested
   • C. Create a serving pipeline in Compute Engine for prediction
   • D. Deploy the model on Al Platform and create a version of it for online inference.
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10. Question #10

    1. What is overfitting in a machine learning model?

    • A. Low bias
    • B. Model performs well on training data
    • C. High variance in predictions
    • D. Model performs poorly on new data
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