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Machine Learning in Google Cloud Platform (GCP) Test

Machine learning on Google Cloud Platform (GCP) combines powerful infrastructure with integrated AI services to build scalable, production-ready models. Success requires more than technical knowledge — it’s about leveraging GCP tools for the entire ML lifecycle, from data prep to deployment. The Machine Learning in GCP Test evaluates a candidate’s ability to use GCP’s ecosystem for building, training, deploying, and managing ML solutions effectively.

Duration (Customizable)

12 min

Skills Covered

4

Trusted by 1,000+ professionals worldwide

About the Machine Learning in Google Cloud Platform (GCP) Test

This assessment mirrors real-world workflows for data scientists and ML engineers using Google Cloud. From preparing datasets in BigQuery to deploying models on Vertex AI, each question is designed to test both conceptual understanding and practical application.

You’ll be evaluated across four critical areas:

  • Data Preparation & Feature Engineering on GCP
    Demonstrate the use of BigQuery, Dataflow, Dataprep, and AI Platform pipelines for cleaning, transforming, and engineering features at scale.

  • Model Development & Training on GCP
    Build and train ML models using Vertex AI, TensorFlow on GCP, AutoML, and distributed training with TPUs or GPUs.

  • Model Deployment & Serving on GCP
    Deploy trained models with Vertex AI endpoints, use APIs for prediction services, and ensure secure, scalable serving.

  • MLOps & Model Management on GCP
    Apply CI/CD for ML pipelines, monitor models with Vertex AI Model Monitoring, and manage drift, retraining, and governance for production ML systems.

This is where cloud infrastructure meets machine learning — proving you can deliver reliable, scalable ML solutions on GCP.

Covered Skills

Data Preparation & Feature Engineering

Model Development & Training

Model Deployment & Serving

MLOps & Model Management

Test Details


Type

Engineering skills


Languages

English


Duration

12 minutes


Difficulty

Intermediate

Recommended Job Roles to Use the Machine Learning in Google Cloud Platform (GCP)

This test is ideal for data scientists, ML engineers, cloud engineers, and AI specialists working within the GCP ecosystem. It’s also valuable for organizations assessing candidates preparing for Google Professional Machine Learning Engineer or Professional Data Engineer certifications.

Machine Learning Engineer
Data Scientist
Cloud ML Specialist
AI Engineer
Data Engineer
Applied Scientist

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Machine Learning in Google Cloud Platform (GCP) Report Preview

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Smart Candidate Monitoring – AI-based face detection, absentee tracking, and dual-screen detection.
Activity Tracking – screen & webcam recording, plus tab-switch monitoring to flag suspicious behavior.
Code Safety Controls – copy-paste disabling and detection of pasted code to ensure original work.
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Advanced Proctoring Features

Comprehensive AI-powered monitoring and security features to maintain test integrity

Face Detection

AI-based Face Detection

Advanced AI detects and verifies candidate's face throughout the test, flagging multiple faces or absence to prevent impersonation.

Dual Monitor

Dual Screen Detection

Monitors and alerts when candidates use multiple screens or external monitors, marking violations for review.

Code Paste

Code Paste Detection

Tracks when candidates paste pre-written code instead of typing their own and flags the behavior for review.

Screen Recording

Screen Recording

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Webcam Monitoring

Webcam Monitoring

Takes camera shots at intervals to ensure continuous monitoring of identity and environment, preventing external help.

Tab Proctoring

Tab Proctoring

Detects and flags when candidates switch between tabs or browsers to prevent searching for answers online.

Flag Suspicious

Flag Suspicious Candidates

Automatically identifies candidates exhibiting unusual behavior like extended absences, dual monitors, or copy-paste attempts.

Code Paste

Copy-Paste Disabling

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Absentee Monitoring

Absentee Monitoring

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