Question 1

How many GCP-PMLE practice questions are there?

Accepted Answer

376 questions across 2 versions, each with the correct answer and an explanation.

Question 2

Are there free GCP-PMLE sample questions?

Accepted Answer

Yes. 20 sample questions are free; 10 are open to everyone and the rest unlock with a free account.

Question 3

What does full access include?

Accepted Answer

Every GCP-PMLE question in study, timed and flashcard modes, explanations for each answer, and progress that saves so you can resume.

Question 4

What topics does GCP-PMLE cover?

Accepted Answer

Architecting Low-Code AI Solutions (13%), Collaborating Within and Across Teams to Manage Data and Models (14%), Scaling Prototypes into ML Models (18%), Serving and Scaling Models (20%), Automating and Orchestrating ML Pipelines (22%), Monitoring AI Solutions (13%).

Question 5

How many questions are on the Google Cloud Professional Machine Learning Engineer exam?

Accepted Answer

The Google Cloud Professional Machine Learning Engineer exam contains 50 to 60 questions in multiple-choice and multiple-select formats. You are given 120 minutes (2 hours) to complete the entire exam. The questions are scenario-based, testing your ability to apply ML engineering knowledge to real-world Google Cloud situations.

Question 6

What is the passing score for the GCP Professional Machine Learning Engineer certification?

Accepted Answer

The passing score for the Google Cloud Professional Machine Learning Engineer certification is 70%. Your result is reported as pass or fail. You do not need to pass each domain individually; a balanced performance across all six domains (Architecting Low-Code AI Solutions, Data and Model Management, Scaling Models, Serving Models, ML Pipelines, and Monitoring) is recommended.

Question 7

Does the Google Cloud Professional ML Engineer exam include generative AI topics?

Accepted Answer

Yes, the current version (v3.1, updated October 2024) of the Professional Machine Learning Engineer exam includes generative AI topics. These include building AI solutions using Model Garden, implementing RAG (Retrieval Augmented Generation) applications with Vertex AI Agent Builder, fine-tuning foundation models, and evaluating generative AI solutions. Generative AI is now integrated throughout multiple exam sections.

Question 8

What are the prerequisites for the Google Cloud Professional Machine Learning Engineer certification?

Accepted Answer

There are no formal prerequisites to take the exam. However, Google recommends 3+ years of industry experience including at least 1 year designing and managing solutions using Google Cloud. You should have proficiency in Python and SQL, familiarity with ML frameworks like TensorFlow, PyTorch, or scikit-learn, and hands-on experience with Vertex AI, BigQuery ML, and AutoML.

Question 9

How long is the Google Cloud Professional Machine Learning Engineer certification valid?

Accepted Answer

The Google Cloud Professional Machine Learning Engineer certification is valid for 2 years from the date you pass the exam. You can begin the recertification process starting 60 days before your certification expiration date by retaking the current version of the exam. Each recertification attempt requires paying the full $200 USD exam fee.

Question 10

What is the biggest topic on the GCP Professional Machine Learning Engineer exam?

Accepted Answer

The largest domain on the exam is 'Automating and Orchestrating ML Pipelines' at approximately 22% of the exam weight. This section covers developing end-to-end ML pipelines, orchestration frameworks like Kubeflow Pipelines and Vertex AI Pipelines, CI/CD for ML model deployment, automated model retraining, and metadata tracking and auditing. The second largest domain is 'Serving and Scaling Models' at approximately 20%.

Question 11

Can I take the Google Cloud ML Engineer exam online?

Accepted Answer

Yes, the Google Cloud Professional Machine Learning Engineer exam offers two delivery methods: online-proctored from any quiet, private location that meets Google Cloud's online testing requirements, or onsite-proctored at a Kryterion testing center near you. Both options provide secure, reliable testing environments. You register through the Google Cloud certification portal at cp.certmetrics.com.

Question 12

What Google Cloud services should I know for the Professional ML Engineer exam?

Accepted Answer

Key Google Cloud services for this exam include: Vertex AI (Custom Training, AutoML, Pipelines, Experiments, Feature Store, Model Registry, Model Monitoring, Workbench, Model Garden, Agent Builder), BigQuery ML, Dataflow, TFX (TensorFlow Extended), Cloud Storage, Kubeflow Pipelines, Cloud Composer, Cloud Build, Cloud Run, Cloud DLP, Dataproc, and various ML APIs (Vision, Natural Language, Speech-to-Text, Document AI, Retail API). Understanding Vertex AI comprehensively is critical.

Question 13

How much does the Google Cloud Professional Machine Learning Engineer exam cost?

Accepted Answer

The registration fee for the Google Cloud Professional Machine Learning Engineer certification exam is $200 USD, plus applicable taxes based on your region. Each retake also costs the full $200 USD fee. After a first failed attempt, you must wait 14 days before retaking. After a second failure, you must wait 60 days, and after a third failure, you must wait 365 days.

Question 14

What is the difference between the GCP ML Engineer and Data Engineer certifications?

Accepted Answer

The Professional Machine Learning Engineer certification focuses on building, training, deploying, and monitoring ML models and AI solutions on Google Cloud, with emphasis on Vertex AI, model serving, ML pipelines, and generative AI. The Professional Data Engineer certification focuses on designing data processing systems, building data pipelines, ensuring data quality, and operationalizing ML models. While there is overlap in data engineering concepts, the ML Engineer exam goes deeper into…

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Professional Machine Learning Engineer practice test

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Exam information

01Exam format120 min · 50-605

02Registration$2004

03Validity2 years2

04In our question bankv1 (241), GCP-PMLE-Ext (135)

What GCP-PMLE covers

01Architecting Low-Code AI Solutions13%3 topics

1.1Developing ML models by using BigQuery MLBuild appropriate BigQuery ML models (linear/binary classification, regression, time-series, matrix factorization, boosted trees, autoencoders) based on business problems6 obj.

1.2Building AI solutions by using ML APIs or foundation modelsBuild applications using ML APIs from Model Garden6 obj.

1.3Training models by using AutoMLPrepare data for AutoML (e.g., feature selection, data labeling, Tabular Workflows on AutoML)8 obj.

Hands-on skills & common mistakesCreating and training BigQuery ML models · Choosing custom training when BigQuery ML or AutoML would suffice

02Collaborating Within and Across Teams to Manage Data and Models14%3 topics

03Scaling Prototypes into ML Models18%3 topics

04Serving and Scaling Models20%2 topics

05Automating and Orchestrating ML Pipelines22%3 topics

06Monitoring AI Solutions13%2 topics