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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.
Exam Details
The Google Professional Machine Learning Engineer exam is two hours long. The candidates can expect multiple-choice as well as multiple-select questions in their delivery of the certification test. The exam is currently given to the learners in the English language. To register for and schedule it, you need to pay $200 (plus applicable taxes). While registering for the test, the potential applicants will be offered to select the convenient mode of exam delivery: an online proctored session from a remote location or an in-person proctored session at the nearest testing center.
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Collaborate to manage data and models | 16% | - Manage datasets and features in Vertex AI - Address data privacy, compliance, and governance - Organize and prepare enterprise data
|
| Topic 2: Automate and orchestrate ML pipelines | 18% | - Automate retraining and model updates - Design end-to-end ML workflows - Implement CI/CD for ML systems - Use Vertex AI Pipelines, TFX, and other orchestration tools |
| Topic 3: Scale prototypes into AI models | 18% | - Work with foundation models and generative AI techniques - Optimize model performance and generalization - Design and run experiments - Select appropriate model architectures and frameworks |
| Topic 4: Train and deploy models | 20% | - Deploy models for online, batch, and streaming prediction - Use Vertex AI deployment features and infrastructure - Implement generative AI deployment patterns - Configure training jobs and environments |
| Topic 5: Architect low-code AI solutions | 12% | - Identify use cases for low-code/no-code AI tools - Apply responsible AI principles to low-code designs - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder |
| Topic 6: Monitor and optimize AI solutions | 16% | - Monitor model performance, fairness, and drift - Monitor data quality and pipeline health - Optimize cost, latency, and resource usage - Troubleshoot and maintain production systems |
Google Professional Machine Learning Engineer Sample Questions:
Your e-commerce platform serves customers in 14 languages. You need to build semantic product search so that a query in Spanish returns relevant products whose descriptions are written in English or Japanese. You want to avoid maintaining a separate index per language.
What should you do?
- A. Fine-tune a monolingual English embedding model on translated product descriptions.
- B. Translate every query into English with the Cloud Translation API, and use a keyword-based BM25 index over English descriptions.
- C. Train a separate embedding model per language, and query all 14 indexes in parallel, merging results by score.
- D. Generate embeddings for queries and product descriptions with a multilingual text embedding model, store them in a single Vector Search index, and retrieve by approximate nearest neighbor.
Correct Answer: D 🗳️
Explanation: Only visible for Exam4Free members. You can sign-up / login (it's free).
You built a deep learning-based image classification model by using on-premises data. You want to use Vertex AI to deploy the model to production. Due to security concerns, you cannot move your data to the cloud. You are aware that the input data distribution might change over time. You need to detect model performance changes in production. What should you do?
- A. Create a Vertex AI Model Monitoring job. Enable feature attribution skew and drift detection for your model.
- B. Use Vertex Explainable AI for model explainability. Configure feature-based explanations.
- C. Use Vertex Explainable AI for model explainability. Configure example-based explanations.
- D. Create a Vertex AI Model Monitoring job. Enable training-serving skew detection for your model.
Correct Answer: A 🗳️
You have recently used TensorFlow to train a classification model on tabular data. You have created a Dataflow pipeline that can transform several terabytes of data into training or prediction datasets consisting of TFRecords. You now need to productionize the model, and you want the predictions to be automatically uploaded to a BigQuery table on a weekly schedule. What should you do?
- A. Import the model into Vertex AI and deploy it to a Vertex AI endpoint. Create a Dataflow pipeline that reuses the data processing logic sends requests to the endpoint, and then uploads predictions to a BigQuery table.
- B. Import the model into BigQuery. Implement the data processing logic in a SQL query. On Vertex AI Pipelines create a pipeline that uses the BigquervQueryJobOp and the BigqueryPredictModelJobOp components.
- C. Import the model into Vertex AI and deploy it to a Vertex AI endpoint. On Vertex AI Pipelines, create a pipeline that uses the DataflowPythonJobOp and the ModelBacthPredictOp components.
- D. Import the model into Vertex AI. On Vertex AI Pipelines, create a pipeline that uses the DataflowPvthonJobOp and the ModelBatchPredictOp components.
Correct Answer: A 🗳️
Your company recently migrated several of is ML models to Google Cloud. You have started developing models in Vertex AI. You need to implement a system that tracks model artifacts and model lineage. You want to create a simple, effective solution that can also be reused for future models. What should you do?
- A. Use Vertex AI Pipelines for model artifacts and MLflow for model lineage.
- B. Use Vertex AI Experiments for model artifacts and use Vertex ML Metadata for model lineage.
- C. Use a combination of Vertex AI Pipelines and the Vertex AI SDK to integrate metadata tracking into the ML workflow.
- D. Implement a scheduled metadata tracking solution using Cloud Composer and Cloud Run functions.
Correct Answer: C 🗳️
Explanation: Only visible for Exam4Free members. You can sign-up / login (it's free).
Your company stores a large number of audio files of phone calls made to your customer call center in an on-premises database. Each audio file is in wav format and is approximately 5 minutes long. You need to analyze these audio files for customer sentiment. You plan to use the Speech-to-Text API You want to use the most efficient approach. What should you do?
- A. 1. Iterate over your local files in Python
2. Use the Speech-to-Text Python library to create a speech.RecognitionAudio object, and set the content to the audio file data
3. Call the speech:recognize API endpoint to generate transcriptions
4. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions. - B. 1. Iterate over your local files in Python
2. Use the Speech-to-Text Python Library to create a speech.RecognitionAudio object and set the content to the audio file data
3. Call the speech:longrunningrecognize API endpoint to generate transcriptions.
4. Call the Natural Language API by using the analyzeSentiment method - C. 1. Upload the audio files to Cloud Storage
2. Call the speech:longrunningrecognize API endpoint to generate transcriptions
3. Call the predict method of an AutoML sentiment analysis model to analyze the transcriptions. - D. 1. Upload the audio files to Cloud Storage.
2. Call the speech:longrunningrecognize API endpoint to generate transcriptions
3. Create a Cloud Function that calls the Natural Language API by using the analyzeSentiment method
Correct Answer: C 🗳️
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