Exam Name: Google Professional Machine Learning Engineer
Certification Provider: Google
Corresponding Certification: Google Cloud Certified
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A Google certification keeps paying dividends long after the exam ends, and the Google Professional Machine Learning Engineer exam is the only toll gate. The 412 practice questions at NewPassLeader get you through it efficiently.
Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Deployment and operations | - Model deployment
|
| Topic 2: ML model development | - Model training and tuning
|
| Topic 3: Data preparation and processing | - Feature engineering
|
| Topic 4: ML pipeline automation and orchestration | - Pipeline design
|
| Topic 5: Designing ML solutions | - Framing ML problems
|
Professional-Machine-Learning-Engineer Exam FAQ: Pooling Efforts With Google Candidates
Google Professional Machine Learning Engineer is an official Google Cloud certification exam, registered under the code Professional-Machine-Learning-Engineer. Passing it awards the Google Cloud Professional Machine Learning Engineer certification, a credential at the Professional level. It also connects to Google Cloud Associate Cloud Engineer, Google Cloud Professional Data Engineer, Google Cloud Professional Cloud Architect. Successfully passing matters to every candidate because the credential keeps working for your career long after exam day.
You will answer Approximately 50–60 questions questions within 120 minutes on the Google Professional Machine Learning Engineer exam. That combination rewards candidates who practiced under realistic timing, so make timed sessions in the NewPassLeader engine a daily habit; one to two hours a day is enough when every minute rehearses the real thing.
Google Cloud recommends the following training for Google Professional Machine Learning Engineer candidates.
Training broadens your technology knowledge; the 412 practice questions in the NewPassLeader Professional-Machine-Learning-Engineer package sharpen it into exam-day scoring ability.
Sign-up for Google Professional Machine Learning Engineer runs through the official channels below.
One logistics note: the exam is delivered Online proctored exam or in-person testing via Kryterion test centers..
No formal prerequisites required, but 3+ years of industry experience in ML/AI and familiarity with Google Cloud Platform are strongly recommended.
Vendor requirements do change, so verify the current conditions before registering on the official exam page.
The Google Professional Machine Learning Engineer blueprint covers 5 domains, with the largest being Designing ML solutions, ML model development, and Data preparation and processing. The full topic list is above on this page; it tells you exactly where your daily hour or two earns the most marks.
Yes, download the free demo of the Google Professional Machine Learning Engineer questions before deciding, and read former customers' comments for an independent verdict. After purchase, new versions download free for one year, and when your product expires you can extend the update service at a 50% discount. Returning customers also enjoy bountiful discounts on future exams.
Your purchase carries a 100% money-back guarantee with defined conditions. Take the Google Professional Machine Learning Engineer exam within 60 days of purchase; if you fail, you may claim a full refund, provided the exam matches your product. Attempts within 3 days of purchase are ineligible, as are downloaded-but-unused products, free materials, and expired orders; the candidate name must match the payer name. Submit a scanned enrollment slip and the official Score Report PDF within 2 days of the exam, and claims are processed within 7 days. Alternatively, exchange for two other exam products of equal value, free, with your original purchase keeping its update service.
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Google Professional Machine Learning Engineer Sample Questions:
Your company's business stakeholders want to understand the factors driving customer churn to inform their business strategy. You need to build a customer churn prediction model that prioritizes simple interpretability of your model's results. You need to choose the ML framework and modeling technique that will explain which features led to the prediction. What should you do?
- A. Build a PyTorch long short-term memory (LSTM) network, and use attention mechanisms for interpretability.
- B. Build a TensorFlow deep neural network (DNN) model, and use SHAP values for feature importance analysis.
- C. Build a linear regression model in scikit-learn, and interpret the model's standardized coefficients to understand feature impact.
- D. Build a logistic regression model in scikit-learn, and interpret the model's output coefficients to understand feature impact.
Correct Answer: D 🗳️
Explanation: Only visible for NewPassLeader members. You can sign-up / login (it's free).
You work for an online retailer. Your company has a few thousand short lifecycle products. Your company has five years of sales data stored in BigQuery. You have been asked to build a model that will make monthly sales predictions for each product. You want to use a solution that can be implemented quickly with minimal effort. What should you do?
- A. Use BigQuery ML to build a statistical ARIMA_PLUS model.
- B. Use Vertex AI Forecast to build a NN-based model.
- C. Use Prophet on Vertex AI Training to build a custom model.
- D. Use TensorFlow on Vertex AI Training to build a custom model.
Correct Answer: A 🗳️
You need to build classification workflows over several structured datasets currently stored in BigQuery. Because you will be performing the classification several times, you want to complete the following steps without writing code: exploratory data analysis, feature selection, model building, training, and hyperparameter tuning and serving. What should you do?
- A. Train a classification Vertex AutoML model.
- B. Use scikit-learn in Notebooks with pandas library.
- C. Train a TensorFlow model on Vertex AI.
- D. Run a logistic regression job on BigQuery ML.
Correct Answer: A 🗳️
You work for a retail company. You have been tasked with building a model to determine the probability of churn for each customer. You need the predictions to be interpretable so the results can be used to develop marketing campaigns that target at-risk customers. What should you do?
- A. Build a random forest regression model in a Vertex AI Workbench notebook instance. Configure the model to generate feature importances after the model is trained.
- B. Build a random forest classification model in a Vertex AI Workbench notebook instance.
Configure the model to generate feature importances after the model is trained. - C. Build a custom TensorFlow neural network by using Vertex AI custom training. Configure the model to generate explanations when it makes predictions.
- D. Build an AutoML tabular regression model. Configure the model to generate explanations when it makes predictions.
Correct Answer: B 🗳️
You work for a gaming company that has millions of customers around the world. All games offer a chat feature that allows players to communicate with each other in real time. Messages can be typed in more than 20 languages and are translated in real time using the Cloud Translation API.
You have been asked to build an ML system to moderate the chat in real time while assuring that the performance is uniform across the various languages and without changing the serving infrastructure.
You trained your first model using an in-house word2vec model for embedding the chat messages translated by the Cloud Translation API. However, the model has significant differences in performance across the different languages. How should you improve it?
- A. Train a classifier using the chat messages in their original language.
- B. Add a regularization term such as the Min-Diff algorithm to the loss function.
- C. Remove moderation for languages for which the false positive rate is too high.
- D. Replace the in-house word2vec with GPT-3 or T5.
Correct Answer: A 🗳️
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