Google Professional Machine Learning Engineer Practice Exams
Last updated on Oct 10,2026- Exam Code: Professional Machine Learning Engineer
- Exam Name: Google Professional Machine Learning Engineer
- Certification Provider: Google
- Latest update: Oct 10,2026
You work with a team of researchers to develop state-of-the-art algorithms for financial analysis. Your team develops and debugs complex models in TensorFlow. You want to maintain the ease of debugging while also reducing the model training time.
How should you set up your training environment?
- A . Configure a v3-8 TPU VM SSH into the VM to tram and debug the model.
- B . Configure a v3-8 TPU node Use Cloud Shell to SSH into the Host VM to train and debug the model.
- C . Configure a M-standard-4 VM with 4 NVIDIA P100 GPUs SSH into the VM and use Parameter Server Strategy to train the model.
- D . Configure a M-standard-4 VM with 4 NVIDIA P100 GPUs SSH into the VM and use MultiWorkerMirroredStrategy to train the model.
A
Explanation:
A TPU VM is a virtual machine that has direct access to a Cloud TPU device. TPU VMs provide a simpler and more flexible way to use Cloud TPUs, as they eliminate the need for a separate host VM and network setup. TPU VMs also support interactive debugging tools such as TensorFlow Debugger (tfdbg) and Python Debugger (pdb), which can help researchers develop and troubleshoot complex models. A v3-8 TPU VM has 8 TPU cores, which can provide high performance and scalability for training large models. SSHing into the TPU VM allows the user to run and debug the TensorFlow code directly on the TPU device, without any network overhead or data transfer issues.
Reference: 1: TPU VMs Overview
2: TPU VMs Quickstart
3: Debugging TensorFlow Models on Cloud TPUs
You have recently created a proof-of-concept (POC) deep learning model. You are satisfied with the overall architecture, but you need to determine the value for a couple of hyperparameters. You want to perform hyperparameter tuning on Vertex AI to determine both the appropriate embedding dimension for a categorical feature used by your model and the optimal learning rate.
You configure the following settings:
For the embedding dimension, you set the type to INTEGER with a minValue of 16 and maxValue of 64.
For the learning rate, you set the type to DOUBLE with a minValue of 10e-05 and maxValue of 10e-02.
You are using the default Bayesian optimization tuning algorithm, and you want to maximize model accuracy. Training time is not a concern.
How should you set the hyperparameter scaling for each hyperparameter and the maxParallelTrials?
- A . Use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a large number of parallel trials.
- B . Use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a small number of parallel trials.
- C . Use UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a large number of parallel trials.
- D . Use UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a small number of parallel trials.
A
Explanation:
The best option for performing hyperparameter tuning on Vertex AI to determine the appropriate embedding dimension and the optimal learning rate is to use UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a large number of parallel trials.
This option has the following advantages:
It matches the appropriate scaling type for each hyperparameter, based on their range and distribution. The embedding dimension is an integer hyperparameter that varies linearly between 16 and 64, so using UNIT_LINEAR_SCALE makes sense. The learning rate is a double hyperparameter that varies exponentially between 10e-05 and 10e-02, so using UNIT_LOG_SCALE is more suitable. It maximizes the exploration of the hyperparameter space, by using a large number of parallel trials. Since training time is not a concern, using more trials can help find the best combination of hyperparameters that maximizes model accuracy. The default Bayesian optimization tuning algorithm can efficiently sample the hyperparameter space and converge to the optimal values.
The other options are less optimal for the following reasons:
Option B: Using UNIT_LINEAR_SCALE for the embedding dimension, UNIT_LOG_SCALE for the learning rate, and a small number of parallel trials, reduces the exploration of the hyperparameter space, by using a small number of parallel trials. Since training time is not a concern, using fewer trials can miss some potentially good combinations of hyperparameters that maximize model accuracy. The default Bayesian optimization tuning algorithm can benefit from more trials to sample the hyperparameter space and converge to the optimal values.
Option C: Using UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a large number of parallel trials, mismatches the appropriate scaling type for each hyperparameter, based on their range and distribution. The embedding dimension is an integer hyperparameter that varies linearly between 16 and 64, so using UNIT_LOG_SCALE is not suitable. The learning rate is a double hyperparameter that varies exponentially between 10e-05 and 10e-02, so using UNIT_LINEAR_SCALE makes less sense.
Option D: Using UNIT_LOG_SCALE for the embedding dimension, UNIT_LINEAR_SCALE for the learning rate, and a small number of parallel trials, combines the drawbacks of option B and option C. It mismatches the appropriate scaling type for each hyperparameter, based on their range and distribution, and reduces the exploration of the hyperparameter space, by using a small number of parallel trials.
Reference: [Vertex AI: Hyperparameter tuning overview]
[Vertex AI: Configuring the hyperparameter tuning job]
Feed the resulting BigQuery view into Vertex Al Training.
Explanation:
TFX (TensorFlow Extended) is a platform for end-to-end machine learning pipelines. It provides components for data ingestion, preprocessing, validation, model training, serving, and monitoring. Dataflow is a fully managed service for scalable data processing. By using TFX components with Dataflow, you can perform feature engineering on large-scale tabular data in a distributed and efficient way. You can use the Transform component to apply the MaxMin scaler and the one-hot encoding to the numerical and categorical features, respectively. You can also use the ExampleGen component to read data from BigQuery and the Trainer component to train your TensorFlow model. The output of the Transform component is a TFRecord file, which is a binary format for storing TensorFlow data. You can export the TFRecord file to Cloud Storage and feed it into Vertex AI Training, which is a managed service for training custom machine learning models on Google Cloud.
Reference: TFX | TensorFlow
Dataflow | Google Cloud
Vertex AI Training | Google Cloud
You work for a company that sells corporate electronic products to thousands of businesses worldwide. Your company stores historical customer data in BigQuery. You need to build a model that predicts customer lifetime value over the next three years. You want to use the simplest approach to build the model.
What should you do?
- A . Access BigQuery Studio in the Google Cloud console. Run the create model statement in the SQL editor to create an ARIMA model.
- B . Create a Vertex Al Workbench notebook. Use IPython magic to run the create model statement to
create an ARIMA model. - C . Access BigQuery Studio in the Google Cloud console. Run the create model statement in the SQL editor to create an AutoML regression model.
- D . Create a Vertex Al Workbench notebook. Use IPython magic to run the create model statement to create an AutoML regression model.
C
Explanation:
BigQuery ML allows you to build and run machine learning models using SQL queries directly within BigQuery, which is one of the simplest approaches because it doesn’t require setting up an external environment like Vertex AI or managing infrastructure.
AutoML regression is more appropriate for predicting customer lifetime value (CLV) compared to ARIMA, which is typically used for time series forecasting (e.g., sales over time, stock prices, etc.). CLV prediction involves understanding complex relationships between customer behavior and value, which is best captured by a regression model.
Using BigQuery Studio and running a CREATE MODEL statement to build an AutoML regression model offers the simplicity you’re looking for because it automates much of the feature engineering, model selection, and hyperparameter tuning.
The other options involving ARIMA models (A and B) are not appropriate for CLV, and setting up a Vertex AI Workbench notebook (D) introduces unnecessary complexity for this task.
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 . Create a serving pipeline in Compute Engine for prediction
- C . Use Cloud Functions for prediction each time a new data point is ingested
- D . Deploy the model on Al Platform and create a version of it for online inference.
A
Explanation:
Batch prediction is the process of using an ML model to make predictions on a large set of data points. Batch prediction is suitable for scenarios where the predictions are not time-sensitive and can be done in batches, such as digitizing scanned customer forms at the end of each day. Batch prediction can also handle large volumes of data and scale up or down the resources as needed. AI Platform provides a batch prediction service that allows users to submit a job with their TensorFlow model and input data stored in Cloud Storage, and receive the output predictions in Cloud Storage as well. This service requires minimal manual intervention and can be automated with Cloud Scheduler or Cloud Functions. Therefore, using the batch prediction functionality of AI Platform is the best option for this use case.
Reference: Batch prediction overview
Using batch prediction
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 . Create a serving pipeline in Compute Engine for prediction
- C . Use Cloud Functions for prediction each time a new data point is ingested
- D . Deploy the model on Al Platform and create a version of it for online inference.
A
Explanation:
Batch prediction is the process of using an ML model to make predictions on a large set of data points. Batch prediction is suitable for scenarios where the predictions are not time-sensitive and can be done in batches, such as digitizing scanned customer forms at the end of each day. Batch prediction can also handle large volumes of data and scale up or down the resources as needed. AI Platform provides a batch prediction service that allows users to submit a job with their TensorFlow model and input data stored in Cloud Storage, and receive the output predictions in Cloud Storage as well. This service requires minimal manual intervention and can be automated with Cloud Scheduler or Cloud Functions. Therefore, using the batch prediction functionality of AI Platform is the best option for this use case.
Reference: Batch prediction overview
Using batch prediction
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 . Add a regularization term such as the Min-Diff algorithm to the loss function.
- B . Train a classifier using the chat messages in their original language.
- C . Replace the in-house word2vec with GPT-3 or T5.
- D . Remove moderation for languages for which the false positive rate is too high.
B
Explanation:
The problem with the current approach is that it relies on the Cloud Translation API to translate the chat messages into a common language before embedding them with the in-house word2vec model. This introduces two sources of error: the translation quality and the word2vec quality. The translation quality may vary across different languages, depending on the availability of data and the complexity of the grammar and vocabulary. The word2vec quality may also vary depending on the size and diversity of the corpus used to train it. These errors may affect the performance of the classifier that moderates the chat messages, resulting in significant differences across the languages. A better approach would be to train a classifier using the chat messages in their original language, without relying on the Cloud Translation API or the in-house word2vec model. This way, the classifier can learn the nuances and subtleties of each language, and avoid the errors introduced by the translation and embedding processes. This would also reduce the latency and cost of the moderation system, as it would not need to invoke the Cloud Translation API for every message. To train a classifier using the chat messages in their original language, one could use a multilingual pre-trained model such as mBERT or XLM-R, which can handle multiple languages and domains. Alternatively, one could train a separate classifier for each language, using a monolingual pre-trained model such as BERT or a custom model tailored to the specific language and task.
Reference: Professional ML Engineer Exam Guide
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate Google Cloud launches machine learning engineer certification
[mBERT: Bidirectional Encoder Representations from Transformers]
[XLM-R: Unsupervised Cross-lingual Representation Learning at Scale]
[BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding]
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 . Add a regularization term such as the Min-Diff algorithm to the loss function.
- B . Train a classifier using the chat messages in their original language.
- C . Replace the in-house word2vec with GPT-3 or T5.
- D . Remove moderation for languages for which the false positive rate is too high.
B
Explanation:
The problem with the current approach is that it relies on the Cloud Translation API to translate the chat messages into a common language before embedding them with the in-house word2vec model. This introduces two sources of error: the translation quality and the word2vec quality. The translation quality may vary across different languages, depending on the availability of data and the complexity of the grammar and vocabulary. The word2vec quality may also vary depending on the size and diversity of the corpus used to train it. These errors may affect the performance of the classifier that moderates the chat messages, resulting in significant differences across the languages. A better approach would be to train a classifier using the chat messages in their original language, without relying on the Cloud Translation API or the in-house word2vec model. This way, the classifier can learn the nuances and subtleties of each language, and avoid the errors introduced by the translation and embedding processes. This would also reduce the latency and cost of the moderation system, as it would not need to invoke the Cloud Translation API for every message. To train a classifier using the chat messages in their original language, one could use a multilingual pre-trained model such as mBERT or XLM-R, which can handle multiple languages and domains. Alternatively, one could train a separate classifier for each language, using a monolingual pre-trained model such as BERT or a custom model tailored to the specific language and task.
Reference: Professional ML Engineer Exam Guide
Preparing for Google Cloud Certification: Machine Learning Engineer Professional Certificate Google Cloud launches machine learning engineer certification
[mBERT: Bidirectional Encoder Representations from Transformers]
[XLM-R: Unsupervised Cross-lingual Representation Learning at Scale]
[BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding]
One of your models is trained using data provided by a third-party data broker. The data broker does not reliably notify you of formatting changes in the data. You want to make your model training pipeline more robust to issues like this.
What should you do?
- A . Use TensorFlow Data Validation to detect and flag schema anomalies.
- B . Use TensorFlow Transform to create a preprocessing component that will normalize data to the expected distribution, and replace values that don’t match the schema with 0.
- C . Use tf.math to analyze the data, compute summary statistics, and flag statistical anomalies.
- D . Use custom TensorFlow functions at the start of your model training to detect and flag known formatting errors.
A
Explanation:
TensorFlow Data Validation (TFDV) is a library that helps you understand, validate, and monitor your data for machine learning. It can automatically detect and report schema anomalies, such as missing features, new features, or different data types, in your data. It can also generate descriptive statistics and data visualizations to help you explore and debug your data. TFDV can be integrated with your model training pipeline to ensure data quality and consistency throughout the machine learning lifecycle.
Reference: TensorFlow Data Validation
Data Validation | TensorFlow
Data Validation | Machine Learning Crash Course | Google Developers
You work for a company that captures live video footage of checkout areas in their retail stores You need to use the live video footage to build a mode! to detect the number of customers waiting for service in near real time You want to implement a solution quickly and with minimal effort.
How should you build the model?
- A . Use the Vertex Al Vision Occupancy Analytics model.
- B . Use the Vertex Al Vision Person/vehicle detector model
- C . Train an AutoML object detection model on an annotated dataset by using Vertex AutoML
- D . Train a Seq2Seq+ object detection model on an annotated dataset by using Vertex AutoML
A
Explanation:
According to the official exam guide1, one of the skills assessed in the exam is to “design, build, and productionalize ML models to solve business challenges using Google Cloud technologies”. The Vertex AI Vision Occupancy Analytics model2 is a specialized pre-built vision model that lets you count people or vehicles given specific inputs you add in video frames. It provides advanced features such as active zones counting, line crossing counting, and dwelling detection. This model is suitable for the use case of detecting the number of customers waiting for service in near real time. You can easily create and deploy an occupancy analytics application using Vertex AI Vision3. The other options are not relevant or optimal for this scenario.
Reference: Professional ML Engineer Exam Guide
Occupancy analytics guide
Create an occupancy analytics app with BigQuery forecasting Google Professional Machine Learning Certification Exam 2023
Latest Google Professional Machine Learning Engineer Actual Free Exam Questions