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Google Professional Machine Learning Engineer Exam Questions

Exam Name: Google Professional Machine Learning Engineer
Exam Code: Professional Machine Learning Engineer
Related Certification(s):
  • Google Cloud Certified Certifications
  • Google Cloud Engineer Certifications
Certification Provider: Google
Number of Professional Machine Learning Engineer practice questions in our database: 283 (updated: Apr. 18, 2025)
Expected Professional Machine Learning Engineer Exam Topics, as suggested by Google :
  • Topic 1: Architecting low-code AI solutions: This section of the exam measures the skills of Google Machine Learning Engineers and covers developing machine learning models using BigQuery ML. It includes selecting appropriate models for business problems, such as linear and binary classification, regression, time series, matrix factorization, boosted trees, and autoencoders. Additionally, it involves feature engineering or selection and generating predictions using BigQuery ML.
  • Topic 2: Collaborating within and across teams to manage data and models: It explores and processes organization-wide data including Apache Spark, Cloud Storage, Apache Hadoop, Cloud SQL, and Cloud Spanner. The topic also discusses using Jupyter Notebooks to model prototypes. Lastly, it discusses tracking and running ML experiments.
  • Topic 3: Scaling prototypes into ML models: This topic covers building and training models. It also focuses on opting for suitable hardware for training.
  • Topic 4: Serving and scaling models: This section deals with Batch and online inference, using frameworks such as XGBoost, and managing features using VertexAI.
  • Topic 5: Automating and orchestrating ML pipelines: This topic focuses on developing end-to-end ML pipelines, automation of model retraining, and lastly tracking and auditing metadata.
  • Topic 6: Monitoring ML solutions: It identifies risks to ML solutions. Moreover, the topic discusses monitoring, testing, and troubleshooting ML solutions.
Disscuss Google Professional Machine Learning Engineer Topics, Questions or Ask Anything Related

Kenneth

21 days ago
Computer vision topics were well-represented. Study CNN architectures, transfer learning, and object detection algorithms.
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Daniel

1 months ago
Several questions on ML pipelines and MLOps. Understand the end-to-end ML lifecycle and tools for automating workflows.
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Casie

1 months ago
Thanks to Pass4Success, I crushed the Google ML Engineer exam. Highly recommend!
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Gladys

2 months ago
Natural Language Processing questions were challenging. Focus on text preprocessing, word embeddings, and transformer architectures.
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Ressie

2 months ago
Time series forecasting was more prominent than I expected. Review ARIMA, Prophet, and RNN-based approaches. Pass4Success materials covered this well.
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Ronnie

2 months ago
Google ML certification achieved! Pass4Success questions were key to my success.
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Clemencia

3 months ago
Ethics and responsible AI questions surprised me. Study bias in ML, fairness considerations, and model interpretability.
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Marta

3 months ago
Just passed the Google Professional Machine Learning Engineer exam! The Pass4Success practice questions were invaluable. There was a tricky question on architecting ML solutions, asking about the best practices for deploying models in a multi-cloud environment. I wasn't confident, but I still passed.
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Penney

3 months ago
The exam tested deep knowledge of TensorFlow. Make sure you're comfortable with building and training models using TF 2.x.
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Teddy

3 months ago
Grateful for Pass4Success - made studying for the Google ML exam so efficient.
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Stanford

4 months ago
Ensemble methods were well-represented in the exam. Understand bagging, boosting, and stacking algorithms. Pass4Success practice questions were really helpful here.
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Angelyn

4 months ago
Don't underestimate the importance of data validation and testing. Several questions on cross-validation techniques and performance metrics.
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Jonell

4 months ago
I passed the Google Professional Machine Learning Engineer exam, and the Pass4Success practice questions were a big help. One question that caught me off guard was about designing data preparation and processing systems, particularly on feature engineering techniques for time-series data. I was unsure, but I succeeded.
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Nickie

4 months ago
Pass4Success nailed it with their exam prep. Google ML cert in the bag!
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Noe

5 months ago
Glad I focused on Google Cloud AI Platform. Many questions on deploying and managing ML models in the cloud. Thanks, Pass4Success, for the comprehensive coverage!
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Blondell

5 months ago
Thrilled to announce that I passed the Google Professional Machine Learning Engineer exam! The Pass4Success practice questions were very useful. There was a question on framing ML problems, asking about the steps to convert a business problem into an ML problem. I wasn't entirely sure of my answer, but I still passed.
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Murray

5 months ago
Hyperparameter tuning was a significant part of the exam. Know various techniques like grid search, random search, and Bayesian optimization.
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Chaya

5 months ago
Google Professional ML Engineer? Check! Couldn't have done it without Pass4Success.
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Dorathy

5 months ago
I successfully passed the Google Professional Machine Learning Engineer exam, and the Pass4Success practice questions played a significant role. One question that puzzled me was about automating and orchestrating ML pipelines, specifically on the use of CI/CD tools for ML workflows. Despite my doubts, I managed to pass.
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Lenora

6 months ago
Neural network architecture questions were tricky. Study different types of layers and their functions. Pass4Success materials helped me grasp these concepts quickly.
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Carey

6 months ago
Happy to share that I passed the Google Professional Machine Learning Engineer exam! The Pass4Success practice questions were a great help. There was a question on monitoring, optimizing, and maintaining ML solutions, asking about the best metrics to monitor for model drift. I was unsure, but I still passed.
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Sage

6 months ago
The exam had a good mix of theory and practical scenarios. Be prepared to apply ML concepts to real-world problems. Understanding business requirements is key.
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Lura

6 months ago
I passed the Google Professional Machine Learning Engineer exam, thanks to the Pass4Success practice questions. One challenging question was about developing ML models, particularly on selecting the appropriate loss function for a classification problem. I wasn't confident in my answer, but I succeeded nonetheless.
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Theola

7 months ago
Wow, aced the Google ML certification! Pass4Success made prep a breeze.
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Salina

7 months ago
Encountered several questions on model selection. Know the pros and cons of different algorithms and when to use them. Pass4Success practice tests were spot-on for this topic!
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Theresia

7 months ago
Just cleared the Google Professional Machine Learning Engineer exam! The Pass4Success practice questions were a lifesaver. There was this tricky question on architecting ML solutions, specifically about choosing the right cloud infrastructure for a scalable model. I wasn't entirely sure about the optimal choice, but I still made it through.
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Georgene

7 months ago
Just passed the Google Professional Machine Learning Engineer exam! The questions on data preprocessing were challenging. Make sure to study feature scaling and handling missing data thoroughly.
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Beth

7 months ago
I recently passed the Google Professional Machine Learning Engineer exam, and I must say, the Pass4Success practice questions were incredibly helpful. One question that stumped me was about how to design a data preparation and processing system for a large-scale dataset. It asked about the best practices for handling missing data and ensuring data quality. Despite my uncertainty, I managed to pass!
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Margart

8 months ago
Just passed the Google ML Engineer exam! Thanks Pass4Success for the spot-on practice questions.
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Thaddeus

8 months ago
Passing the Google Professional Machine Learning Engineer exam was a great accomplishment for me. With the help of Pass4Success practice questions, I was able to tackle topics like development of ML models using BigQuery ML and tracking and running ML experiments. One question that I found particularly challenging was related to processing organization-wide data using Apache Spark. Despite my uncertainty, I was able to pass the exam successfully.
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Elfrieda

9 months ago
My experience with the Google Professional Machine Learning Engineer exam was challenging but rewarding. Thanks to Pass4Success practice questions, I was able to successfully navigate through topics like using ML APIs to build AI solutions and collaborating within and across teams to manage data and models. One question that I remember from the exam was about using Jupyter notebooks to model prototype. It was a tricky one, but I was able to make an educated guess and pass the exam.
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Jesse

10 months ago
Just passed the Google Professional ML Engineer exam! The MLOps questions were challenging, especially on model versioning and continuous integration. Make sure to study Vertex AI's model registry and CI/CD pipelines. Thanks to Pass4Success for their spot-on practice questions that helped me prepare efficiently!
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Caprice

10 months ago
I recently passed the Google Professional Machine Learning Engineer exam with the help of Pass4Success practice questions. The exam covered topics like architecting low-code ML solutions and collaborating within and across teams to manage data and models. One question that stood out to me was related to using AutoML to train models. I wasn't completely sure of the answer, but I still managed to pass the exam.
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Xochitl

10 months ago
I encountered several questions on model evaluation metrics. Be ready to interpret ROC curves and confusion matrices. Study various metrics for classification and regression problems, and know when to use each one. Pass4Success really helped me prepare for these types of questions quickly.
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petal

1 years ago
Wow, this Google Professional Machine Learning Engineer certification sounds fascinating! I'm curious, could you clarify how this certification addresses the challenge of ensuring responsible AI and fairness throughout the machine learning model development process?
upvoted 1 times
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Free Google Professional Machine Learning Engineer Exam Actual Questions

Note: Premium Questions for Professional Machine Learning Engineer were last updated On Apr. 18, 2025 (see below)

Question #1

You trained a text classification model. You have the following SignatureDefs:

What is the correct way to write the predict request?

Reveal Solution Hide Solution
Correct Answer: D

A predict request is a way to send data to a trained model and get predictions in return. A predict request can be written in different formats, such as JSON, protobuf, or gRPC, depending on the service and the platform that are used to host and serve the model. A predict request usually contains the following information:

The signature name: This is the name of the signature that defines the inputs and outputs of the model. A signature is a way to specify the expected format, type, and shape of the data that the model can accept and produce. A signature can be specified when exporting or saving the model, or it can be automatically inferred by the service or the platform. A model can have multiple signatures, but only one can be used for each predict request.

The instances: This is the data that is sent to the model for prediction. The instances can be a single instance or a batch of instances, depending on the size and shape of the data. The instances should match the input specification of the signature, such as the number, name, and type of the input tensors.

For the use case of training a text classification model, the correct way to write the predict request is D. data json.dumps({''signature_name'': ''serving_default'', ''instances'': [['a', 'b'], ['c', 'd'], ['e', 'f']]})

This option involves writing the predict request in JSON format, which is a common and convenient format for sending and receiving data over the web. JSON stands for JavaScript Object Notation, and it is a way to represent data as a collection of name-value pairs or an ordered list of values. JSON can be easily converted to and from Python objects using the json module.

This option also involves using the signature name ''serving_default'', which is the default signature name that is assigned to the model when it is saved or exported without specifying a custom signature name. The serving_default signature defines the input and output tensors of the model based on the SignatureDef that is shown in the image. According to the SignatureDef, the model expects an input tensor called ''text'' that has a shape of (-1, 2) and a type of DT_STRING, and produces an output tensor called ''softmax'' that has a shape of (-1, 2) and a type of DT_FLOAT. The -1 in the shape indicates that the dimension can vary depending on the number of instances, and the 2 indicates that the dimension is fixed at 2. The DT_STRING and DT_FLOAT indicate that the data type is string and float, respectively.

This option also involves sending a batch of three instances to the model for prediction. Each instance is a list of two strings, such as ['a', 'b'], ['c', 'd'], or ['e', 'f']. These instances match the input specification of the signature, as they have a shape of (3, 2) and a type of string. The model will process these instances and produce a batch of three predictions, each with a softmax output that has a shape of (1, 2) and a type of float. The softmax output is a probability distribution over the two possible classes that the model can predict, such as positive or negative sentiment.

Therefore, writing the predict request as data json.dumps({''signature_name'': ''serving_default'', ''instances'': [['a', 'b'], ['c', 'd'], ['e', 'f']]}) is the correct and valid way to send data to the text classification model and get predictions in return.


[json --- JSON encoder and decoder]

Question #2

You are developing an image recognition model using PyTorch based on ResNet50 architecture. Your code is working fine on your local laptop on a small subsample. Your full dataset has 200k labeled images You want to quickly scale your training workload while minimizing cost. You plan to use 4 V100 GPUs. What should you do? (Choose Correct Answer and Give Reference and Explanation)

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Correct Answer: B

The best option for scaling the training workload while minimizing cost is to package the code with Setuptools, and use a pre-built container. Train the model with Vertex AI using a custom tier that contains the required GPUs. This option has the following advantages:

It allows the code to be easily packaged and deployed, as Setuptools is a Python tool that helps to create and distribute Python packages, and pre-built containers are Docker images that contain all the dependencies and libraries needed to run the code. By packaging the code with Setuptools, and using a pre-built container, you can avoid the hassle and complexity of building and maintaining your own custom container, and ensure the compatibility and portability of your code across different environments.

It leverages the scalability and performance of Vertex AI, which is a fully managed service that provides various tools and features for machine learning, such as training, tuning, serving, and monitoring. By training the model with Vertex AI, you can take advantage of the distributed and parallel training capabilities of Vertex AI, which can speed up the training process and improve the model quality. Vertex AI also supports various frameworks and models, such as PyTorch and ResNet50, and allows you to use custom containers and custom tiers to customize your training configuration and resources.

It reduces the cost and complexity of the training process, as Vertex AI allows you to use a custom tier that contains the required GPUs, which can optimize the resource utilization and allocation for your training job. By using a custom tier that contains 4 V100 GPUs, you can match the number and type of GPUs that you plan to use for your training job, and avoid paying for unnecessary or underutilized resources. Vertex AI also offers various pricing options and discounts, such as per-second billing, sustained use discounts, and preemptible VMs, that can lower the cost of the training process.

The other options are less optimal for the following reasons:

Option A: Configuring a Compute Engine VM with all the dependencies that launches the training. Train the model with Vertex AI using a custom tier that contains the required GPUs, introduces additional complexity and overhead. This option requires creating and managing a Compute Engine VM, which is a virtual machine that runs on Google Cloud. However, using a Compute Engine VM to launch the training may not be necessary or efficient, as it requires installing and configuring all the dependencies and libraries needed to run the code, and maintaining and updating the VM. Moreover, using a Compute Engine VM to launch the training may incur additional cost and latency, as it requires paying for the VM usage and transferring the data and the code between the VM and Vertex AI.

Option C: Creating a Vertex AI Workbench user-managed notebooks instance with 4 V100 GPUs, and using it to train the model, introduces additional cost and risk. This option requires creating and managing a Vertex AI Workbench user-managed notebooks instance, which is a service that allows you to create and run Jupyter notebooks on Google Cloud. However, using a Vertex AI Workbench user-managed notebooks instance to train the model may not be optimal or secure, as it requires paying for the notebooks instance usage, which can be expensive and wasteful, especially if the notebooks instance is not used for other purposes. Moreover, using a Vertex AI Workbench user-managed notebooks instance to train the model may expose the model and the data to potential security or privacy issues, as the notebooks instance is not fully managed by Google Cloud, and may be accessed or modified by unauthorized users or malicious actors.

Option D: Creating a Google Kubernetes Engine cluster with a node pool that has 4 V100 GPUs. Prepare and submit a TFJob operator to this node pool, introduces additional complexity and cost. This option requires creating and managing a Google Kubernetes Engine cluster, which is a fully managed service that runs Kubernetes clusters on Google Cloud. Moreover, this option requires creating and managing a node pool that has 4 V100 GPUs, which is a group of nodes that share the same configuration and resources. Furthermore, this option requires preparing and submitting a TFJob operator to this node pool, which is a Kubernetes custom resource that defines a TensorFlow training job. However, using Google Kubernetes Engine, node pool, and TFJob operator to train the model may not be necessary or efficient, as it requires configuring and maintaining the cluster, the node pool, and the TFJob operator, and paying for their usage. Moreover, using Google Kubernetes Engine, node pool, and TFJob operator to train the model may not be compatible or scalable, as they are designed for TensorFlow models, not PyTorch models, and may not support distributed or parallel training.


[Vertex AI: Training with custom containers]

[Vertex AI: Using custom machine types]

[Setuptools documentation]

[PyTorch documentation]

[ResNet50 | PyTorch]

Question #3

You trained a model on data stored in a Cloud Storage bucket. The model needs to be retrained frequently in Vertex AI Training using the latest data in the bucket. Data preprocessing is required prior to retraining. You want to build a simple and efficient near-real-time ML pipeline in Vertex AI that will preprocess the data when new data arrives in the bucket. What should you do?

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Correct Answer: B

Cloud Run can be triggered on new data arrivals, which makes it ideal for near-real-time processing. The function then initiates the Vertex AI Pipeline for preprocessing and storing features in Vertex AI Feature Store, aligning with the retraining needs. Cloud Scheduler (Option A) is suitable for scheduled jobs, not event-driven triggers. Dataflow (Option C) is better suited for batch processing or ETL rather than ML preprocessing pipelines.


Question #4

You need to train a natural language model to perform text classification on product descriptions that contain millions of examples and 100,000 unique words. You want to preprocess the words individually so that they can be fed into a recurrent neural network. What should you do?

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Correct Answer: B

Option A is incorrect because creating a one-hot encoding of words, and feeding the encodings into your model is not an efficient way to preprocess the words individually for a natural language model.One-hot encoding is a method of representing categorical variables as binary vectors, where each element corresponds to a category and only one element is 1 and the rest are 01.However, this method is not suitable for high-dimensional and sparse data, such as words in a large vocabulary, because it requires a lot of memory and computation, and does not capture the semantic similarity or relationship between words2.

Option B is correct because identifying word embeddings from a pre-trained model, and using the embeddings in your model is a good way to preprocess the words individually for a natural language model.Word embeddings are low-dimensional and dense vectors that represent the meaning and usage of words in a continuous space3.Word embeddings can be learned from a large corpus of text using neural networks, such as word2vec, GloVe, or BERT4.Using pre-trained word embeddings can save time and resources, and improve the performance of the natural language model, especially when the training data is limited or noisy5.

Option C is incorrect because sorting the words by frequency of occurrence, and using the frequencies as the encodings in your model is not a meaningful way to preprocess the words individually for a natural language model. This method implies that the frequency of a word is a good indicator of its importance or relevance, which may not be true. For example, the word ''the'' is very frequent but not very informative, while the word ''unicorn'' is rare but more distinctive. Moreover, this method does not capture the semantic similarity or relationship between words, and may introduce noise or bias into the model.

Option D is incorrect because assigning a numerical value to each word from 1 to 100,000 and feeding the values as inputs in your model is not a valid way to preprocess the words individually for a natural language model. This method implies an ordinal relationship between the words, which may not be true. For example, assigning the values 1, 2, and 3 to the words ''apple'', ''banana'', and ''orange'' does not make sense, as there is no inherent order among these fruits. Moreover, this method does not capture the semantic similarity or relationship between words, and may confuse the model with irrelevant or misleading information.


One-hot encoding

Word embeddings

Word embedding

Pre-trained word embeddings

Using pre-trained word embeddings in a Keras model

[Term frequency]

[Term frequency-inverse document frequency]

[Ordinal variable]

[Encoding categorical features]

Question #5

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?

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Correct Answer: B

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.


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]


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