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Google Professional ML Engineer Exam (Professional Machine Learning Engineer Exam) - Google Cloud Exam Questions

Last updated on June 20, 2026

97% Exam Compliance
283 Total Questions
1
Question
You are working on a prototype of a text classification model in a managed Vertex AI Workbench notebook. You want to quickly experiment with tokenizing text by using a Natural Language Toolkit (NLTK) library. How should you add the library to your Jupyter kernel?
Options
A Write a custom Dataflow job that uses NLTK to tokenize your text and saves the output to Cloud Storage.
B Create a new Vertex Al Workbench notebook with a custom image that includes the NLTK library.
C Install the NLTK library from a terminal by using the pip install nltk command.
D Install the NLTK library from a Jupyter cell by using the! pip install nltk —user command.
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2
Question
You work for a rapidly growing social media company. Your team builds TensorFlow recommender models in an on-premises CPU cluster. The data contains billions of historical user events and 100 000 categorical features. You notice that as the data increases the model training time increases. You plan to move the models to Google Cloud You want to use the most scalable approach that also minimizes training time. What should you do?
Options
A nn. embedding_lookup API.
B Deploy the training jobs in an autoscaling Google Kubernetes Engine cluster with CPUs
C Deploy a matrix factorization model training job by using BigQuery ML.
D Deploy the training jobs by using TPU VMs with TPUv3 Pod slices, and use the TPUEmbedding API.
E Deploy the training jobs by using Compute Engine instances with A100 GPUs and use the t
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3
Question
You work for an international manufacturing organization that ships scientific products all over the world Instruction manuals for these products need to be translated to 15 different languages Your organization's leadership team wants to start using machine learning to reduce the cost of manual human translations and increase translation speed. You need to implement a scalable solution that maximizes accuracy and minimizes operational overhead. You also want to include a process to evaluate and fix incorrect translations. What should you do?
Options
A Create a Vertex Al pipeline that processes the documents1 launches an AutoML Translation training job evaluates the translations, and deploys the model to a Vertex Al endpoint with autoscaling and model monitoring When there is a predetermined skew between training and live data re-trigger the pipeline with the latest data.
B Use AutoML Translation to tram a model Configure a Translation Hub project and use the trained model to translate the documents Use human reviewers to evaluate the incorrect translations
C Create a workflow using Cloud Function Triggers Configure a Cloud Function that is triggered when documents are uploaded to an input Cloud Storage bucket Configure another Cloud Function that translates the documents using the Cloud Translation API and saves the translations to an output Cloud Storage bucket Use human reviewers to evaluate the incorrect translations.
D Use Vertex Al custom training jobs to fine-tune a state-of-the-art open source pretrained model with your data Deploy the model to a Vertex Al endpoint with autoscaling and model monitoring When there is a predetermined skew between the training and live data, configure a trigger to run another training job with the latest data.
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4
Question
You have created a Vertex Al pipeline that includes two steps. The first step preprocesses 10 TB data completes in about 1 hour, and saves the result in a Cloud Storage bucket The second step uses the processed data to train a model You need to update the model's code to allow you to test different algorithms You want to reduce pipeline execution time and cost, while also minimizing pipeline changes What should you do?
Options
A Create another pipeline without the preprocessing step, and hardcode the preprocessed Cloud Storage file location for model training.
B Configure a machine with more CPU and RAM from the compute-optimized machine family for the data preprocessing step.
C Add a pipeline parameter and an additional pipeline step Depending on the parameter value the pipeline step conducts or skips data preprocessing and starts model training.
D Enable caching for the pipeline job. and disable caching for the model training step.
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5
Question
You are developing a Kubeflow pipeline on Google Kubernetes Engine. The first step in the pipeline is to issue a query against BigQuery. You plan to use the results of that query as the input to the next step in your pipeline. You want to achieve this in the easiest way possible. What should you do?
Options
A Write a Python script that uses the BigQuery API to execute queries against BigQuery Execute this script as the first step in your Kubeflow pipeline
B Use the Kubeflow Pipelines domain-specific language to create a custom component that uses the
Python BigQuery client library to execute queries
C Use the BigQuery console to execute your query and then save the query results Into a new BigQuery table.
D Locate the Kubeflow Pipelines repository on GitHub Find the BigQuery Query Component, copy that component's URL, and use it to load the component into your pipeline. Use the component to execute queries against BigQuery
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