Professional Machine Learning Engineer Free Practice Questions
This is the free Google Professional Machine Learning Engineer practice question bank —
170 of 332 total questions, each with a full explanation, free to
read with no signup required. Updated 2026-08-06.
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Question 1
You are an AI architect at a popular photo sharing social media platform. Your organization's content moderation team currently scans images uploaded by users and removes explicit images manually. You want to implement an AI service to automatically prevent users from uploading explicit images. What should you do?
A. Train an image clustering model by using TensorFlow in a Vertex AI Workbench instance. Deploy this model to a Vertex AI endpoint and configure it for online inference. Run this model each time a new image is uploaded to identify and block inappropriate uploads.
B. Develop a custom TensorFlow model in a Vertex AI Workbench instance. Train the model on a dataset of manually labeled images. Deploy the model to a Vertex AI endpoint. Run periodic batch inference to identify inappropriate uploads and report them to the content moderation team.
C. Create a dataset using manually labeled images. Ingest this dataset into AutoML. Train an image classification model and deploy into a Vertex AI endpoint. Integrate this endpoint with the image upload process to identify and block inappropriate uploads. Monitor predictions and periodically retrain the model.
D. Send a copy of every user-uploaded image to a Cloud Storage bucket. Configure a Cloud Run function that triggers the Cloud Vision API to detect explicit content each time a new image is uploaded. Report the classifications to the content moderation team for review.
Show Answer
Correct Answer: C
Explanation: The requirement is to prevent explicit images from being uploaded, which requires real-time online inference integrated into the upload path. Image clustering is an unsupervised technique and is not appropriate for explicit-content classification. Batch inference cannot prevent uploads because it runs after the fact. Using AutoML image classification with a manually labeled dataset, deploying the model to a Vertex AI endpoint, integrating it into the upload flow, and periodically retraining based on monitored predictions matches the stated requirements.
Question 2
You have recently developed a new ML model in a Jupyter notebook. You want to establish a reliable and repeatable model training process that tracks the versions and lineage of your model artifacts. You plan to retrain your model weekly. How should you operationalize your training process?
A. 1. Create an instance of the CustomTrainingJob class with the Vertex AI SDK to train your model. 2. Using the Notebooks API, create a scheduled execution to run the training code weekly.
B. 1. Create an instance of the CustomJob class with the Vertex AI SDK to train your model. 2. Use the Metadata API to register your model as a model artifact. 3. Using the Notebooks API, create a scheduled execution to run the training code weekly.
C. 1. Create a managed pipeline in Vertex AI Pipelines to train your model by using a Vertex AI CustomTrainingJobOp component. 2. Use the ModelUploadOp component to upload your model to Vertex AI Model Registry. 3. Use Cloud Scheduler and Cloud Run functions to run the Vertex AI pipeline weekly.
D. 1. Create a managed pipeline in Vertex AI Pipelines to train your model using a Vertex AI HyperParameterTuningJobRunOp component. 2. Use the ModelUploadOp component to upload your model to Vertex AI Model Registry. 3. Use Cloud Scheduler and Cloud Run functions to run the Vertex AI pipeline weekly.
Show Answer
Correct Answer: C
Explanation: Vertex AI Pipelines provides a reliable, repeatable training workflow with automatic metadata and lineage tracking. Using CustomTrainingJobOp encapsulates the training step, ModelUploadOp registers the resulting model in Vertex AI Model Registry for versioning, and scheduling the pipeline weekly with Cloud Scheduler invoking a Cloud Run function provides recurring retraining. Hyperparameter tuning is not a stated requirement, making the tuning job option unnecessary. Notebook scheduling and standalone custom jobs do not provide the same operational robustness and lineage management as managed pipelines.
Question 3
You are a SQL analyst. You need to utilize a TensorFlow customer segmentation model stored In Cloud Storage. You want to use the simplest and most efficient approach. What should you do?
A. Import the model into Vertex AI Model Registry. Deploy the model to a Vertex AI endpoint, and use SQL for inference in BigQuery.
B. Deploy the model by using TensorFlow Serving, and call for inference from BigQuery.
C. Convert the model into a BigQuery ML model, and use SQL for inference.
D. Import the model into BigQuery, and use SQL for inference.
Show Answer
Correct Answer: C
Explanation: The simplest approach is to import the TensorFlow SavedModel from Cloud Storage into BigQuery ML using the CREATE MODEL statement, creating an imported BigQuery ML model. You then run inference directly in SQL with ML.PREDICT. Option D is imprecise because there is no standalone feature to 'import into BigQuery' outside the BigQuery ML import process.
Sources:
https://cloud.google.com/bigquery/docs/making-predictions-with-imported-tensorflow-models
Question 4
You built a custom Vertex AI pipeline job that preprocesses images and trains an object detection model. The pipeline currently uses 1 n1-standard-8 machine with 1 NVIDIA Tesla V100 GPU. You want to reduce the model training time without compromising model accuracy. What should you do?
A. Reduce the number of layers in your object detection model.
B. Train the same model on a stratified subset of your dataset.
C. Update the WorkerPoolSpec to use a machine with 24 vCPUs and 1 NVIDIA Tesla V100 GPU.
D. Update the WorkerPoolSpec to use a machine with 24 vCPUs and 3 NVIDIA Tesla V100 GPUs.
Show Answer
Correct Answer: D
Explanation: To reduce training time without compromising model accuracy, increase training compute rather than changing the model architecture or reducing the dataset. Adding more GPUs enables distributed or parallel training, whereas simply increasing CPU cores while keeping a single GPU is unlikely to provide a comparable reduction in GPU-bound training time.
Question 5
Your company needs to generate product summaries for vendors. You evaluate a foundation model from Model Garden for text summarization and find the style of the summaries are not aligned with your company's brand voice. How should you improve this LLM-based summarization model to better meet your business objectives?
A. Replace the pre-trained model with another model in Model Garden.
B. Fine-tune the model using a company-specific dataset.
C. Increase the model's temperature parameter.
D. Tune the token output limit in the response.
Show Answer
Correct Answer: B
Explanation: Fine-tuning the foundation model on a company-specific dataset is the appropriate way to adapt its output style and tone to match a brand voice while retaining its summarization capability. Replacing the model may not produce the desired style, and adjusting temperature or token limits affects randomness or length rather than aligning outputs with a specific writing style.
Question 6
You have developed a custom ML model using Vertex AI and want to deploy it for online serving. You need to optimize the model's serving performance by ensuring that the model can handle high throughput while minimizing latency. You want to use the simplest solution. What should you do?
A. Deploy the model to a Vertex AI endpoint resource to automatically scale the serving backend based on the throughput. Configure the endpoint's autoscaling settings to minimize latency.
B. Implement a containerized serving solution using Cloud Run. Configure the concurrency settings to handle multiple requests simultaneously.
C. Apply simplification techniques such as model pruning and quantization to reduce the model's size and complexity. Retrain the model using Vertex AI to improve its performance, latency, memory, and throughput.
D. Enable request-response logging for the model hosted in Vertex AI. Use Looker Studio to analyze the logs, identify bottlenecks, and optimize the model accordingly.
Show Answer
Correct Answer: A
Explanation: Deploying a custom model to a Vertex AI Endpoint is the managed and simplest approach for online serving. Vertex AI Endpoints support autoscaling of serving resources to accommodate throughput while helping minimize latency through appropriate autoscaling configuration. Cloud Run requires building and managing the serving stack yourself, model optimization techniques are beneficial but do not directly provide scalable serving infrastructure, and request-response logging is for observability rather than performance optimization.
Question 7
You are responsible for managing and monitoring a Vertex AI model that is deployed in production. You want to automatically retrain the model when its performance deteriorates. What should you do?
A. Create a Vertex AI Model Monitoring job to track the model's performance with production data, and trigger retraining when specific metrics drop below predefined thresholds.
B. Collect feedback from end users, and retrain the model based on their assessment of its performance.
C. Configure a scheduled job to evaluate the model's performance on a static dataset, and retrain the model if the performance drops below predefined thresholds.
D. Use Vertex Explainable AI to analyze feature attributions and identify potential biases in the model. Retrain when significant shifts in feature importance or biases are detected.
Show Answer
Correct Answer: A
Explanation: Vertex AI Model Monitoring is designed to monitor deployed models using production data for issues such as prediction drift and, depending on setup, performance when ground truth is available. It is the appropriate mechanism to trigger automated retraining workflows when monitored metrics cross predefined thresholds. The other options rely on manual feedback, static offline evaluation, or explainability rather than continuous production monitoring.
Question 8
You are using Vertex AI to manage your ML models and datasets. You recently updated one of your models. You want to track and compare the new version with the previous one and incorporate dataset versioning. What should you do?
A. Use Vertex AI TensorBoard to visualize the training metrics of the new model version, and use Data Catalog to manage dataset versioning.
B. Use Vertex AI Model Monitoring to monitor the performance of the new model version, and use Vertex AI Training to manage dataset versioning.
C. Use Vertex AI Experiments to track and compare model artifacts and versions, and use Vertex ML Metadata to manage dataset versioning.
D. Use Vertex AI Experiments to track and compare model artifacts and versions, and use Vertex AI managed datasets to manage dataset versioning.
Show Answer
Correct Answer: D
Explanation: Vertex AI Experiments is designed to track, compare, and organize experiment runs, model artifacts, and model versions. For dataset management, Vertex AI managed datasets provide dataset versioning capabilities, making them the appropriate service for managing dataset versions. TensorBoard is for visualization, Model Monitoring is for deployed model monitoring, and ML Metadata is a lower-level lineage/metadata store rather than the primary dataset version management feature.
Question 9
You deployed a conversational application that uses a large language model (LLM). The application has 1,000 users. You collect user feedback about the verbosity and accuracy of the model 's responses. The user feedback indicates that the responses are factually correct but users want different levels of verbosity depending on the type of question. You want the model to return responses that are more consistent with users' expectations, and you want to use a scalable solution. What should you do?
A. Implement a keyword-based routing layer. If the user's input contains the words "detailed" or "description," return a verbose response. If the user's input contains the word "fact." re-prompt the language model to summarize the response and return a concise response.
B. Ask users to provide examples of responses with the appropriate verbosity as a list of question and answer pairs. Use this dataset to perform supervised fine tuning of the foundational model. Re-evaluate the verbosity of responses with the tuned model.
C. Ask users to indicate all scenarios where they expect concise responses versus verbose responses. Modify the application 's prompt to include these scenarios and their respective verbosity levels. Re-evaluate the verbosity of responses with updated prompts.
D. Experiment with other proprietary and open-source LLMs. Perform A/B testing by setting each model as your application's default model. Choose a model based on the results.
Show Answer
Correct Answer: C
Explanation: The issue is response style (verbosity), not factual accuracy. The most scalable first approach is prompt engineering: encode guidance about when to be concise versus detailed based on scenarios, then re-evaluate. Fine-tuning is heavier and generally unnecessary for formatting/style preferences when prompting can address them. Keyword routing is brittle and does not generalize well, while switching models does not directly solve the requirement.
Sources:
https://hamel.dev/blog/posts/evals-faq
Question 10
You are developing an AI text generator that will be able to dynamically adapt its generated responses to mirror the writing style of the user and mimic famous authors if their style is detected. You have a large dataset of various authors' works, and you plan to host the model on a custom VM. You want to use the most effective model. What should you do?
A. Deploy Llama 3 from Model Garden, and use prompt engineering techniques.
B. Fine-tune a BERT-based model from TensorFlow Hub.
C. Fine-tune Llama 3 from Model Garden on Vertex AI Pipelines.
D. Use the Gemini 1.5 Flash foundational model to build the text generator.
Show Answer
Correct Answer: C
Explanation: The requirement is to build a text generation model that can closely emulate writing styles using a large dataset of authors' works while hosting on a custom VM. A generative Llama 3 model is appropriate for text generation and can be fine-tuned on the provided corpus to learn stylistic patterns. BERT is an encoder model, not suited for generative text generation. Prompt engineering alone does not leverage the large labeled corpus as effectively as fine-tuning. While Gemini is a strong foundation model, the scenario emphasizes using a custom-hosted model and adapting it with the available dataset, making a fine-tuned open model from Model Garden the best fit.
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