Professional Machine Learning Engineer Free Practice Questions
This is the free Google Professional Machine Learning Engineer practice question bank —
170 of 333 total questions, each with a full explanation, free to
read with no signup required. Updated 2026-04-24.
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see our methodology.
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 at upload time, which requires real-time (online) inference integrated into the upload flow. Training a supervised image classification model with labeled data and deploying it to a Vertex AI endpoint enables synchronous checks to block uploads immediately. AutoML is appropriate for this use case and simplifies model development, monitoring, and retraining. Other options either use unsupervised methods, rely on batch/after-the-fact review, or do not explicitly block uploads in real time.
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: A managed Vertex AI Pipeline provides a reliable and repeatable training workflow with built-in tracking of executions, artifacts, and lineage. Using CustomTrainingJobOp encapsulates training in a reproducible step, ModelUploadOp registers versions in the Model Registry, and triggering the pipeline weekly with Cloud Scheduler ensures consistent retraining. The other options rely on notebooks or ad-hoc jobs, which do not provide the same level of operational rigor, lineage, and version management.
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 and most efficient approach is to use BigQuery ML’s native support for TensorFlow models. BigQuery ML allows you to import a TensorFlow SavedModel from Cloud Storage using CREATE MODEL with MODEL_TYPE = 'TENSORFLOW' and then run inference directly with SQL. There is no separate concept of importing a model into plain BigQuery outside of BigQuery ML, so option D is imprecise. Options A and B add unnecessary deployment and serving complexity.
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 sacrificing accuracy, you need to increase compute resources rather than change the model or data. Options A and B would likely reduce model capacity or effective data, harming accuracy. Simply increasing CPU cores (option C) offers limited benefit because object detection training is GPU-bound. Using a machine with multiple GPUs (option D) enables parallel training and significantly speeds up training while preserving the same model and dataset.
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: The issue is that the generated summaries do not match the company’s brand voice. Fine-tuning the existing foundation model with a company-specific dataset teaches the model stylistic preferences, tone, and vocabulary aligned with business objectives. Replacing the model does not guarantee a better brand fit, and adjusting temperature or token limits only affects randomness or length, not writing style or voice.
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: Vertex AI endpoints are designed for online prediction with managed infrastructure. Deploying the model to an endpoint provides built-in autoscaling, load balancing, and optimized serving, which is the simplest way to achieve high throughput with low latency. Other options either add unnecessary complexity (Cloud Run), require model rework beyond the question’s scope, or focus on monitoring rather than serving performance.
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: Automatically retraining based on production performance requires continuous monitoring of live data and metrics. Vertex AI Model Monitoring is designed to track model performance and data drift in production and can trigger alerts or workflows when metrics fall below thresholds. The other options do not provide automated, production-grade performance monitoring tied directly to retraining.
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 core issue is not model accuracy but aligning response verbosity with user expectations in a scalable way. Prompt engineering is the most appropriate solution: by modifying the system or application prompt to encode when concise versus verbose responses are expected, you can dynamically guide the model without retraining, brittle rules, or costly experimentation. Keyword-based routing (A) is fragile and does not scale well, supervised fine-tuning (B) is expensive and requires substantial labeled data, and switching models (D) does not directly address verbosity control.
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 generate text that can closely mirror user writing styles and mimic famous authors, using a large proprietary dataset and hosting on a custom VM. This calls for a powerful generative LLM that can be fine-tuned on domain- and style-specific data. Llama 3 is a state-of-the-art open model designed for text generation and supports fine-tuning, making it suitable for learning stylistic nuances from author corpora. Fine-tuning via Vertex AI Pipelines aligns with scalable, production-grade training. BERT is not a generative model, prompt engineering alone is insufficient for deep style imitation, and Gemini Flash is primarily accessed as a managed API and not intended for custom fine-tuning or self-hosting on a VM.
Question 11
You work at an organization that maintains a cloud-based communication platform that integrates conventional chat, voice, and video conferencing into one platform. The audio recordings are stored in Cloud Storage. All recordings have a 16 kHz sample rate and are more than one minute long. You need to implement a new feature in the platform that will automatically transcribe voice call recordings into text for future applications, such as call summarization and sentiment analysis. How should you implement the voice call transcription feature while following Google-recommended practices?
A. Use the original audio sampling rate, and transcribe the audio by using the Speech-to-Text API with synchronous recognition.
B. Use the original audio sampling rate, and transcribe the audio by using the Speech-to-Text API with asynchronous recognition.
C. Downsample the audio recordings to 8 kHz, and transcribe the audio by using the Speech-to-Text API with synchronous recognition.
D. Downsample the audio recordings to 8 kHz, and transcribe the audio by using the Speech-to-Text API with asynchronous recognition.
Show Answer
Correct Answer: B
Explanation: The recordings are longer than one minute and stored in Cloud Storage, which aligns with Speech-to-Text asynchronous recognition. Google-recommended practice is to keep the original 16 kHz sample rate (commonly used for speech models) rather than downsampling, and use asynchronous recognition for longer, non-real-time transcription jobs.
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