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Solving business challenges with ML practice questions

Practise Google Professional Machine Learning Engineer Solving business challenges with ML practice questions — original exam-style scenarios with answer choices, explanations, and analysis of common mistakes.

Courseiva uses original exam-style practice questions designed for learning and revision. The goal is to understand the concepts, recognise exam patterns, and improve through explanations — not memorise copied exam dumps.

Reviewed byJohnson Ajibi· MSc IT Security
20 questionsDomain: Solving business challenges with ML
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What the exam tests

What to know about Solving business challenges with ML

Solving business challenges with ML questions test whether you can apply the concept in context, not just recognise a definition.

How the topic appears in realistic exam-style scenarios.

Which detail in the question changes the correct answer.

How to eliminate plausible but wrong options.

How to connect the question back to the wider exam objective.

Watch out for

Common Solving business challenges with ML exam traps

  • ▸Answering from memory before reading the full scenario.
  • ▸Missing a constraint such as cost, availability, security, scope or command context.
  • ▸Choosing a broad answer when the question asks for the most specific fix.
  • ▸Ignoring why the wrong options are tempting.

Practice set

Solving business challenges with ML questions

20 questions · select your answer, then reveal the explanation

Question 1easymultiple choice
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A retail company wants to forecast weekly sales for each of its 500 stores. The data includes historical sales, promotions, holidays, and local weather. The company needs to update forecasts every week with new data. Which ML approach should they use?

Question 2mediummulti select
Study the full Python automation breakdown →

A media company uses a custom Python script on a Compute Engine VM to run batch predictions with a large ML model. The script loads the model from Cloud Storage, processes records from a Pub/Sub pull subscription, and writes results to BigQuery. Predictions are taking too long and the VM often runs out of memory. Which two changes should the company implement to improve performance and scalability? (Choose TWO)

Question 3hardmultiple choice
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A hospital wants to deploy a machine learning model for detecting anomalies in patient vital signs. The model was trained on historical data but must comply with HIPAA regulations. The model serving must be low-latency (under 100 ms) and handle up to 1000 requests per second. Which architecture should they use on Google Cloud?

Question 4mediummultiple choice
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A data scientist deployed a TensorFlow model for sentiment analysis to Vertex AI Prediction. The model expects input key 'text' but the client sends requests with key 'review_text'. Which step should the data scientist take to resolve the error without retraining the model?

Exhibit

Refer to the exhibit.
```
Error: INVALID_ARGUMENT: Model 'projects/my-project/models/sentiment_v2' failed to load. The model's signature definition does not match the prediction request.
Expected: input: 'text' (string), output: 'scores' (float array)
Received: input: 'review_text' (string)
```
Question 5mediummultiple choice
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A logistics company uses a regression model to predict delivery times. The model currently uses features: distance (km), traffic index, weather condition, and time of day. The data scientist notices that the model's predictions are systematically too low for deliveries during peak traffic hours. Which action would best address this issue?

Question 6hardmulti select
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An e-commerce company uses a recommendation model that suggests products based on user browsing history. The model was trained on data from the past year and has high accuracy on the test set. However, after deployment, the click-through rate (CTR) on recommendations is much lower than expected. Which three steps should the data scientist take to diagnose and improve the model? (Choose THREE)

Question 7easymultiple choice
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A data scientist runs a BigQuery ML prediction query and gets a region mismatch error. The model is in the US region, but the new_data table is in the EU region. What is the simplest way to resolve this?

Network Topology
$ bq queryuse_legacy_sql=false 'Refer to the exhibit.```SELECTml.PREDICT(MODEL `mydataset.my_model`,(SELECT * FROM `mydataset.new_data`))FROMUNNEST([1])'
Question 8mediummultiple choice
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A financial services company wants to detect fraudulent transactions in real-time. They have a trained XGBoost model that runs on a single Compute Engine instance. The current solution processes about 100 transactions per second, but they need to scale to 10,000 transactions per second. Which approach should they take?

Question 9hardmulti select
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A manufacturing company wants to predict equipment failure using sensor data. The data is highly imbalanced (only 1% failures). They are using a gradient boosted tree model with class weights. The model achieves 0.99 recall but 0.2 precision on the test set. Which two actions should they take to improve precision without significantly hurting recall? (Choose TWO)

Question 10hardmultiple choice
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A healthcare startup is building a diagnostic tool that uses a deep learning model to classify medical images. The model is trained on TensorFlow and deployed on Vertex AI Prediction. The startup has strict latency requirements: predictions must return within 200 ms for 95% of requests. Current performance shows p95 latency of 350 ms. The team has already tried using a smaller model, but accuracy dropped below acceptable levels. The traffic pattern is spiky: low load during nights but bursts of 1000 requests per second during business hours. Currently, they use a single n1-highmem-8 VM with a GPU attached. They have a budget for additional resources but need to optimize cost. The model is about 500 MB and requires GPU for inference. Which course of action should they take to meet the latency requirement while managing costs?

Question 11easymultiple choice
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A retail company wants to forecast daily sales for inventory planning. They have 3 years of historical sales data with clear weekly and yearly seasonality. Which approach should they use?

Question 12mediummultiple choice
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A company is training a large neural network on Vertex AI and training jobs keep failing with 'Out of memory' errors. The VM uses a standard n1-standard-4 machine with 15 GB RAM. Which action should they take first?

Question 13hardmultiple choice
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A financial institution needs to deploy a fraud detection model with strict latency <100ms per prediction and high throughput (1000 predictions/sec). The model is a deep neural network. Which architecture on Google Cloud meets these requirements?

Question 14easymultiple choice
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A startup wants to add sentiment analysis to their customer feedback app without any labeled data or custom model training. Which Google Cloud service should they use?

Question 15mediummultiple choice
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A data science team is using AI Platform for training. They want to track hyperparameters and metrics across multiple experiments. What should they use?

Question 16hardmultiple choice
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A company has a large dataset of 1 million unlabeled images for object detection. They want to use AutoML Vision but need to minimize labeling effort. Which strategy should they use?

Question 17easymultiple choice
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A data scientist wants to perform feature engineering on a large dataset stored in BigQuery before training a model. Which feature engineering tool is most appropriate?

Question 18mediummultiple choice
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A model deployed on Vertex AI Prediction is returning high latency for real-time requests. The model is a small TensorFlow model. Which troubleshooting step should the team take first?

Question 19hardmultiple choice
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A company wants to use ML to predict customer churn. They have user activity logs in Cloud Storage, account data in BigQuery, and want an automated pipeline. Which pipeline architecture on Google Cloud should they use?

Question 20mediummulti select
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A company is evaluating Google Cloud ML solutions. Which TWO services are appropriate for building custom machine learning models (not using pre-built APIs)? (Choose TWO.)

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Frequently asked questions

What does the PMLE exam test about Solving business challenges with ML?
Solving business challenges with ML questions test whether you can apply the concept in context, not just recognise a definition.
How should I use these practice questions?
Select your answer before revealing the explanation. Then read why each option is right or wrong — this active recall approach builds retention far faster than re-reading notes.
Can I practise just Solving business challenges with ML questions in a focused session?
Yes — the session launcher on this page draws every question from the Solving business challenges with ML domain. Use a 10-question session first to gauge your baseline, then move to 20 or 30 once the weak spots are clear.
Where can I practise other PMLE topics?
Use the topic links above to move to related areas, or go back to the PMLE question bank to see all topics.
Are these real exam questions or dumps?
These are original practice questions written to test the same concepts the PMLE exam covers. They are not copied from any real exam or dump site.