Professional Machine Learning Engineer Free Practice Questions — Page 2
Question 12
Your company's business stakeholders want to understand the factors driving customer churn to inform their business strategy. You need to build a customer churn prediction model that prioritizes simple interpretability of your model's results. You need to choose the ML framework and modeling technique that will explain which features led to the prediction. What should you do?
A. Build a TensorFlow deep neural network (DNN) model, and use SHAP values for feature importance analysis.
B. Build a PyTorch long short-term memory (LSTM) network, and use attention mechanisms for interpretability.
C. Build a logistic regression model in scikit-learn, and interpret the model's output coefficients to understand feature impact.
D. Build a linear regression model in scikit-learn, and interpret the model's standardized coefficients to understand feature impact.
Show Answer
Correct Answer: C
Explanation: The goal is to predict customer churn (a binary classification problem) while prioritizing simple, transparent interpretability for business stakeholders. Logistic regression is a naturally interpretable model where each feature’s coefficient directly indicates the direction and magnitude of its impact on churn probability. In contrast, deep learning models (DNNs, LSTMs) require post-hoc interpretability methods and are less straightforward, and linear regression is not appropriate for binary outcomes. Therefore, a scikit-learn logistic regression model best satisfies both the problem type and interpretability requirement.
Question 13
You are creating a retraining policy for a customer churn prediction model deployed in Vertex AI. New training data is added weekly. You want to implement a model retraining process that minimizes cost and effort. What should you do?
A. Retrain the model when a significant shift in the distribution of customer attributes is detected in the production data compared to the training data.
B. Retrain the model when the model's latency increases by 10% due to increased traffic.
C. Retrain the model when the model accuracy drops by 10% on the new training dataset.
D. Retrain the model every week when new training data is available.
Show Answer
Correct Answer: A
Explanation: To minimize cost and effort, retraining should be triggered only when it is likely to improve model performance. Monitoring for data distribution shifts (data drift) between training and production data ensures the model is retrained only when underlying patterns change. Weekly retraining is unnecessary, latency is unrelated to model quality, and accuracy drops alone may lag or be noisy compared to proactive drift detection.
Question 14
You are a lead ML architect at a small company that is migrating from on-premises to Google Cloud. Your company has limited resources and expertise in cloud infrastructure. You want to serve your models from Google Cloud as soon as possible. You want to use a scalable, reliable, and cost-effective solution that requires no additional resources. What should you do?
A. Configure Compute Engine VMs to host your models.
B. Create a Cloud Run function to deploy your models as serverless functions.
C. Create a managed cluster on Google Kubernetes Engine (GKE), and deploy your models as containers.
D. Deploy your models on Vertex AI endpoints.
Show Answer
Correct Answer: D
Explanation: Vertex AI endpoints provide a fully managed model serving platform on Google Cloud. They handle infrastructure provisioning, scaling, reliability, and monitoring automatically, requiring minimal cloud infrastructure expertise and no additional resources to manage. This makes them the fastest, most scalable, and cost-effective option for a small team migrating from on‑premises.
Question 15
You work at an organization that manages a popular payment app. You built a fraudulent transaction detection model by using scikit-learn and deployed it to a Vertex AI endpoint. The endpoint is currently using 1 e2-standard-2 machine with 2 vCPUs and 8 GB of memory. You discover that traffic on the gateway fluctuates to four times more than the endpoint's capacity. You need to address this issue by using the most cost-effective approach. What should you do?
A. Re-deploy the model with a TPU accelerator.
B. Change the machine type to e2-highcpu-32 with 32 vCPUs and 32 GB of memory.
C. Set up a monitoring job and an alert for CPU usage. If you receive an alert, scale the vCPUs as needed.
D. Increase the number of maximum replicas to 6 nodes, each with 1 e2-standard-2 machine.
Show Answer
Correct Answer: D
Explanation: The traffic fluctuates to about 4× the current endpoint capacity, so the most cost‑effective solution is horizontal autoscaling. Increasing the maximum number of replicas allows Vertex AI to scale out during traffic spikes and scale back down when demand drops, avoiding the constant cost of a much larger machine. TPUs are not supported for scikit‑learn, vertical scaling to a large machine is overkill, and monitoring alone does not solve the capacity issue.
Question 16
You have created multiple versions of an ML model and have imported them to Vertex AI Model Registry. You want to perform A/B testing to identify the best performing model using the simplest approach. What should you do?
A. Split incoming traffic to distribute prediction requests among the versions. Monitor the performance of each version using Vertex AI's built-in monitoring tools.
B. Split incoming traffic among Google Kubernetes Engine (GKE) clusters, and use Traffic Director to distribute prediction requests to different versions. Monitor the performance of each version using Cloud Monitoring.
C. Split incoming traffic to distribute prediction requests among the versions. Monitor the performance of each version using Looker Studio dashboards that compare logged data for each version.
D. Split incoming traffic among separate Cloud Run instances of deployed models. Monitor the performance of each version using Cloud Monitoring.
Show Answer
Correct Answer: A
Explanation: Vertex AI Model Registry and endpoints natively support traffic splitting across model versions, enabling straightforward A/B testing without additional infrastructure. You can route a percentage of prediction traffic to each version and use Vertex AI’s built-in monitoring and evaluation tools to compare performance. The other options introduce extra components (GKE, Traffic Director, Cloud Run, or custom dashboards), which makes them more complex and not the simplest approach.
Question 17
You are an AI engineer with an apparel retail company. The sales team has observed seasonal sales patterns over the past 5-6 years. The sales team analyzes and visualizes the weekly sales data stored in CSV files. You have been asked to estimate weekly sales for future seasons to optimize inventory and personnel workloads. You want to use the most efficient approach. What should you do?
A. Upload the files into Cloud Storage. Use Python to preprocess and load the tabular data into BigQuery. Use time series forecasting models to predict weekly sales.
B. Upload the files into Cloud Storage. Use Python to preprocess and load the tabular data into BigQuery. Train a logistic regression model by using BigQuery ML to predict each product's weekly sales as one of three categories: high, medium, or low.
C. Load the files into BigQuery. Preprocess data by using BigQuery SQL. Connect BigQuery to Looker. Create a Looker dashboard that shows weekly sales trends in real time and can slice and dice the data based on relevant filters.
D. Create a custom conversational application using Vertex AI Agent Builder. Include code that enables file upload functionality, and upload the files. Use few-shot prompting and retrieval-augmented generation (RAG) to predict future sales trends by using the Gemini large language model (LLM).
Show Answer
Correct Answer: A
Explanation: The goal is to estimate future weekly sales based on historical seasonal patterns. This is a classic time series forecasting problem with continuous numeric targets. Option A uses an efficient and appropriate pipeline: storing data in Cloud Storage/BigQuery and applying time series forecasting models designed to capture seasonality and trends. Option B reframes the problem as classification, which loses information and is less suitable. Option C focuses only on visualization without prediction. Option D overcomplicates the task and uses LLMs, which are not well-suited for precise numerical time series forecasting.
Question 18
You are building an ML model to predict customer churn for a subscription service. You have trained your model on Vertex AI using historical data, and deployed it to a Vertex AI endpoint for real-time predictions. After a few weeks, you notice that the model's performance, measured by AUC (area under the ROC curve), has dropped significantly in production compared to its performance during training. How should you troubleshoot this problem?
A. Monitor the training/serving skew of feature values for requests sent to the endpoint.
B. Monitor the resource utilization of the endpoint, such as CPU and memory usage, to identify potential bottlenecks in performance.
C. Enable Vertex Explainable AI feature attribution to analyze model predictions and understand the impact of each feature on the model's predictions.
D. Monitor the latency of the endpoint to determine whether predictions are being served within the expected time frame.
Show Answer
Correct Answer: A
Explanation: A significant drop in AUC in production typically indicates data drift or training/serving skew, where the feature distributions seen by the deployed model differ from those used during training. Monitoring training/serving skew on the Vertex AI endpoint directly helps detect whether incoming real-time data has changed in a way that degrades model performance. The other options focus on system performance or interpretability, which do not explain a degradation in predictive quality.
Question 19
You are an ML researcher and are evaluating multiple deep learning-based model architectures and hyperparameter configurations. You need to implement a robust solution to track the progress of each model iteration, visualize key metrics, gain insights into model internals, and optimize training performance.
You want your solution to have the most efficient and powerful approach to compare the models and have the strongest visualization abilities. How should you bull this solution?
A. Use Vertex AI TensorBoard for in-depth visualization and analysis, and use BigQuery for experiment tracking and analysis.
B. Use Vertex AI TensorBoard for visualizing training progress and model behavior, and use Vertex AI Feature Store to stove and manage experiment data for analysis and reproducibility.
C. Use Vertex AI Experiments for tracking iterations and comparison, and use Vertex AI TensorBoard for visualization and analysis of the training metrics and model architecture.
D. Use Vertex AI Experiments for tracking iterations and comparison, and use BigQuery and Looker Studio for visualization and analysis of the training metrics and model architecture.
Show Answer
Correct Answer: C
Explanation: Vertex AI Experiments is purpose-built to log, track, and compare model runs, hyperparameters, and metrics across iterations, making it ideal for systematic experimentation. Vertex AI TensorBoard provides the most powerful and efficient visualization capabilities for deep learning, including training curves, model graphs, embeddings, and performance diagnostics. Together, they offer seamless integration for robust experiment tracking, deep insights into model internals, and strong visualization, outperforming alternatives that rely on general analytics tools or services not designed for experiment management.
Question 20
You need to train an XGBoost model on a small dataset. Your training code requires custom dependencies. You need to set up a Vertex AI custom training job. You want to minimize the startup time of the training job while following Google-recommended practices. What should you do?
A. Create a custom container that includes the data and the custom dependencies. In your training application, load the data into a pandas DataFrame and train the model.
B. Store the data in a Cloud Storage bucket, and use the XGBoost prebuilt custom container to run your training application. Create a Python source distribution that installs the custom dependencies at runtime. In your training application, read the data from Cloud Storage and train the model.
C. Use the XGBoost prebuilt custom container. Create a Python source distribution that includes the data and installs the custom dependencies at runtime. In your training application, load the data into a pandas DataFrame and train the model.
D. Store the data in a Cloud Storage bucket, and create a custom container with your training application and its custom dependencies. In your training application, read the data from Cloud Storage and train the model.
Show Answer
Correct Answer: D
Explanation: To minimize startup time on Vertex AI, all custom dependencies should be preinstalled in the container image rather than installed at runtime. Google-recommended practices also discourage baking data into the container; data should be read from Cloud Storage. Creating a custom container with the training code and dependencies prebuilt, and loading the small dataset from Cloud Storage at runtime, avoids dependency installation delays and follows best practices.
Question 21
You work for a large retailer, and you need to build a model to predict customer chum. The company has a dataset of historical customer data, including customer demographics purchase history, and website activity. You need to create the model in BigQuery ML and thoroughly evaluate its performance. What should you do?
A. Create a linear regression model in BigQuery ML, and register the model in Vertex AI Model Registry. Use Vertex AI to evaluate the model performance.
B. Create a logistic regression model in BigQuery ML, and register the model in Vertex AI Model Registry. Use ML.ARIMA_EVALUATE function to evaluate the model performance.
C. Create a linear regression model in BigQuery ML. Use the ML.EVALUATE function to evaluate the model performance.
D. Create a logistic regression model in BigQuery ML. Use the ML.CONFUSION_MATRIX function to evaluate the model performance.
Show Answer
Correct Answer: D
Explanation: Customer churn is a binary classification problem, so a logistic regression model is appropriate in BigQuery ML. For classification models, evaluation should use classification metrics; ML.CONFUSION_MATRIX provides a standard way to assess prediction performance (e.g., true/false positives and negatives), making option D the correct choice.
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