Professional Machine Learning Engineer Free Practice Questions — Page 2
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: Use the original 16 kHz audio because Speech-to-Text performs best without unnecessary downsampling, and 16 kHz is a recommended sample rate for speech recognition. Since the recordings are longer than one minute and transcription is for later processing rather than immediate interaction, asynchronous recognition is the appropriate API mode.
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: Customer churn prediction is a binary classification problem, making logistic regression an appropriate modeling technique. Logistic regression in scikit-learn is inherently interpretable because each feature has a coefficient indicating its effect on the log-odds of churn, allowing stakeholders to understand feature impact directly. Deep neural networks and LSTMs are less interpretable even with post hoc methods like SHAP or attention, and linear regression is not the appropriate model for a binary target.
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: Retraining should be triggered based on evidence that the production data no longer matches the training data (data drift/skew), rather than on a fixed schedule or infrastructure metrics. Detecting a significant distribution shift is a standard, cost-effective trigger in Vertex AI because it retrains only when needed. Increased latency is unrelated to model quality, weekly retraining can waste resources, and evaluating accuracy on newly collected training data is not the preferred production trigger compared with monitoring for drift.
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 with autoscaling, high availability, and minimal infrastructure management. This best fits a small team seeking a scalable, reliable, and cost-effective solution that requires no additional cloud infrastructure expertise. Compute Engine, Cloud Run, and GKE all require more deployment and infrastructure management.
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 endpoint experiences fluctuating traffic up to four times its current capacity. The most cost-effective solution is to use Vertex AI's horizontal autoscaling by increasing the maximum number of replicas while keeping the same machine type. This allows the service to scale out during traffic spikes and scale back down when demand decreases. TPUs are not appropriate for a scikit-learn model, switching to a much larger machine is unnecessary and more expensive for variable workloads, and relying on monitoring plus manual scaling does not proactively address fluctuating demand.
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 supports traffic splitting across deployed model versions/endpoints for A/B testing, making it the simplest managed approach. You can route a percentage of prediction traffic to each model version and use Vertex AI's built-in monitoring capabilities to evaluate performance. The other options introduce additional infrastructure (GKE, Cloud Run) or extra dashboarding work (Looker Studio), making them less simple.
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 task is to forecast future weekly sales from historical seasonal time series data. Time series forecasting models are specifically designed for this use case and are the most appropriate and efficient choice. Logistic regression is for classification rather than forecasting numeric sales values, Looker provides visualization rather than prediction, and using an LLM with RAG is unnecessary and not appropriate for structured 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 degradation in production AUC after deployment commonly indicates data drift or training-serving skew. Vertex AI Model Monitoring can detect differences between feature distributions seen during training and those observed in online prediction requests, helping identify whether the model is receiving data that differs from what it was trained on. CPU/memory utilization and latency affect service performance, not model accuracy, and Explainable AI helps interpret predictions rather than diagnose production performance degradation due to distribution changes.
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 track experiment runs, hyperparameters, metrics, and compare model iterations. Vertex AI TensorBoard provides rich visualization of training progress, scalars, graphs, embeddings, and model internals, making it the strongest combination for experiment tracking plus deep learning visualization and analysis.
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: Use a custom container with the training application and all custom dependencies preinstalled to minimize startup time. Store training data separately in Cloud Storage, which is the recommended practice rather than baking data into the container. Options B and C incur runtime dependency installation overhead, and A incorrectly packages data into the container.
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