PMLE practice questions 151–160

1. Question 151 · 364 in bank

   You are using Vertex AI and TensorFlow to develop a custom image classification model. You need the model’s decisions and the rationale to be understandable to your company’s stakeholders. You also want to explore the results to identify any issues or potential biases. What should you do?

   • A1. Use TensorFlow to generate and visualize features and statistics. 2. Analyze the results together with the standard model evaluation metrics.
   • B1. Use TensorFlow Profiler to visualize the model execution. 2. Analyze the relationship between incorrect predictions and execution bottlenecks.
   • C1. Use Vertex Explainable AI to generate example-based explanations. 2. Visualize the results of sample inputs from the entire dataset together with the standard model evaluation metrics.
   • D1. Use Vertex Explainable AI to generate feature attributions. Aggregate feature attributions over the entire dataset. 2. Analyze the aggregation result together with the standard model evaluation metrics.

   Show answer

   Answer: D

   Explanation

   My Answer: D This approach leverages Vertex Explainable AI to provide feature attributions, which helps in understanding the rationale behind the model's decisions. By aggregating these feature attributions over the entire dataset, you can gain insights into potential biases or areas of concern. Analyzing these results alongside standard model evaluation metrics allows for a comprehensive understanding of the model's performance and its interpretability. Option C is better to understand specific cases, but does not show overall contributions.

   Source: Adapted from community discussion — guilhermebutzke

   References

   • Google Cloud documentation

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2. Question 152 · 364 in bank

   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?

   • ACreate a linear regression model in BigQuery ML, and register the model in Vertex AI Model Registry. Evaluate the model performance in Vertex AI .
   • BCreate a logistic regression model in BigQuery ML and register the model in Vertex AI Model Registry. Evaluate the model performance in Vertex AI .
   • CCreate a linear regression model in BigQuery ML. Use the ML.EVALUATE function to evaluate the model performance.
   • DCreate a logistic regression model in BigQuery ML. Use the ML.CONFUSION_MATRIX function to evaluate the model performance.

   Show answer

   Answer: B

   Explanation

   B. Linear regression because customer churn is a number of customers (not just 1/0). The key here imo is "thoroughly evaluate performance", which Vertex AI seems to be better suited for than BQ (including the possibility of tracking experiment lineage, inspecting parameter selection of each run, etc)

   Source: Adapted from community discussion — b1a8fae

   References

   • Google Cloud documentation

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3. Question 153 · 364 in bank

   You are developing a model to identify traffic signs in images extracted from videos taken from the dashboard of a vehicle. You have a dataset of 100,000 images that were cropped to show one out of ten different traffic signs. The images have been labeled accordingly for model training, and are stored in a Cloud Storage bucket. You need to be able to tune the model during each training run. How should you train the model?

   • ATrain a model for object detection by using Vertex AI AutoML.
   • BTrain a model for image classification by using Vertex AI AutoML.
   • CDevelop the model training code for object detection, and train a model by using Vertex AI custom training.
   • DDevelop the model training code for image classification, and train a model by using Vertex AI custom training.

   Show answer

   Answer: D

   Explanation

   Not A or B since automl doesnt provide you without flexibility to tune. Not C because object detection is not required since the images are cropped to a single traffic light

   Source: Adapted from community discussion — pikachu007

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4. Question 154 · 364 in bank

   You have deployed a scikit-team model to a Vertex AI endpoint using a custom model server. You enabled autoscaling: however, the deployed model fails to scale beyond one replica, which led to dropped requests. You notice that CPU utilization remains low even during periods of high load. What should you do?

   • AAttach a GPU to the prediction nodes
   • BIncrease the number of workers in your model server
   • CSchedule scaling of the nodes to match expected demand
   • DIncrease the minReplicaCount in your DeployedModel configuration

   Show answer

   Answer: B

   Explanation

   Low CPU Utilization: Despite high load, low CPU utilization indicates underutilization of available resources, suggesting a bottleneck within the model server itself, not overall compute capacity. Worker Concurrency: Increasing the number of workers within the model server allows it to handle more concurrent requests, effectively utilizing available CPU resources and addressing the bottleneck.

   Source: Adapted from community discussion — pikachu007

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5. Question 155 · 364 in bank

   You work for a pet food company that manages an online forum. Customers upload photos of their pets on the forum to share with others. About 20 photos are uploaded daily. You want to automatically and in near real time detect whether each uploaded photo has an animal. You want to prioritize time and minimize cost of your application development and deployment. What should you do?

   • ASend user-submitted images to the Cloud Vision API. Use object localization to identify all objects in the image and compare the results against a list of animals.
   • BDownload an object detection model from TensorFlow Hub. Deploy the model to a Vertex AI endpoint. Send new user-submitted images to the model endpoint to classify whether each photo has an animal.
   • CManually label previously submitted images with bounding boxes around any animals. Build an AutoML object detection model by using Vertex AI. Deploy the model to a Vertex AI endpoint Send new user-submitted images to your model endpoint to detect whether each photo has an animal.
   • DManually label previously submitted images as having animals or not. Create an image dataset on Vertex AI. Train a classification model by using Vertex AutoML to distinguish the two classes. Deploy the model to a Vertex AI endpoint. Send new user-submitted images to your model endpoint to classify whether each photo has an animal.

   Show answer

   Answer: A

   Explanation

   A. B would also work and I wonder if cost would be lower, but I think going with the google hosted service is most times the most likely choice to be correct.

   Source: Adapted from community discussion — b1a8fae

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6. Question 156 · 364 in bank

   You are an ML engineer at a global shoe store. You manage the ML models for the company's website. You are asked to build a model that will recommend new products to the user based on their purchase behavior and similarity with other users. What should you do?

   • ABuild a classification model
   • BBuild a knowledge-based filtering model
   • CBuild a collaborative-based filtering model
   • DBuild a regression model using the features as predictors

   Show answer

   Answer: C

   Explanation

   Chat gPT: Collaborative filtering models are specifically designed for recommendation systems. They work by analyzing the interactions and behaviors of users and items, then making predictions about what users will like based on similarities with other users. In this case, since you're looking at purchase behavior and user similarities, a collaborative filtering approach is well-suited to identify and recommend products that users with similar behaviors have liked or purchased. Classification models (Option A) and regression models (Option D) are generally used for different types of predictive modeling tasks, not specifically for recommendations. A knowledge-based filtering model (Option B), while useful in recommendation systems, relies more on explicit knowledge about users and items, rather than on user interaction patterns and similarities, which seems to be the focus in this scenario.

   Source: Adapted from community discussion — Sum_Sum

   References

   • Google Cloud documentation
   • Google developer documentation
   • Google Cloud documentation

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7. Question 157 · 364 in bank

   You work at a mobile gaming startup that creates online multiplayer games. Recently, your company observed an increase in players cheating in the games, leading to a loss of revenue and a poor user experience You built a binary classification model to determine whether a player cheated after a completed game session, and then send a message to other downstream systems to ban the player that cheated. Your model has performed well during testing, and you now need to deploy the model to production. You want your serving solution to provide immediate classifications after a completed game session to avoid further loss of revenue. What should you do?

   • AImport the model into Vertex AI Model Registry. Use the Vertex Batch Prediction service to run batch inference jobs.
   • BSave the model files in a Cloud Storage bucket. Create a Cloud Function to read the model files and make online inference requests on the Cloud Function.
   • CSave the model files in a VM. Load the model files each time there is a prediction request, and run an inference job on the VM
   • DImport the model into Vertex AI Model Registry. Create a Vertex AI endpoint that hosts the model, and make online inference requests.

   Show answer

   Answer: D

   Explanation

   Low Latency: Vertex AI Endpoints are specifically designed for low-latency online inference. They offer automatic scaling and efficient resource allocation, ensuring quick responses to game session completion signals. Real-time Decisions: This deployment method allows your game backend to send data from finished game sessions to the Vertex AI endpoint in near real-time. The endpoint can then make classifications (cheater or not cheater) promptly. Managed Service: Vertex AI handles the infrastructure management and scaling of your model, freeing you from managing servers or virtual machines (VMs).

   Source: Adapted from community discussion — fitri001

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8. Question 158 · 364 in bank

   You have created a Vertex AI pipeline that automates custom model training. You want to add a pipeline component that enables your team to most easily collaborate when running different executions and comparing metrics both visually and programmatically. What should you do?

   • AAdd a component to the Vertex AI pipeline that logs metrics to a BigQuery table. Query the table to compare different executions of the pipeline. Connect BigQuery to Looker Studio to visualize metrics.
   • BAdd a component to the Vertex AI pipeline that logs metrics to a BigQuery table. Load the table into a pandas DataFrame to compare different executions of the pipeline. Use Matplotlib to visualize metrics.
   • CAdd a component to the Vertex AI pipeline that logs metrics to Vertex ML Metadata. Use Vertex AI Experiments to compare different executions of the pipeline. Use Vertex AI TensorBoard to visualize metrics.
   • DAdd a component to the Vertex AI pipeline that logs metrics to Vertex ML Metadata. Load the Vertex ML Metadata into a pandas DataFrame to compare different executions of the pipeline. Use Matplotlib to visualize metrics.

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   Answer: C

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9. Question 159 · 364 in bank

   Your team is training a large number of ML models that use different algorithms, parameters, and datasets. Some models are trained in Vertex AI Pipelines, and some are trained on Vertex AI Workbench notebook instances. Your team wants to compare the performance of the models across both services. You want to minimize the effort required to store the parameters and metrics. What should you do?

   • AImplement an additional step for all the models running in pipelines and notebooks to export parameters and metrics to BigQuery.
   • BCreate a Vertex AI experiment. Submit all the pipelines as experiment runs. For models trained on notebooks log parameters and metrics by using the Vertex AI SDK.
   • CImplement all models in Vertex AI Pipelines Create a Vertex AI experiment, and associate all pipeline runs with that experiment.
   • DStore all model parameters and metrics as model metadata by using the Vertex AI Metadata API.

   Show answer

   Answer: B

   Explanation

   My Answer: B A: Not Correct: Not the best approach compared with Vertex AI experiment that does the same B: CORRECT: By submitting all pipelines as experiment runs, you can centralize the storage of parameters and metrics for models trained in Vertex AI Pipelines. This approach minimizes effort by providing a unified platform for storing and comparing model performance across different services. C: Not Correct: not feasible or ideal for models trained on Vertex AI Workbench notebook instances. D: Not Correct: If only basic parameter and metric storage is needed, and your team prioritizes simplicity over in-depth comparison, option D could be an alternative. For more complex scenarios requiring comprehensive analysis and comparison across diverse models, option B with Vertex AI Experiments

   Source: Adapted from community discussion — guilhermebutzke

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10. Question 160 · 364 in bank

    You work on a team that builds state-of-the-art deep learning models by using the TensorFlow framework. Your team runs multiple ML experiments each week, which makes it difficult to track the experiment runs. You want a simple approach to effectively track, visualize, and debug ML experiment runs on Google Cloud while minimizing any overhead code. How should you proceed?

    • ASet up Vertex AI Experiments to track metrics and parameters. Configure Vertex AI TensorBoard for visualization.
    • BSet up a Cloud Function to write and save metrics files to a Cloud Storage bucket. Configure a Google Cloud VM to host TensorBoard locally for visualization.
    • CSet up a Vertex AI Workbench notebook instance. Use the instance to save metrics data in a Cloud Storage bucket and to host TensorBoard locally for visualization.
    • DSet up a Cloud Function to write and save metrics files to a BigQuery table. Configure a Google Cloud VM to host TensorBoard locally for visualization.

    Show answer

    Answer: A

    Explanation

    Built-in Tracking: Vertex AI Experiments is specifically designed for tracking ML experiments on Google Cloud. It simplifies logging metrics and parameters, eliminating the need for custom code. TensorBoard Integration: Vertex AI integrates with TensorBoard, allowing visualization of training logs and metrics directly within the Experiments interface. This provides a centralized location for both tracking and visualization. Minimized Overhead: This approach leverages existing services, minimizing the need for additional code or infrastructure setup compared to options with Cloud Functions or VMs.

    Source: Adapted from community discussion — fitri001

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PMLE practice, question 151. 364 questions available.