PMLE practice questions 291–300

1. Question 291 · 364 in bank

   You work as an ML engineer at a social media company, and you are developing a visual filter for users’ profile photos. This requires you to train an ML model to detect bounding boxes around human faces. You want to use this filter in your company’s iOS-based mobile phone application. You want to minimize code development and want the model to be optimized for inference on mobile phones. What should you do?

   • ATrain a model using AutoML Vision and use the “export for Core ML” option.
   • BTrain a model using AutoML Vision and use the “export for Coral” option.
   • CTrain a model using AutoML Vision and use the “export for TensorFlow.js” option.
   • DTrain a custom TensorFlow model and convert it to TensorFlow Lite (TFLite).

   Show answer

   Answer: A

   Explanation

   Updated Vertex AI link:https://cloud.google.com/vertex-ai/docs/export/export-edge-model Trained AutoML Edge image classification models can be exported in the following formats: TF Lite - to run your model on edge or mobile devices. Edge TPU TF Lite - to run your model on Edge TPU devices. Container - to run on a Docker container. Core ML - to run your model on iOS and macOS devices. Tensorflow.js - to run your model in the browser and in Node.js.

   Source: Adapted from community discussion — maukaba

   References

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

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2. Question 292 · 364 in bank

   You have been asked to build a model using a dataset that is stored in a medium-sized (~10 GB) BigQuery table. You need to quickly determine whether this data is suitable for model development. You want to create a one-time report that includes both informative visualizations of data distributions and more sophisticated statistical analyses to share with other ML engineers on your team. You require maximum flexibility to create your report. What should you do?

   • AUse Vertex AI Workbench user-managed notebooks to generate the report.
   • BUse the Google Data Studio to create the report.
   • CUse the output from TensorFlow Data Validation on Dataflow to generate the report.
   • DUse Dataprep to create the report.

   Show answer

   Answer: A

   Explanation

   I think it's A.One time report containing real datasets STATISTICAL measurements to tell if the data is suitable for model development. Target audience is also other ML engineers. Getting a whole report of exactly this with TFDV/Facets is like two lines of code: https://www.tensorflow.org/tfx/data_validation/get_started A similar data studio report for this would take lots of time and work, and there would be no benefit from reuseability since task was a one-time job.

   Source: Adapted from community discussion — JamesDoe

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3. Question 293 · 364 in bank

   You work on an operations team at an international company that manages a large fleet of on-premises servers located in few data centers around the world. Your team collects monitoring data from the servers, including CPU/memory consumption. When an incident occurs on a server, your team is responsible for fixing it. Incident data has not been properly labeled yet. Your management team wants you to build a predictive maintenance solution that uses monitoring data from the VMs to detect potential failures and then alerts the service desk team. What should you do first?

   • ATrain a time-series model to predict the machines’ performance values. Configure an alert if a machine’s actual performance values significantly differ from the predicted performance values.
   • BImplement a simple heuristic (e.g., based on z-score) to label the machines’ historical performance data. Train a model to predict anomalies based on this labeled dataset.
   • CDevelop a simple heuristic (e.g., based on z-score) to label the machines’ historical performance data. Test this heuristic in a production environment.
   • DHire a team of qualified analysts to review and label the machines’ historical performance data. Train a model based on this manually labeled dataset.

   Show answer

   Answer: C

   Explanation

   I would go for C, it is important to have a clear understanding of what constitutes a potential failure and how to detect it. A heuristic based on z-scores, for example, can be used to flag instances where the performance values of a machine significantly differ from its historical baseline.

   Source: Adapted from community discussion — mil_spyro

   References

   • Google developer documentation

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4. Question 294 · 364 in bank

   You are developing an ML model that uses sliced frames from video feed and creates bounding boxes around specific objects. You want to automate the following steps in your training pipeline: ingestion and preprocessing of data in Cloud Storage, followed by training and hyperparameter tuning of the object model using Vertex AI jobs, and finally deploying the model to an endpoint. You want to orchestrate the entire pipeline with minimal cluster management. What approach should you use?

   • AUse Kubeflow Pipelines on Google Kubernetes Engine.
   • BUse Vertex AI Pipelines with TensorFlow Extended (TFX) SDK.
   • CUse Vertex AI Pipelines with Kubeflow Pipelines SDK.
   • DUse Cloud Composer for the orchestration.

   Show answer

   Answer: C

   Explanation

   From: https://cloud.google.com/vertex-ai/docs/pipelines/build-pipeline#sdk "1. If you use TensorFlow in an ML workflow that processes terabytes of structured data or text data, we recommend that you build your pipeline using TFX. To learn more about building a TFX pipeline, follow the TFX getting started tutorials. To learn more about using Vertex AI Pipelines to run a TFX pipeline, follow the TFX on Google Cloud tutorials. 2. For other use cases, we recommend that you build your pipeline using the Kubeflow Pipelines SDK. By building a pipeline with the Kubeflow Pipelines SDK, you can implement your workflow by building custom components or reusing prebuilt components, such as the Google Cloud Pipeline Components. Google Cloud Pipeline Components make it easier to use Vertex AI services like AutoML in your pipeline." So I guess since it is image processing, it should be Kubeflow - answer C (TFX is for structured or text data).

   Source: Adapted from community discussion — qaz09

   References

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

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5. Question 295 · 364 in bank

   You are an ML engineer at a travel company. You have been researching customers’ travel behavior for many years, and you have deployed models that predict customers’ vacation patterns. You have observed that customers’ vacation destinations vary based on seasonality and holidays; however, these seasonal variations are similar across years. You want to quickly and easily store and compare the model versions and performance statistics across years. What should you do?

   • AStore the performance statistics in Cloud SQL. Query that database to compare the performance statistics across the model versions.
   • BCreate versions of your models for each season per year in Vertex AI. Compare the performance statistics across the models in the Evaluate tab of the Vertex AI UI.
   • CStore the performance statistics of each pipeline run in Kubeflow under an experiment for each season per year. Compare the results across the experiments in the Kubeflow UI.
   • DStore the performance statistics of each version of your models using seasons and years as events in Vertex ML Metadata. Compare the results across the slices.

   Show answer

   Answer: D

   Explanation

   I absolutely do not master this topicm but I would say correct answer is D. It does not sound right to systematically create versions of a model beased on seasonality, if the model has not changed. "Events" in metadata sound right.

   Source: Adapted from community discussion — andresvelasco

   References

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

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6. Question 296 · 364 in bank

   Your company recently migrated several of is ML models to Google Cloud. You have started developing models in Vertex AI. You need to implement a system that tracks model artifacts and model lineage. You want to create a simple, effective solution that can also be reused for future models. What should you do?

   • AUse a combination of Vertex AI Pipelines and the Vertex AI SDK to integrate metadata tracking into the ML workflow.
   • BUse Vertex AI Pipelines for model artifacts and MLflow for model lineage.
   • CUse Vertex AI Experiments for model artifacts and use Vertex ML Metadata for model lineage.
   • DImplement a scheduled metadata tracking solution using Cloud Composer and Cloud Run functions.

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   Answer: A

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7. Question 297 · 364 in bank

   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?

   • ATrain 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.
   • BDevelop 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.
   • CCreate 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.
   • DSend 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.

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   Answer: C

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8. Question 298 · 364 in bank

   You are an ML engineer at a bank. The bank's leadership team wants to reduce the number of loan defaults. The bank has labeled historic data about loan defaults stored in BigQuery. You have been asked to use AI to support the loan application process. For compliance reasons, you need to provide explanations for loan rejections. What should you do?

   • AImport the historic loan default data into AutoML. Train and deploy a linear regression model to predict default probability. Report the probability of default for each loan application.
   • BCreate a custom application that uses the Gemini large language model (LLM). Provide the historic data as context to the model, and prompt the model to predict customer defaults. Report the prediction and explanation provided by the LLM for each loan application.
   • CTrain and deploy a BigQuery ML classification model trained on historic loan default data. Enable feature-based explanations for each prediction. Report the prediction, probability of default, and feature attributions for each loan application.
   • DLoad the historic loan default data into a Vertex AI Workbench instance. Train a deep learning classification model using TensorFlow to predict loan default. Run inference for each loan application, and report the predictions.

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   Answer: C

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9. Question 299 · 364 in bank

   You are developing a natural language processing model that analyzes customer feedback to identify positive, negative, and neutral experiences. During the testing phase, you notice that the model demonstrates a significant bias against certain demographic groups, leading to skewed analysis results. You want to address this issue following Google's responsible AI practices. What should you do?

   • AUse Vertex AI's model evaluation lo assess bias in the model's predictions, and use post-processing to adjust outputs for identified demographic discrepancies.
   • BImplement a more complex model architecture that can capture nuanced patterns in language to reduce bias.
   • CAudit the training dataset to identify underrepresented groups and augment the dataset with additional samples before retraining the model.
   • DUse Vertex Explainable AI to generate explanations and systematically adjust the predictions to address identified biases.

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   Answer: C

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10. Question 300 · 364 in bank

    You recently deployed an image classification model on Google Cloud. You used Cloud Build to build a CI/CD pipeline for the model. You need to ensure that the model stays up-to-date with data and code changes by using an efficient retraining process. What should you do?

    • AUse Cloud Run functions to monitor data drift in real time and trigger a Vertex AI Training job to retrain the model when data drift exceeds a predetermined threshold.
    • BConfigure a Git repository trigger in Cloud Build to initiate retraining when there are new code commits to the model's repository and a Pub/Sub trigger when there is new data in Cloud Storage.
    • CUse Cloud Scheduler to initiate a daily retraining job in Vertex AI Pipelines.
    • DConfigure Cloud Composer to orchestrate a weekly retraining job that includes data extraction from BigQuery, model retraining with Vertex AI Training, and model deployment to a Vertex AI endpoint.

    Show answer

    Answer: B

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PMLE practice, question 291. 364 questions available.