PMLE practice questions 141–150

1. Question 141 · 364 in bank

   You recently trained a XGBoost model that you plan to deploy to production for online inference. Before sending a predict request to your model’s binary, you need to perform a simple data preprocessing step. This step exposes a REST API that accepts requests in your internal VPC Service Controls and returns predictions. You want to configure this preprocessing step while minimizing cost and effort. What should you do?

   • AStore a pickled model in Cloud Storage. Build a Flask-based app, package the app in a custom container image, and deploy the model to Vertex AI Endpoints.
   • BBuild a Flask-based app, package the app and a pickled model in a custom container image, and deploy the model to Vertex AI Endpoints.
   • CBuild a custom predictor class based on XGBoost Predictor from the Vertex AI SDK, package it and a pickled model in a custom container image based on a Vertex built-in image, and deploy the model to Vertex AI Endpoints.
   • DBuild a custom predictor class based on XGBoost Predictor from the Vertex AI SDK, and package the handler in a custom container image based on a Vertex built-in container image. Store a pickled model in Cloud Storage, and deploy the model to Vertex AI Endpoints.

   Show answer

   Answer: D

   Explanation

   why D? Reduced Code Footprint: You only need to write the custom predictor logic, not a full Flask application. This minimizes development effort and container size. Leverages Vertex AI Features: By using the XGBoost Predictor from the Vertex AI SDK, you benefit from pre-built functionality for handling XGBoost models. Cost-Effective Deployment: Utilizing Vertex built-in container images reduces the need for custom image maintenance and potentially lowers container runtime costs. Separate Model Storage: Storing the pickled model in Cloud Storage keeps the model separate from the prediction logic, allowing for easier model updates without redeploying the entire container.

   Source: Adapted from community discussion — fitri001

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2. Question 142 · 364 in bank

   You work at a bank. You need to develop a credit risk model to support loan application decisions. You decide to implement the model by using a neural network in TensorFlow. Due to regulatory requirements, you need to be able to explain the model’s predictions based on its features. When the model is deployed, you also want to monitor the model’s performance over time. You decided to use Vertex AI for both model development and deployment. What should you do?

   • AUse Vertex Explainable AI with the sampled Shapley method, and enable Vertex AI Model Monitoring to check for feature distribution drift.
   • BUse Vertex Explainable AI with the sampled Shapley method, and enable Vertex AI Model Monitoring to check for feature distribution skew.
   • CUse Vertex Explainable AI with the XRAI method, and enable Vertex AI Model Monitoring to check for feature distribution drift.
   • DUse Vertex Explainable AI with the XRAI method, and enable Vertex AI Model Monitoring to check for feature distribution skew.

   Show answer

   Answer: A

   Explanation

   why not the others? B. Feature Distribution Skew: While skew can be relevant, drift is generally a more significant concern for credit risk models. Drift indicates a change in the underlying data distribution, potentially impacting model performance. C & D. XRAI Method: XRAI (Explainable AI for Images) is specifically designed for interpreting image classification models. It wouldn't be the most effective choice for a neural network-based credit risk model working with tabular data.

   Source: Adapted from community discussion — fitri001

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3. Question 143 · 364 in bank

   You are investigating the root cause of a misclassification error made by one of your models. You used Vertex AI Pipelines to train and deploy the model. The pipeline reads data from BigQuery. creates a copy of the data in Cloud Storage in TFRecord format, trains the model in Vertex AI Training on that copy, and deploys the model to a Vertex AI endpoint. You have identified the specific version of that model that misclassified, and you need to recover the data this model was trained on. How should you find that copy of the data?

   • AUse Vertex AI Feature Store. Modify the pipeline to use the feature store, and ensure that all training data is stored in it. Search the feature store for the data used for the training.
   • BUse the lineage feature of Vertex AI Metadata to find the model artifact. Determine the version of the model and identify the step that creates the data copy and search in the metadata for its location.
   • CUse the logging features in the Vertex AI endpoint to determine the timestamp of the model’s deployment. Find the pipeline run at that timestamp. Identify the step that creates the data copy, and search in the logs for its location.
   • DFind the job ID in Vertex AI Training corresponding to the training for the model. Search in the logs of that job for the data used for the training.

   Show answer

   Answer: B

   Explanation

   Vertex AI Metadata Lineage: This feature tracks the relationships between pipeline components and the artifacts they produce. By identifying the model version's lineage, you can pinpoint the specific pipeline run that generated it. Data Copy Step: Within the pipeline run, locate the step responsible for creating the data copy in TFRecord format for training. Metadata Search: Vertex AI Metadata likely stores information about the data copy's location in Cloud Storage, allowing you to access it.

   Source: Adapted from community discussion — fitri001

   References

   • Google Cloud documentation

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4. Question 144 · 364 in bank

   You work for a manufacturing company. You need to train a custom image classification model to detect product defects at the end of an assembly line. Although your model is performing well, some images in your holdout set are consistently mislabeled with high confidence. You want to use Vertex AI to understand your model’s results. What should you do?

   • AConfigure feature-based explanations by using Integrated Gradients. Set visualization type to PIXELS, and set clip_percent_upperbound to 95.
   • BCreate an index by using Vertex AI Matching Engine. Query the index with your mislabeled images.
   • CConfigure feature-based explanations by using XRAI. Set visualization type to OUTLINES, and set polarity to positive.
   • DConfigure example-based explanations. Specify the embedding output layer to be used for the latent space representation.

   Show answer

   Answer: D

   Explanation

   https://cloud.google.com/vertex-ai/docs/explainable-ai/overview#example-based "Improve your data or model: One of the core use cases for example-based explanations is helping you understand why your model made certain mistakes in its predictions, and using those insights to improve your data or model. [...] For example, suppose we have a model that classifies images as either a bird or a plane, and that it is misclassifying the following bird as a plane with high confidence. You can use Example-based explanations to retrieve similar images from the training set to figure out what is happening." Not A: Integrated Gradients is recommended for low-contrast images, such as X-rays https://cloud.google.com/vertex-ai/docs/explainable-ai/overview#compare-methods Not C: Cannot set Outlines for XRAI https://cloud.google.com/ai-platform/prediction/docs/ai-explanations/visualizing-explanations

   Source: Adapted from community discussion — eico

   References

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

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5. Question 145 · 364 in bank

   Your organization's call center has asked you to develop a model that analyzes customer sentiments in each call. The call center receives over one million calls daily, and data is stored in Cloud Storage. The data collected must not leave the region in which the call originated, and no Personally Identifiable Information (PII) can be stored or analyzed. The data science team has a third-party tool for visualization and access which requires a SQL ANSI-2011 compliant interface. You need to select components for data processing and for analytics. How should the data pipeline be designed?

   

   • A1= Dataflow, 2= BigQuery
   • B1 = Pub/Sub, 2= Datastore
   • C1 = Dataflow, 2 = Cloud SQL
   • D1 = Cloud Function, 2= Cloud SQL

   Show answer

   Answer: A

   Explanation

   A is correct Dataflow - Unified stream and batch data processing that's serverless, fast, and cost-effective BigQuery - Good for analytics and dashboards

   Source: Adapted from community discussion — mousseUwU

   References

   • Google Cloud documentation

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6. Question 146 · 364 in bank

   You are training models in Vertex AI by using data that spans across multiple Google Cloud projects. You need to find, track, and compare the performance of the different versions of your models. Which Google Cloud services should you include in your ML workflow?

   • ADataplex, Vertex AI Feature Store, and Vertex AI TensorBoard
   • BVertex AI Pipelines, Vertex AI Feature Store, and Vertex AI Experiments
   • CDataplex, Vertex AI Experiments, and Vertex AI ML Metadata
   • DVertex AI Pipelines, Vertex AI Experiments, and Vertex AI Metadata

   Show answer

   Answer: D

   Explanation

   Why not the others? A. Dataplex & Vertex AI Feature Store: While Dataplex can manage data across projects, it's not directly tied to model versioning and comparison. Feature Store focuses on feature engineering, not model version management. B. Vertex AI Feature Store & Vertex AI TensorBoard: Similar to option A, Feature Store isn't directly involved in model version tracking, and TensorBoard is primarily for visualizing training data and metrics, not model version comparison across projects. C. Dataplex & Vertex AI ML Metadata: Dataplex, as mentioned earlier, doesn't directly address model version comparison. While ML Metadata tracks lineage, it might not have the experiment management features of Vertex AI Experiments.

   Source: Adapted from community discussion — fitri001

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7. Question 147 · 364 in bank

   You are using Keras and TensorFlow to develop a fraud detection model. Records of customer transactions are stored in a large table in BigQuery. You need to preprocess these records in a cost-effective and efficient way before you use them to train the model. The trained model will be used to perform batch inference in BigQuery. How should you implement the preprocessing workflow?

   • AImplement a preprocessing pipeline by using Apache Spark, and run the pipeline on Dataproc. Save the preprocessed data as CSV files in a Cloud Storage bucket.
   • BLoad the data into a pandas DataFrame. Implement the preprocessing steps using pandas transformations, and train the model directly on the DataFrame.
   • CPerform preprocessing in BigQuery by using SQL. Use the BigQueryClient in TensorFlow to read the data directly from BigQuery.
   • DImplement a preprocessing pipeline by using Apache Beam, and run the pipeline on Dataflow. Save the preprocessed data as CSV files in a Cloud Storage bucket.

   Show answer

   Answer: C

   Explanation

   A. Spark on Dataproc: While powerful, it incurs additional cluster setup and management costs, potentially less cost-effective for this specific use case. B. pandas DataFrame: Loading large datasets into memory might lead to resource constraints and performance issues, especially for large-scale preprocessing. D. Apache Beam on Dataflow: While scalable, it introduces extra complexity for managing a separate pipeline and storage for preprocessed data.

   Source: Adapted from community discussion — pikachu007

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8. Question 148 · 364 in bank

   You need to use TensorFlow to train an image classification model. Your dataset is located in a Cloud Storage directory and contains millions of labeled images. Before training the model, you need to prepare the data. You want the data preprocessing and model training workflow to be as efficient, scalable, and low maintenance as possible. What should you do?

   • A1. Create a Dataflow job that creates sharded TFRecord files in a Cloud Storage directory. 2. Reference tf.data.TFRecordDataset in the training script. 3. Train the model by using Vertex AI Training with a V100 GPU.
   • B1. Create a Dataflow job that moves the images into multiple Cloud Storage directories, where each directory is named according to the corresponding label 2. Reference tfds.folder_dataset:ImageFolder in the training script. 3. Train the model by using Vertex AI Training with a V100 GPU.
   • C1. Create a Jupyter notebook that uses an nt-standard-64 V100 GPU Vertex AI Workbench instance. 2. Write a Python script that creates sharded TFRecord files in a directory inside the instance. 3. Reference tf.data.TFRecordDataset in the training script. 4. Train the model by using the Workbench instance.
   • D1. Create a Jupyter notebook that uses an n1-standard-64, V100 GPU Vertex AI Workbench instance. 2. Write a Python script that copies the images into multiple Cloud Storage directories, where each. directory is named according to the corresponding label. 3. Reference tfds.foladr_dataset.ImageFolder in the training script. 4. Train the model by using the Workbench instance.

   Show answer

   Answer: A

   Explanation

   B. Folder-Based Structure: While viable, it's less efficient for large datasets compared to TFRecord files, potentially leading to slower I/O during training. C. Workbench Processing: Local preprocessing on a single instance can be less scalable and efficient for millions of images, potentially introducing bottlenecks. D. Workbench Training: While Workbench offers a Jupyter environment, Vertex AI Training is specifically designed for scalable model training, providing optimized hardware and infrastructure.

   Source: Adapted from community discussion — pikachu007

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9. Question 149 · 364 in bank

   You are building a custom image classification model and plan to use Vertex AI Pipelines to implement the end-to-end training. Your dataset consists of images that need to be preprocessed before they can be used to train the model. The preprocessing steps include resizing the images, converting them to grayscale, and extracting features. You have already implemented some Python functions for the preprocessing tasks. Which components should you use in your pipeline?

   • ADataprocSparkBatchOp and CustomTrainingJobOp
   • BDataflowPythonJobOp, WaitGcpResourcesOp, and CustomTrainingJobOp
   • Cdsl.ParallelFor, dsl.component, and CustomTrainingJobOp
   • DImageDatasetImportDataOp, dsl.component, and AutoMLImageTrainingJobRunOp

   Show answer

   Answer: B

   Explanation

   My Answer: B Looking for the options, DataflowPythonJobOp can be used for parallelizing the preprocessing tasks, which is suitable for image resizing, converting to grayscale, and extracting features. dsl.ParallelFor could be useful for parallelizing tasks but might not be the most straightforward option for image preprocessing. Generally DataflowPythonJobOp is followed by WaitGcpResourcesOp. https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/fe7d3e4b8edc137d90ec061789b879b7cc8d3854/notebooks/community/ml_ops/stage3/get_started_with_dataflow_flex_template_component.ipynb

   Source: Adapted from community discussion — guilhermebutzke

   References

   • Google Cloud documentation

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10. Question 150 · 364 in bank

    You work for a retail company that is using a regression model built with BigQuery ML to predict product sales. This model is being used to serve online predictions. Recently you developed a new version of the model that uses a different architecture (custom model). Initial analysis revealed that both models are performing as expected. You want to deploy the new version of the model to production and monitor the performance over the next two months. You need to minimize the impact to the existing and future model users. How should you deploy the model?

    • AImport the new model to the same Vertex AI Model Registry as a different version of the existing model. Deploy the new model to the same Vertex AI endpoint as the existing model, and use traffic splitting to route 95% of production traffic to the BigQuery ML model and 5% of production traffic to the new model.
    • BImport the new model to the same Vertex AI Model Registry as the existing model. Deploy the models to one Vertex AI endpoint. Route 95% of production traffic to the BigQuery ML model and 5% of production traffic to the new model.
    • CImport the new model to the same Vertex AI Model Registry as the existing model. Deploy each model to a separate Vertex AI endpoint.
    • DDeploy the new model to a separate Vertex AI endpoint. Create a Cloud Run service that routes the prediction requests to the corresponding endpoints based on the input feature values.

    Show answer

    Answer: A

    Explanation

    Minimal Disruption: Deploying the new model to the same endpoint avoids changes for existing users. Traffic splitting ensures a gradual rollout, minimizing any potential impact on production. Performance Monitoring: By routing a small percentage of traffic (5%) to the new model, you can monitor its performance in a controlled environment for the next two months. Metrics like prediction accuracy and latency can be compared with the BigQuery ML model. Versioning in Model Registry: Storing both models in the same Vertex AI Model Registry with clear versioning allows easy tracking and management.

    Source: Adapted from community discussion — fitri001

    References

    • Google Cloud documentation

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PMLE practice, question 141. 364 questions available.