PMLE practice questions 161–170

1. Question 161 · 364 in bank

   Your work for a textile manufacturing company. Your company has hundreds of machines, and each machine has many sensors. Your team used the sensory data to build hundreds of ML models that detect machine anomalies. Models are retrained daily, and you need to deploy these models in a cost-effective way. The models must operate 24/7 without downtime and make sub millisecond predictions. What should you do?

   • ADeploy a Dataflow batch pipeline and a Vertex AI Prediction endpoint.
   • BDeploy a Dataflow batch pipeline with the Runlnference API, and use model refresh.
   • CDeploy a Dataflow streaming pipeline and a Vertex AI Prediction endpoint with autoscaling.
   • DDeploy a Dataflow streaming pipeline with the Runlnference API, and use automatic model refresh.

   Show answer

   Answer: D

   Explanation

   why D? Real-time Predictions: Dataflow streaming pipelines continuously process sensor data, enabling real-time anomaly detection with sub-millisecond predictions. This is crucial for immediate response to potential machine issues. RunInference API: This API allows invoking TensorFlow models directly within the Dataflow pipeline for on-the-fly inference. This eliminates the need for separate prediction endpoints and reduces latency. Automatic Model Refresh: Since models are retrained daily, automatic refresh ensures the pipeline utilizes the latest version without downtime. This is essential for maintaining model accuracy and anomaly detection effectiveness. Why not C? Dataflow Streaming Pipeline with Vertex AI Prediction Endpoint with Autoscaling: While autoscaling can handle varying workloads, Vertex AI Prediction endpoints might incur higher costs for real-time, high-volume predictions compared to invoking models directly within the pipeline using RunInference.

   Source: Adapted from community discussion — fitri001

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2. Question 162 · 364 in bank

   You are developing an ML model that predicts the cost of used automobiles based on data such as location, condition, model type, color, and engine/battery efficiency. The data is updated every night. Car dealerships will use the model to determine appropriate car prices. You created a Vertex AI pipeline that reads the data splits the data into training/evaluation/test sets performs feature engineering trains the model by using the training dataset and validates the model by using the evaluation dataset. You need to configure a retraining workflow that minimizes cost. What should you do?

   • ACompare the training and evaluation losses of the current run. If the losses are similar, deploy the model to a Vertex AI endpoint. Configure a cron job to redeploy the pipeline every night.
   • BCompare the training and evaluation losses of the current run. If the losses are similar, deploy the model to a Vertex AI endpoint with training/serving skew threshold model monitoring. When the model monitoring threshold is triggered redeploy the pipeline.
   • CCompare the results to the evaluation results from a previous run. If the performance improved deploy the model to a Vertex AI endpoint. Configure a cron job to redeploy the pipeline every night.
   • DCompare the results to the evaluation results from a previous run. If the performance improved deploy the model to a Vertex AI endpoint with training/serving skew threshold model monitoring. When the model monitoring threshold is triggered redeploy the pipeline.

   Show answer

   Answer: D

   Explanation

   My answer D: A and C: Not Correct: Schedule a retrain every night is not necessary since the model is performing well. B. Not Correct: This approach focuses on internal consistency within the current training run, train versus loss evaluation. Comparing similar training and validation losses doesn't guarantee better performance than previous models. This is an approach to identity overfitting, for example, or model quality. D. Correct: This approach focuses on identifying performance changes over time. Comparing to previous runs helps assess if the new model performs better than the old one on the evaluation set. we will check if this new version is better or not than the old one https://www.youtube.com/watch?v=1ykDWsnL2LE&ab_channel=GoogleCloudTech

   Source: Adapted from community discussion — guilhermebutzke

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3. Question 163 · 364 in bank

   You recently used BigQuery ML to train an AutoML regression model. You shared results with your team and received positive feedback. You need to deploy your model for online prediction as quickly as possible. What should you do?

   • ARetrain the model by using BigQuery ML, and specify Vertex AI as the model registry. Deploy the model from Vertex AI Model Registry to a Vertex AI endpoint,
   • BRetrain the model by using Vertex Al Deploy the model from Vertex AI Model. Registry to a Vertex AI endpoint.
   • CAlter the model by using BigQuery ML, and specify Vertex AI as the model registry. Deploy the model from Vertex AI Model Registry to a Vertex AI endpoint.
   • DExport the model from BigQuery ML to Cloud Storage. Import the model into Vertex AI Model Registry. Deploy the model to a Vertex AI endpoint.

   Show answer

   Answer: C

   Explanation

   the answer is C, no need to export the model : "You can register BigQuery ML models with the Model Registry, in order to manage them alongside your other ML models without needing to export them" https://cloud.google.com/bigquery/docs/managing-models-vertex a simple update is sufficient : https://cloud.google.com/bigquery/docs/update_vertex

   Source: Adapted from community discussion — sonicclasps

   References

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

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4. Question 164 · 364 in bank

   You built a deep learning-based image classification model by using on-premises data. You want to use Vertex AI to deploy the model to production. Due to security concerns, you cannot move your data to the cloud. You are aware that the input data distribution might change over time. You need to detect model performance changes in production. What should you do?

   • AUse Vertex Explainable AI for model explainability. Configure feature-based explanations.
   • BUse Vertex Explainable AI for model explainability. Configure example-based explanations.
   • CCreate a Vertex AI Model Monitoring job. Enable training-serving skew detection for your model.
   • DCreate a Vertex AI Model Monitoring job. Enable feature attribution skew and drift detection for your model.

   Show answer

   Answer: D

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5. Question 165 · 364 in bank

   You trained a model packaged it with a custom Docker container for serving, and deployed it to Vertex AI Model Registry. When you submit a batch prediction job, it fails with this error: "Error model server never became ready. Please validate that your model file or container configuration are valid. " There are no additional errors in the logs. What should you do?

   • AAdd a logging configuration to your application to emit logs to Cloud Logging
   • BChange the HTTP port in your model’s configuration to the default value of 8080
   • CChange the healthRoute value in your model’s configuration to /healthcheck
   • DPull the Docker image locally, and use the docker run command to launch it locally. Use the docker logs command to explore the error logs

   Show answer

   Answer: D

   Explanation

   My Answer: D A: Not correct: While logging can be helpful for monitoring and debugging, it won't directly address the issue of the model server not becoming ready. B: Not correct: The error message doesn't indicate a port issue, changing it preemptively might not resolve the underlying problem. C: Not correct: changing the health route, which could be helpful if the issue is related to health checks, but without further information, it's not the most conclusive option. D: CORRECT: This option allows you to simulate the deployment environment locally and inspect the logs directly, which can help diagnose the issue with the model server not becoming ready.

   Source: Adapted from community discussion — guilhermebutzke

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6. Question 166 · 364 in bank

   You are developing an ML model to identify your company’s products in images. You have access to over one million images in a Cloud Storage bucket. You plan to experiment with different TensorFlow models by using Vertex AI Training. You need to read images at scale during training while minimizing data I/O bottlenecks. What should you do?

   • ALoad the images directly into the Vertex AI compute nodes by using Cloud Storage FUSE. Read the images by using the tf.data.Dataset.from_tensor_slices function
   • BCreate a Vertex AI managed dataset from your image data. Access the AIP_TRAINING_DATA_URI environment variable to read the images by using the tf.data.Dataset.list_files function.
   • CConvert the images to TFRecords and store them in a Cloud Storage bucket. Read the TFRecords by using the tf.data.TFRecordDataset function.
   • DStore the URLs of the images in a CSV file. Read the file by using the tf.data.experimental.CsvDataset function.

   Show answer

   Answer: C

   Explanation

   TFRecords is a binary storage format optimized for TensorFlow. By storing images as TFRecords, you can improve the I/O efficiency as the data is serialized and can be efficiently loaded off-disk in a batched manner. TFRecordDataset is specifically designed for reading these files efficiently, which helps in minimizing I/O bottlenecks. This approach is typically recommended for large-scale image datasets as it ensures data is read efficiently in a manner suitable for distributed training.

   Source: Adapted from community discussion — tavva_prudhvi

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7. Question 167 · 364 in bank

   You work for a social media company. You need to detect whether posted images contain cars. Each training example is a member of exactly one class. You have trained an object detection neural network and deployed the model version to AI Platform Prediction for evaluation. Before deployment, you created an evaluation job and attached it to the AI Platform Prediction model version. You notice that the precision is lower than your business requirements allow. How should you adjust the model's final layer softmax threshold to increase precision?

   • AIncrease the recall.
   • BDecrease the recall.
   • CIncrease the number of false positives.
   • DDecrease the number of false negatives.

   Show answer

   Answer: B

   Explanation

   Decreasing FN increases recall (D). So D and A are the same. Increasing FP decreases precision (C). Answer: B ("improving precision typically reduces recall and vice versa", https://developers.google.com/machine-learning/crash-course/classification/precision-and-recall)

   Source: Adapted from community discussion — Paul_Dirac

   References

   • Google developer documentation

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8. Question 168 · 364 in bank

   You work at an ecommerce startup. You need to create a customer churn prediction model. Your company’s recent sales records are stored in a BigQuery table. You want to understand how your initial model is making predictions. You also want to iterate on the model as quickly as possible while minimizing cost. How should you build your first model?

   • AExport the data to a Cloud Storage bucket. Load the data into a pandas DataFrame on Vertex AI Workbench and train a logistic regression model with scikit-learn.
   • BCreate a tf.data.Dataset by using the TensorFlow BigQueryClient. Implement a deep neural network in TensorFlow.
   • CPrepare the data in BigQuery and associate the data with a Vertex AI dataset. Create an AutoMLTabularTrainingJob to tram a classification model.
   • DExport the data to a Cloud Storage bucket. Create a tf.data.Dataset to read the data from Cloud Storage. Implement a deep neural network in TensorFlow.

   Show answer

   Answer: C

   Explanation

   Option A: While logistic regression is interpretable, manual training in Vertex AI Workbench adds time and complexity. Options B and D: Deep neural networks can be powerful but often lack interpretability, making it challenging to understand model decisions. They also require more hands-on model development and infrastructure management.

   Source: Adapted from community discussion — pikachu007

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9. Question 169 · 364 in bank

   You are developing a training pipeline for a new XGBoost classification model based on tabular data. The data is stored in a BigQuery table. You need to complete the following steps: 1. Randomly split the data into training and evaluation datasets in a 65/35 ratio 2. Conduct feature engineering 3. Obtain metrics for the evaluation dataset 4. Compare models trained in different pipeline executions How should you execute these steps?

   • A1. Using Vertex AI Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering. 2. Enable autologging of metrics in the training component. 3. Compare pipeline runs in Vertex AI Experiments.
   • B1. Using Vertex AI Pipelines, add a component to divide the data into training and evaluation sets, and add another component for feature engineering. 2. Enable autologging of metrics in the training component. 3. Compare models using the artifacts’ lineage in Vertex ML Metadata.
   • C1. In BigQuery ML, use the CREATE MODEL statement with BOOSTED_TREE_CLASSIFIER as the model type and use BigQuery to handle the data splits. 2. Use a SQL view to apply feature engineering and train the model using the data in that view. 3. Compare the evaluation metrics of the models by using a SQL query with the ML.TRAINING_INFO statement.
   • D1. In BigQuery ML, use the CREATE MODEL statement with BOOSTED_TREE_CLASSIFIER as the model type and use BigQuery to handle the data splits. 2. Use ML TRANSFORM to specify the feature engineering transformations and tram the model using the data in the table. 3. Compare the evaluation metrics of the models by using a SQL query with the ML.TRAINING_INFO statement.

   Show answer

   Answer: A

   Explanation

   Option B: While Vertex ML Metadata provides artifact lineage, it's less comprehensive for model comparison than Experiments. Options C and D: BigQuery ML is powerful for in-database model training, but it has limitations in pipeline orchestration, complex feature engineering, and detailed model comparison features, making it less suitable for this scenario.

   Source: Adapted from community discussion — pikachu007

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10. Question 170 · 364 in bank

    You work for a company that sells corporate electronic products to thousands of businesses worldwide. Your company stores historical customer data in BigQuery. You need to build a model that predicts customer lifetime value over the next three years. You want to use the simplest approach to build the model and you want to have access to visualization tools. What should you do?

    • ACreate a Vertex AI Workbench notebook to perform exploratory data analysis. Use IPython magics to create a new BigQuery table with input features. Use the BigQuery console to run the CREATE MODEL statement. Validate the results by using the ML.EVALUATE and ML.PREDICT statements.
    • BRun the CREATE MODEL statement from the BigQuery console to create an AutoML model. Validate the results by using the ML.EVALUATE and ML.PREDICT statements.
    • CCreate a Vertex AI Workbench notebook to perform exploratory data analysis and create input features. Save the features as a CSV file in Cloud Storage. Import the CSV file as a new BigQuery table. Use the BigQuery console to run the CREATE MODEL statement. Validate the results by using the ML.EVALUATE and ML.PREDICT statements.
    • DCreate a Vertex AI Workbench notebook to perform exploratory data analysis. Use IPython magics to create a new BigQuery table with input features, create the model, and validate the results by using the CREATE MODEL, ML.EVALUATE, and ML.PREDICT statements.

    Show answer

    Answer: B

    Explanation

    D Vertex AI Workbench notebook: Provides an environment for data analysis, model building, and visualization tools all in one place. IPython magics: Allows seamless interaction with BigQuery for data exploration and feature creation directly within the notebook. CREATE MODEL statement: Enables model creation within the notebook environment, simplifying the workflow. ML.EVALUATE and ML.PREDICT statements: Facilitate model validation directly within the notebook for assessing performance.

    Source: Adapted from community discussion — rcapj

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PMLE practice, question 161. 364 questions available.