PMLE practice questions 131–140

1. Question 131 · 364 in bank

   Your team frequently creates new ML models and runs experiments. Your team pushes code to a single repository hosted on Cloud Source Repositories. You want to create a continuous integration pipeline that automatically retrains the models whenever there is any modification of the code. What should be your first step to set up the CI pipeline?

   • AConfigure a Cloud Build trigger with the event set as "Pull Request"
   • BConfigure a Cloud Build trigger with the event set as "Push to a branch"
   • CConfigure a Cloud Function that builds the repository each time there is a code change
   • DConfigure a Cloud Function that builds the repository each time a new branch is created

   Show answer

   Answer: B

   Explanation

   Cloud Build Integration: Cloud Build is Google Cloud's fully managed CI/CD platform, designed to automate builds and deployments, making it ideal for this task. Trigger on Code Pushes: Setting the trigger event to "Push to a branch" ensures that the pipeline automatically activates whenever new code is pushed to any branch of the repository, aligning with the goal of retraining models on code modifications.

   Source: Adapted from community discussion — pikachu007

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2. Question 132 · 364 in bank

   You have built a custom model that performs several memory-intensive preprocessing tasks before it makes a prediction. You deployed the model to a Vertex AI endpoint, and validated that results were received in a reasonable amount of time. After routing user traffic to the endpoint, you discover that the endpoint does not autoscale as expected when receiving multiple requests. What should you do?

   • AUse a machine type with more memory
   • BDecrease the number of workers per machine
   • CIncrease the CPU utilization target in the autoscaling configurations.
   • DDecrease the CPU utilization target in the autoscaling configurations

   Show answer

   Answer: D

   Explanation

   D. The idea behind this question is getting autoscaling to handle well the fluctuating input of requests. Changing the machine (A) is not related to autoscaling, and you might not be using the full potential of the machine during the whole time, bur rather only during instances of peak traffic. You need to lower the autoscaling threshold (the target utilization metric mentioned in the options is CPU, so we will go with this) so you make use of more resources whenever too many memory-intensive requests are happening. https://cloud.google.com/compute/docs/autoscaler/scaling-cpu#scaling_based_on_cpu_utilization https://cloud.google.com/compute/docs/autoscaler#autoscaling_policy

   Source: Adapted from community discussion — b1a8fae

   References

   • Google Cloud documentation
   • Google Cloud documentation

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3. Question 133 · 364 in bank

   Your company manages an ecommerce website. You developed an ML model that recommends additional products to users in near real time based on items currently in the user’s cart. The workflow will include the following processes: 1. The website will send a Pub/Sub message with the relevant data and then receive a message with the prediction from Pub/Sub 2. Predictions will be stored in BigQuery 3. The model will be stored in a Cloud Storage bucket and will be updated frequently You want to minimize prediction latency and the effort required to update the model. How should you reconfigure the architecture?

   • AWrite a Cloud Function that loads the model into memory for prediction. Configure the function to be triggered when messages are sent to Pub/Sub.
   • BCreate a pipeline in Vertex AI Pipelines that performs preprocessing, prediction, and postprocessing. Configure the pipeline to be triggered by a Cloud Function when messages are sent to Pub/Sub.
   • CExpose the model as a Vertex AI endpoint. Write a custom DoFn in a Dataflow job that calls the endpoint for prediction.
   • DUse the RunInference API with WatchFilePattern in a Dataflow job that wraps around the model and serves predictions.

   Show answer

   Answer: D

   Explanation

   My answer: D This Google Documentation explains “Instead of deploying the model to an endpoint, you can use the RunInference API to serve machine learning models in your Apache Beam pipeline. This approach has several advantages, including flexibility and portability.” https://cloud.google.com/blog/products/ai-machine-learning/streaming-prediction-with-dataflow-and-vertex This documentation uses RunInference and WatchFilePattern to “to automatically update the ML model without stopping the Apache Beam”. https://cloud.google.com/dataflow/docs/notebooks/automatic_model_refresh So, thinking in “minimize prediction latency”, its suggested use RunInfenrece, while “effort required to update the model” the **WatchFilePattern is the best approach.** I think D is the best option

   Source: Adapted from community discussion — guilhermebutzke

   References

   • Google Cloud documentation
   • Google Cloud documentation

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4. Question 134 · 364 in bank

   You trained a text classification model. You have the following SignatureDefs:

   

   You started a TensorFlow-serving component server and tried to send an HTTP request to get a prediction using: headers = {"content-type": "application/json"} json_response = requests.post('http: //localhost:8501/v1/models/text_model:predict', data=data, headers=headers) What is the correct way to write the predict request?

   • Adata = json.dumps({ג€signature_nameג€: ג€seving_defaultג€, ג€instancesג€ [['ab', 'bc', 'cd']]})
   • Bdata = json.dumps({ג€signature_nameג€: ג€serving_defaultג€, ג€instancesג€ [['a', 'b', 'c', 'd', 'e', 'f']]})
   • Cdata = json.dumps({ג€signature_nameג€: ג€serving_defaultג€, ג€instancesג€ [['a', 'b', 'c'], ['d', 'e', 'f']]})
   • Ddata = json.dumps({ג€signature_nameג€: ג€serving_defaultג€, ג€instancesג€ [['a', 'b'], ['c', 'd'], ['e', 'f']]})

   Show answer

   Answer: D

   Explanation

   Options: A. data = json.dumps({“signature_name”: “seving_default”, “instances” [[‘ab’, ‘bc’, ‘cd’]]}) B. data = json.dumps({“signature_name”: “serving_default”, “instances” [[‘a’, ‘b’, ‘c’, ‘d’, ‘e’, ‘f’]]}) C. data = json.dumps({“signature_name”: “serving_default”, “instances” [[‘a’, ‘b’, ‘c’], [‘d’, ‘e’, ‘f’]]}) D. data = json.dumps({“signature_name”: “serving_default”, “instances” [[‘a’, ‘b’], [‘c’, ‘d’], [‘e’, ‘f’]]})

   Source: Adapted from community discussion — [Removed]

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5. Question 135 · 364 in bank

   You are collaborating on a model prototype with your team. You need to create a Vertex AI Workbench environment for the members of your team and also limit access to other employees in your project. What should you do?

   • A1. Create a new service account and grant it the Notebook Viewer role 2. Grant the Service Account User role to each team member on the service account 3. Grant the Vertex AI User role to each team member 4. Provision a Vertex AI Workbench user-managed notebook instance that uses the new service account
   • B1. Grant the Vertex AI User role to the default Compute Engine service account 2. Grant the Service Account User role to each team member on the default Compute Engine service account 3. Provision a Vertex AI Workbench user-managed notebook instance that uses the default Compute Engine service account.
   • C1. Create a new service account and grant it the Vertex AI User role 2. Grant the Service Account User role to each team member on the service account 3. Grant the Notebook Viewer role to each team member. 4. Provision a Vertex AI Workbench user-managed notebook instance that uses the new service account
   • D1. Grant the Vertex AI User role to the primary team member 2. Grant the Notebook Viewer role to the other team members 3. Provision a Vertex AI Workbench user-managed notebook instance that uses the primary user’s account

   Show answer

   Answer: C

   Explanation

   Dedicated Service Account: Creating a separate service account ensures isolation and control over access to Vertex AI resources. Vertex AI User Role: Granting this role to the service account provides it with necessary permissions to interact with Vertex AI services. Service Account User Role: Assigning this role to team members allows them to impersonate the service account, enabling them to use its permissions. Notebook Viewer Role: This role grants team members access to the notebook instance, but not direct Vertex AI resource management. User-Managed Notebook Instance: This type of instance uses a specific service account, ensuring access control is aligned with the designated service account's permissions.

   Source: Adapted from community discussion — pikachu007

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6. Question 136 · 364 in bank

   You work at a leading healthcare firm developing state-of-the-art algorithms for various use cases. You have unstructured textual data with custom labels. You need to extract and classify various medical phrases with these labels. What should you do?

   • AUse the Healthcare Natural Language API to extract medical entities
   • BUse a BERT-based model to fine-tune a medical entity extraction model
   • CUse AutoML Entity Extraction to train a medical entity extraction model
   • DUse TensorFlow to build a custom medical entity extraction model

   Show answer

   Answer: C

   Explanation

   C. "AutoML Entity Extraction for Healthcare allows you to create a custom entity extraction model trained using your own annotated medical text and using your own categories." https://cloud.google.com/healthcare-api/docs/concepts/nlp#choosing_between_the_and

   Source: Adapted from community discussion — b1a8fae

   References

   • Google Cloud documentation

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7. Question 137 · 364 in bank

   You developed a custom model by using Vertex AI to predict your application's user churn rate. You are using Vertex AI Model Monitoring for skew detection. The training data stored in BigQuery contains two sets of features - demographic and behavioral. You later discover that two separate models trained on each set perform better than the original model. You need to configure a new model monitoring pipeline that splits traffic among the two models. You want to use the same prediction-sampling-rate and monitoring-frequency for each model. You also want to minimize management effort. What should you do?

   • AKeep the training dataset as is. Deploy the models to two separate endpoints, and submit two Vertex AI Model Monitoring jobs with appropriately selected feature-thresholds parameters.
   • BKeep the training dataset as is. Deploy both models to the same endpoint and submit a Vertex AI Model Monitoring job with a monitoring-config-from-file parameter that accounts for the model IDs and feature selections.
   • CSeparate the training dataset into two tables based on demographic and behavioral features. Deploy the models to two separate endpoints, and submit two Vertex AI Model Monitoring jobs.
   • DSeparate the training dataset into two tables based on demographic and behavioral features. Deploy both models to the same endpoint, and submit a Vertex AI Model Monitoring job with a monitoring-config-from-file parameter that accounts for the model IDs and training datasets.

   Show answer

   Answer: B

   Explanation

   My answer: B If you're using Vertex AI Model Monitoring for skew detection and your data is stored in BigQuery, it's not strictly necessary to separate the data into two tables. Vertex AI Model Monitoring can indeed analyze each feature individually to detect skew. So, isn't necessary to separate data. Then, the `monitoring-config-from-file` parameter lets you specify unique configurations for each model, including ID and training data information. This ensures targeted monitoring and analysis and a unique monitoring job.

   Source: Adapted from community discussion — guilhermebutzke

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8. Question 138 · 364 in bank

   You work for a pharmaceutical company based in Canada. Your team developed a BigQuery ML model to predict the number of flu infections for the next month in Canada. Weather data is published weekly, and flu infection statistics are published monthly. You need to configure a model retraining policy that minimizes cost. What should you do?

   • ADownload the weather and flu data each week. Configure Cloud Scheduler to execute a Vertex AI pipeline to retrain the model weekly.
   • BDownload the weather and flu data each month. Configure Cloud Scheduler to execute a Vertex AI pipeline to retrain the model monthly.
   • CDownload the weather and flu data each week. Configure Cloud Scheduler to execute a Vertex AI pipeline to retrain the model every month.
   • DDownload the weather data each week, and download the flu data each month. Deploy the model to a Vertex AI endpoint with feature drift monitoring, and retrain the model if a monitoring alert is detected.

   Show answer

   Answer: D

   Explanation

   Weather Data Update: Downloading weather data weekly captures the latest trends potentially influencing flu infections. Flu Data Update: Downloading flu statistics monthly aligns with the data publication schedule and avoids unnecessary processing for data that might not have changed. Feature Drift Monitoring: Vertex AI endpoint monitoring helps identify significant changes in the weather data distribution (feature drift) over time. Retrain Based on Alerts: Retraining the model is triggered only when feature drift is detected, ensuring the model stays relevant without unnecessary retraining cycles.

   Source: Adapted from community discussion — fitri001

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9. Question 139 · 364 in bank

   You are building a MLOps platform to automate your company’s ML experiments and model retraining. You need to organize the artifacts for dozens of pipelines. How should you store the pipelines’ artifacts?

   • AStore parameters in Cloud SQL, and store the models’ source code and binaries in GitHub.
   • BStore parameters in Cloud SQL, store the models’ source code in GitHub, and store the models’ binaries in Cloud Storage.
   • CStore parameters in Vertex ML Metadata, store the models’ source code in GitHub, and store the models’ binaries in Cloud Storage.
   • DStore parameters in Vertex ML Metadata and store the models’ source code and binaries in GitHub.

   Show answer

   Answer: C

   Explanation

   Vertex ML Metadata: This service is specifically designed to store and track metadata for ML pipelines, including parameters. It provides a centralized location to manage and query pipeline execution details, making it ideal for dozens of pipelines. Cloud Storage: This is a scalable and cost-effective storage solution for model binaries. It integrates well with Vertex AI and other cloud services. GitHub: While not a Google Cloud service, it's a popular version control system well-suited for storing and managing your models' source code, particularly for collaboration among team members.

   Source: Adapted from community discussion — fitri001

   References

   • Google Cloud documentation

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10. Question 140 · 364 in bank

    You work for a telecommunications company. You’re building a model to predict which customers may fail to pay their next phone bill. The purpose of this model is to proactively offer at-risk customers assistance such as service discounts and bill deadline extensions. The data is stored in BigQuery and the predictive features that are available for model training include: - Customer_id - Age - Salary (measured in local currency) - Sex - Average bill value (measured in local currency) - Number of phone calls in the last month (integer) - Average duration of phone calls (measured in minutes) You need to investigate and mitigate potential bias against disadvantaged groups, while preserving model accuracy. What should you do?

    • ADetermine whether there is a meaningful correlation between the sensitive features and the other features. Train a BigQuery ML boosted trees classification model and exclude the sensitive features and any meaningfully correlated features.
    • BTrain a BigQuery ML boosted trees classification model with all features. Use the ML.GLOBAL_EXPLAIN method to calculate the global attribution values for each feature of the model. If the feature importance value for any of the sensitive features exceeds a threshold, discard the model and tram without this feature.
    • CTrain a BigQuery ML boosted trees classification model with all features. Use the ML.EXPLAIN_PREDICT method to calculate the attribution values for each feature for each customer in a test set. If for any individual customer, the importance value for any feature exceeds a predefined threshold, discard the model and train the model again without this feature.
    • DDefine a fairness metric that is represented by accuracy across the sensitive features. Train a BigQuery ML boosted trees classification model with all features. Use the trained model to make predictions on a test set. Join the data back with the sensitive features, and calculate a fairness metric to investigate whether it meets your requirements.

    Show answer

    Answer: D

    Explanation

    Fairness Metric: Defining a metric like parity (equal accuracy) or calibration (similar predicted probabilities) across sensitive features like age, sex, or salary allows you to quantify potential bias. Model Training with All Features (Initially): Training the model with all features provides a baseline performance and allows you to identify potentially biased features later. Test Set Predictions: Making predictions on a held-out test set ensures the evaluation is based on unseen data and avoids overfitting. Joining Back Sensitive Features: Reintroducing sensitive features after prediction allows you to calculate fairness metrics for different customer groups. Iterative Refinement: Based on the fairness metric results, you can determine if further mitigation strategies are needed.

    Source: Adapted from community discussion — fitri001

    References

    • Google Cloud documentation

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PMLE practice, question 131. 364 questions available.