PMLE Collaborating Within and Across Teams to Manage Data and Models — All Questions With Answers

Question 1mediummultiple choice

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A data science team uses Vertex AI Experiments to track training runs. They want to automatically log parameters, metrics, and artifacts for all runs with minimal code changes. Which approach should they take?

Question 2easymultiple choice

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A machine learning team wants to share features across multiple models to reduce training-serving skew and ensure consistency. Which Vertex AI service should they use?

Question 3mediummultiple choice

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An organization uses Vertex AI Pipelines and wants to track the lineage of datasets, models, and metrics across pipeline runs. They need to query upstream and downstream dependencies of an artifact. Which service should they use?

Question 4hardmultiple choice

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A team uses Vertex AI Feature Store with an online store for low-latency serving. They need to support frequent updates to features (e.g., every minute) and require high write throughput (thousands of writes per second). Which online store type should they choose?

Question 5mediummultiple choice

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A machine learning team wants to implement champion/challenger model deployment. They have two model versions: v1 (champion) and v2 (challenger). They deploy both to the same endpoint with traffic splitting. How should they manage model versions in Vertex AI Model Registry to reflect this?

Question 6mediummultiple choice

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A data engineer needs to version large datasets (multiple TB) in a Data Lake on Google Cloud. They require ACID transactions to ensure consistency when multiple jobs read/write concurrently. Which solution should they use?

Question 7easymultiple choice

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A team wants to use Vertex AI Workbench for collaborative notebook development. They need a persistent environment that can be stopped and restarted without losing installed packages and data. Which instance type should they choose?

Question 8mediummultiple choice

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A team is building ML pipelines with Vertex AI. They want to reuse standard pipeline components across teams and enforce governance. What approach should they take?

Question 9hardmultiple choice

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A company uses Vertex AI Feature Store for feature engineering. They need to ensure point-in-time correctness to avoid data leakage during training. Which feature retrieval method should they use?

Question 10mediummultiple choice

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A machine learning engineer needs to deploy a model to an endpoint for real-time predictions. The model is registered in Vertex AI Model Registry. Which command should they use to create an endpoint and deploy the model with the alias 'champion'?

Question 11easymultiple choice

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A data scientist wants to automatically generate model documentation that includes model purpose, training data, evaluation results, and intended use. Which tool should they use?

Question 12hardmultiple choice

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A team monitors features in Vertex AI Feature Store for drift. They want to set up automated alerts when a feature's distribution deviates significantly from the baseline. Which feature monitoring configuration should they use?

Question 13mediummulti select

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A company wants to implement a central model governance strategy using Vertex AI. They need to track model lineage, store evaluation metrics, and manage model versions across teams. Which THREE Vertex AI services should they use? (Choose 3)

Question 14hardmulti select

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A team uses Vertex AI Feature Store with an online store for real-time predictions. They notice that the online store queries are taking longer than expected. Which TWO actions could improve online store performance? (Choose 2)

Question 15mediummulti select

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A data science team collaborates using Vertex AI Workbench user-managed notebooks. They want to version control their notebook code and share it with team members. Which TWO tools should they use? (Choose 2)

Question 16easymultiple choice

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A data science team wants to share engineered features across multiple projects while ensuring low-latency serving for online predictions. Which Google Cloud service should they use to store and serve these features?

Question 17mediummultiple choice

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You are configuring a Vertex AI Feature Store online store for a real-time recommendation system that requires single-digit millisecond latency and high throughput. The feature values are updated frequently. Which online store type should you use?

Question 18hardmultiple choice

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A team is training a model using historical data and wants to avoid data leakage when joining feature values from a feature store. The features include time-varying data like user activity counts. Which retrieval method should they use when creating a training dataset?

Question 19mediummultiple choice

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You are setting up feature monitoring in Vertex AI Feature Store to detect drift in a numerical feature. The monitoring job should run daily and alert if the Jensen-Shannon divergence exceeds 0.1. Which configuration should you use?

Question 20easymultiple choice

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A company uses Vertex AI Model Registry to manage multiple model versions. They want to designate a model version as 'champion' for production deployment and another as 'challenger' for A/B testing. Which feature of the registry should they use?

Question 21mediummultiple choice

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A data scientist is using Vertex AI Experiments to track training runs. They want to automatically log all hyperparameters, metrics, and model artifacts without modifying their training code. Which approach should they use?

Question 22hardmultiple choice

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A machine learning pipeline in Vertex AI produces a dataset artifact, a trained model, and evaluation metrics. The team wants to query the lineage to find all downstream artifacts that depend on a particular dataset. Which Vertex AI service should they use?

Question 23easymultiple choice

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A team of data scientists is collaborating on notebooks in Vertex AI Workbench. They need to use Git for version control and share notebooks with real-time editing. Which type of Workbench instance should they choose?

Question 24mediummultiple choice

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You are using DVC for data versioning in an ML project on Google Cloud. Your training data is stored in Cloud Storage. You want to track a new version of the dataset after preprocessing. Which DVC command should you use to register the changes?

Question 25mediummultiple choice

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An organisation uses Delta Lake on Dataproc to manage a data lake for ML training. They need ACID transactions for concurrent reads and writes. Which file format does Delta Lake use as the underlying storage?

Question 26hardmultiple choice

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You need to create a reproducible snapshot of a BigQuery table as of a specific timestamp for ML model training. The snapshot should be queryable without copying the entire dataset. Which BigQuery feature should you use?

Question 27easymultiple choice

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A team wants to enforce governance and compliance for all ML models across the organisation. They need a centralised repository that tracks model versions, deployment history, and evaluation metrics. Which service should they use?

Question 28mediummulti select

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A company uses Vertex AI Pipelines for ML workflows. They want to standardize pipeline templates across teams to ensure consistency. Which TWO approaches should they use?

Question 29hardmulti select

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A machine learning team needs to ensure that the same features used for training are used for serving in production to avoid training-serving skew. They use Vertex AI Feature Store. Which THREE actions should they take?

Question 30mediummulti select

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A data science team uses Vertex AI Experiments to compare multiple model training runs. They want to capture and compare hyperparameters, metrics, and code versions for each run. Which TWO steps should they take?

Question 31easymultiple choice

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A data scientist wants to track machine learning experiments, including parameters, metrics, and artifacts, and compare runs. Which Vertex AI service should they use?

Question 32mediummultiple choice

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A team uses Vertex AI Feature Store with an online store. They need low-latency serving for millions of features with high write throughput. Which online store type should they choose?

Question 33hardmultiple choice

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A company trains a model using features from Vertex AI Feature Store. They notice training-serving skew because the feature values used at training time differ from those served online. How should they address this?

Question 34mediummultiple choice

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A data science team wants to version control their datasets along with code using Git. They need a tool that integrates with Git and tracks changes to large data files. Which tool should they use?

Question 35easymultiple choice

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An ML engineer needs to deploy a model from Vertex AI Model Registry to an endpoint. The model has multiple versions. They want to designate one version as the 'champion' for production traffic. How should they do this?

Question 36mediummultiple choice

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A company uses Vertex AI Pipelines to train and deploy models. They want to automatically generate model documentation that includes model details, intended use, and evaluation results. What should they use?

Question 37mediummultiple choice

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A team wants to share feature definitions across multiple projects in their organization using Vertex AI Feature Store. What is the recommended approach?

Question 38easymultiple choice

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A data scientist is using Vertex AI Workbench notebooks and wants to collaborate with team members in real-time on the same notebook. Which notebook type supports real-time collaboration?

Question 39hardmultiple choice

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A team uses Vertex AI Metadata to track pipeline runs. They need to identify all artifacts that were generated by a particular pipeline execution. Which API method should they use?

Question 40mediummultiple choice

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A company uses Delta Lake on Dataproc for their data lake. They need to ensure ACID transactions and schema enforcement for data ingested from streaming sources. Which Delta Lake feature should they enable?

Question 41hardmultiple choice

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An organization needs to implement MLOps with standardized pipeline templates across multiple teams. Which Vertex AI feature should they use to create reusable pipeline components?

Question 42mediummultiple choice

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A data engineer wants to create a BigQuery table snapshot for point-in-time recovery of a critical dataset. The snapshot should be created daily and retained for 30 days. What should they use?

Question 43mediummulti select

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A company wants to monitor features in Vertex AI Feature Store for drift over time. Which two services should they use? (Choose two.)

Question 44hardmulti select

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A team uses Vertex AI Pipelines and wants to track lineage of artifacts and executions. Which three resources should they use? (Choose three.)

Question 45mediummulti select

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A company wants to implement a centralized model registry for governance. Which two features should they use? (Choose two.)

Question 46mediummultiple choice

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An ML team wants to share feature definitions across multiple projects to reduce training-serving skew and ensure consistency. They currently store features in Cloud Storage and manually coordinate updates, leading to errors. Which Google Cloud service should they use to centrally manage and serve features for both training and online inference?

Question 47hardmultiple choice

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A data scientist is training a model using Vertex AI Experiments and wants to automatically log model parameters, metrics, and artifacts without modifying their training script. Which approach should they use?

Question 48mediummultiple choice

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An ML engineer has a model trained in Vertex AI and wants to deploy it to an endpoint with autoscaling and traffic splitting for canary testing. They have the model artifact stored in Vertex AI Model Registry with alias 'champion'. What is the correct sequence of steps?

Question 49easymultiple choice

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A team wants to track the lineage of ML pipeline runs, including which datasets, parameters, and models were used in each execution. Which Vertex AI service should they use?

Question 50mediummultiple choice

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An organization uses Vertex AI Workbench user-managed notebooks and wants to enable collaboration where multiple data scientists can edit the same notebook simultaneously. Which configuration should they use?

Question 51hardmultiple choice

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A company uses BigQuery as their data warehouse. They want to version datasets for ML experiments and be able to query snapshots at specific points in time. Which approach is most cost-effective and requires minimal operational overhead?

Question 52easymultiple choice

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An ML team uses Vertex AI Pipelines and wants to automatically generate model cards documenting model purpose, evaluation results, and intended use. Which approach should they take?

Question 53mediummultiple choice

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A team is using Vertex AI Feature Store with an online store for low-latency serving. They notice increasing latency during peak hours. The feature data is updated frequently and requires strong consistency. Which online store type should they use?

Question 54mediummultiple choice

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A data scientist needs to retrieve training data from Vertex AI Feature Store that exactly matches the feature values as they were at a specific historical timestamp to avoid label leakage. Which feature view configuration should they use?

Question 55easymultiple choice

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An ML team wants to monitor feature drift in their production model. Which Vertex AI Feature Store capability should they use?

Question 56hardmultiple choice

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A company uses Vertex AI Pipelines to orchestrate ML workflows. After a pipeline run, they want to query the lineage of a particular model artifact to find out which dataset and hyperparameters were used to produce it. Which API method should they use?

Question 57mediummultiple choice

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A team is using Delta Lake on Dataproc for their data lake with ACID transactions. They want to version data for ML experiments and roll back to a previous version if needed. Which Delta Lake feature should they use?

Question 58mediummulti select

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An organization wants to implement central governance for ML models across teams. Which TWO services should they use together to achieve model versioning, lineage, and deployment management? (Select 2)

Question 59hardmulti select

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An ML team uses Vertex AI Workbench managed notebooks and wants to version their notebook code and collaborate using Git. Which THREE steps are required to set up Git integration? (Select 3)

Question 60easymulti select

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A company wants to use DVC for data versioning alongside their ML code in Git. Which TWO statements about DVC are correct? (Select 2)

Question 61mediummultiple choice

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A team is building a fraud detection model that requires joining real-time transaction features with historical user features. They need to ensure that the training data does not use future information (data leakage). Which Vertex AI Feature Store capability should they use?

Question 62easymultiple choice

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An ML team wants to automatically track training runs, including hyperparameters and metrics, with minimal code changes. Which Vertex AI service should they use?

Question 63mediummultiple choice

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A data science team needs to share features across multiple ML models while ensuring consistency between training and serving. Which approach best achieves this?

Question 64hardmultiple choice

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An ML engineer trained a model and registered it in Vertex AI Model Registry. They want to assign the alias 'champion' to the best-performing version for production deployment. Which gcloud command should they use?

Question 65easymultiple choice

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Which Vertex AI service is used to track the lineage of ML pipeline components, artefacts, and executions?

Question 66mediummultiple choice

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A team uses Vertex AI Workbench managed notebooks. They want to version control their notebook files and collaborate using Git. What is the best way to integrate Git?

Question 67mediummultiple choice

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An ML team wants to implement data versioning for large datasets stored in Google Cloud Storage. They need to track changes over time and reproduce previous data states. Which tool is most appropriate?

Question 68hardmultiple choice

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A company uses Vertex AI Feature Store with an online store for low-latency serving. They observe high latency during peak hours. The feature values are small (< 1 KB each) and the workload is read-heavy. Which change would most effectively reduce latency?

Question 69easymultiple choice

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What is the primary benefit of using a centralised model registry in MLOps?

Question 70mediummultiple choice

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An ML engineer needs to deploy a model to an endpoint and gradually shift traffic from the previous version (champion) to a new version (challenger) for A/B testing. How should they configure the endpoint?

Question 71hardmultiple choice

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A team wants to implement automated model documentation that captures training data, feature importance, evaluation metrics, and intended use. Which Vertex AI feature supports this?

Question 72mediummulti select

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A company is implementing MLOps with Vertex AI. They need to ensure that only approved models can be deployed to production. Which TWO practices should they adopt?

Question 73mediummulti select

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An ML team uses Delta Lake on Dataproc for data versioning. Which THREE benefits does Delta Lake provide?

Question 74hardmulti select

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A team wants to monitor features in Vertex AI Feature Store for drift. Which TWO configurations are required?

Question 75easymulti select

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Which THREE are valid uses of Vertex AI Metadata?

Question 76mediummultiple choice

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A data science team wants to share a set of engineered features across multiple projects and teams to reduce training-serving skew and ensure consistency. They need low-latency serving (single-digit milliseconds) for online predictions and also need to retrieve historical feature values for training. Which approach should they take?

Question 77hardmulti select

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A team is operationalizing a machine learning pipeline using Vertex AI. They want to automatically track experiment runs, log model parameters and metrics, and store model artifacts for reproducibility. They also need to capture lineage between pipeline components (e.g., which dataset and hyperparameter tuning job produced a model). Which TWO services should they use together to achieve this? (Choose two.)

Question 78mediummulti select

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A company is implementing MLOps on Google Cloud and needs to manage model versions, assign aliases (e.g., 'champion' for production, 'challenger' for staging), store evaluation metrics alongside each model version, and deploy models to endpoints. Which service should they use? (Choose THREE that are part of the solution.)

Question 79mediummultiple choice

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A team uses Vertex AI Workbench notebooks for collaborative model development. They want to ensure that code changes are version-controlled, that multiple data scientists can work on the same notebook without conflicts, and that the environment is reproducible across team members. Which approach should they take?

Question 1mediummultiple choice

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A data science team uses Vertex AI Experiments to track training runs. They want to automatically log parameters, metrics, and artifacts for all runs with minimal code changes. Which approach should they take?

Question 2easymultiple choice

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A machine learning team wants to share features across multiple models to reduce training-serving skew and ensure consistency. Which Vertex AI service should they use?

Question 3mediummultiple choice

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An organization uses Vertex AI Pipelines and wants to track the lineage of datasets, models, and metrics across pipeline runs. They need to query upstream and downstream dependencies of an artifact. Which service should they use?

Question 4hardmultiple choice

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A team uses Vertex AI Feature Store with an online store for low-latency serving. They need to support frequent updates to features (e.g., every minute) and require high write throughput (thousands of writes per second). Which online store type should they choose?

Question 5mediummultiple choice

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A machine learning team wants to implement champion/challenger model deployment. They have two model versions: v1 (champion) and v2 (challenger). They deploy both to the same endpoint with traffic splitting. How should they manage model versions in Vertex AI Model Registry to reflect this?

Question 6mediummultiple choice

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A data engineer needs to version large datasets (multiple TB) in a Data Lake on Google Cloud. They require ACID transactions to ensure consistency when multiple jobs read/write concurrently. Which solution should they use?

Question 7easymultiple choice

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A team wants to use Vertex AI Workbench for collaborative notebook development. They need a persistent environment that can be stopped and restarted without losing installed packages and data. Which instance type should they choose?

Question 8mediummultiple choice

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A team is building ML pipelines with Vertex AI. They want to reuse standard pipeline components across teams and enforce governance. What approach should they take?

Question 9hardmultiple choice

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A company uses Vertex AI Feature Store for feature engineering. They need to ensure point-in-time correctness to avoid data leakage during training. Which feature retrieval method should they use?

Question 10mediummultiple choice

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A machine learning engineer needs to deploy a model to an endpoint for real-time predictions. The model is registered in Vertex AI Model Registry. Which command should they use to create an endpoint and deploy the model with the alias 'champion'?

Question 11easymultiple choice

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A data scientist wants to automatically generate model documentation that includes model purpose, training data, evaluation results, and intended use. Which tool should they use?

Question 12hardmultiple choice

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A team monitors features in Vertex AI Feature Store for drift. They want to set up automated alerts when a feature's distribution deviates significantly from the baseline. Which feature monitoring configuration should they use?

Question 13mediummulti select

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A company wants to implement a central model governance strategy using Vertex AI. They need to track model lineage, store evaluation metrics, and manage model versions across teams. Which THREE Vertex AI services should they use? (Choose 3)

Question 14hardmulti select

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A team uses Vertex AI Feature Store with an online store for real-time predictions. They notice that the online store queries are taking longer than expected. Which TWO actions could improve online store performance? (Choose 2)

Question 15mediummulti select

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A data science team collaborates using Vertex AI Workbench user-managed notebooks. They want to version control their notebook code and share it with team members. Which TWO tools should they use? (Choose 2)

Question 16easymultiple choice

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A data science team wants to share engineered features across multiple projects while ensuring low-latency serving for online predictions. Which Google Cloud service should they use to store and serve these features?

Question 17mediummultiple choice

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You are configuring a Vertex AI Feature Store online store for a real-time recommendation system that requires single-digit millisecond latency and high throughput. The feature values are updated frequently. Which online store type should you use?

Question 18hardmultiple choice

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A team is training a model using historical data and wants to avoid data leakage when joining feature values from a feature store. The features include time-varying data like user activity counts. Which retrieval method should they use when creating a training dataset?

Question 19mediummultiple choice

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You are setting up feature monitoring in Vertex AI Feature Store to detect drift in a numerical feature. The monitoring job should run daily and alert if the Jensen-Shannon divergence exceeds 0.1. Which configuration should you use?

Question 20easymultiple choice

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A company uses Vertex AI Model Registry to manage multiple model versions. They want to designate a model version as 'champion' for production deployment and another as 'challenger' for A/B testing. Which feature of the registry should they use?

Question 21mediummultiple choice

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A data scientist is using Vertex AI Experiments to track training runs. They want to automatically log all hyperparameters, metrics, and model artifacts without modifying their training code. Which approach should they use?

Question 22hardmultiple choice

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A machine learning pipeline in Vertex AI produces a dataset artifact, a trained model, and evaluation metrics. The team wants to query the lineage to find all downstream artifacts that depend on a particular dataset. Which Vertex AI service should they use?

Question 23easymultiple choice

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A team of data scientists is collaborating on notebooks in Vertex AI Workbench. They need to use Git for version control and share notebooks with real-time editing. Which type of Workbench instance should they choose?

Question 24mediummultiple choice

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You are using DVC for data versioning in an ML project on Google Cloud. Your training data is stored in Cloud Storage. You want to track a new version of the dataset after preprocessing. Which DVC command should you use to register the changes?

Question 25mediummultiple choice

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An organisation uses Delta Lake on Dataproc to manage a data lake for ML training. They need ACID transactions for concurrent reads and writes. Which file format does Delta Lake use as the underlying storage?

Question 26hardmultiple choice

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You need to create a reproducible snapshot of a BigQuery table as of a specific timestamp for ML model training. The snapshot should be queryable without copying the entire dataset. Which BigQuery feature should you use?

Question 27easymultiple choice

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A team wants to enforce governance and compliance for all ML models across the organisation. They need a centralised repository that tracks model versions, deployment history, and evaluation metrics. Which service should they use?

Question 28mediummulti select

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A company uses Vertex AI Pipelines for ML workflows. They want to standardize pipeline templates across teams to ensure consistency. Which TWO approaches should they use?

Question 29hardmulti select

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A machine learning team needs to ensure that the same features used for training are used for serving in production to avoid training-serving skew. They use Vertex AI Feature Store. Which THREE actions should they take?

Question 30mediummulti select

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A data science team uses Vertex AI Experiments to compare multiple model training runs. They want to capture and compare hyperparameters, metrics, and code versions for each run. Which TWO steps should they take?

Question 31easymultiple choice

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A data scientist wants to track machine learning experiments, including parameters, metrics, and artifacts, and compare runs. Which Vertex AI service should they use?

Question 32mediummultiple choice

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A team uses Vertex AI Feature Store with an online store. They need low-latency serving for millions of features with high write throughput. Which online store type should they choose?

Question 33hardmultiple choice

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A company trains a model using features from Vertex AI Feature Store. They notice training-serving skew because the feature values used at training time differ from those served online. How should they address this?

Question 34mediummultiple choice

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A data science team wants to version control their datasets along with code using Git. They need a tool that integrates with Git and tracks changes to large data files. Which tool should they use?

Question 35easymultiple choice

Read the full Collaborating Within and Across Teams to Manage Data and Models explanation →

An ML engineer needs to deploy a model from Vertex AI Model Registry to an endpoint. The model has multiple versions. They want to designate one version as the 'champion' for production traffic. How should they do this?

Question 36mediummultiple choice

Read the full Collaborating Within and Across Teams to Manage Data and Models explanation →

A company uses Vertex AI Pipelines to train and deploy models. They want to automatically generate model documentation that includes model details, intended use, and evaluation results. What should they use?

Question 37mediummultiple choice

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A team wants to share feature definitions across multiple projects in their organization using Vertex AI Feature Store. What is the recommended approach?

Question 38easymultiple choice

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A data scientist is using Vertex AI Workbench notebooks and wants to collaborate with team members in real-time on the same notebook. Which notebook type supports real-time collaboration?

Question 39hardmultiple choice

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A team uses Vertex AI Metadata to track pipeline runs. They need to identify all artifacts that were generated by a particular pipeline execution. Which API method should they use?

Question 40mediummultiple choice

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A company uses Delta Lake on Dataproc for their data lake. They need to ensure ACID transactions and schema enforcement for data ingested from streaming sources. Which Delta Lake feature should they enable?

Question 41hardmultiple choice

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An organization needs to implement MLOps with standardized pipeline templates across multiple teams. Which Vertex AI feature should they use to create reusable pipeline components?

Question 42mediummultiple choice

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A data engineer wants to create a BigQuery table snapshot for point-in-time recovery of a critical dataset. The snapshot should be created daily and retained for 30 days. What should they use?

Question 43mediummulti select

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A company wants to monitor features in Vertex AI Feature Store for drift over time. Which two services should they use? (Choose two.)

Question 44hardmulti select

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A team uses Vertex AI Pipelines and wants to track lineage of artifacts and executions. Which three resources should they use? (Choose three.)

Question 45mediummulti select

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A company wants to implement a centralized model registry for governance. Which two features should they use? (Choose two.)

Question 46mediummultiple choice

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An ML team wants to share feature definitions across multiple projects to reduce training-serving skew and ensure consistency. They currently store features in Cloud Storage and manually coordinate updates, leading to errors. Which Google Cloud service should they use to centrally manage and serve features for both training and online inference?

Question 47hardmultiple choice

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A data scientist is training a model using Vertex AI Experiments and wants to automatically log model parameters, metrics, and artifacts without modifying their training script. Which approach should they use?

Question 48mediummultiple choice

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An ML engineer has a model trained in Vertex AI and wants to deploy it to an endpoint with autoscaling and traffic splitting for canary testing. They have the model artifact stored in Vertex AI Model Registry with alias 'champion'. What is the correct sequence of steps?

Question 49easymultiple choice

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A team wants to track the lineage of ML pipeline runs, including which datasets, parameters, and models were used in each execution. Which Vertex AI service should they use?

Question 50mediummultiple choice

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An organization uses Vertex AI Workbench user-managed notebooks and wants to enable collaboration where multiple data scientists can edit the same notebook simultaneously. Which configuration should they use?

Question 51hardmultiple choice

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A company uses BigQuery as their data warehouse. They want to version datasets for ML experiments and be able to query snapshots at specific points in time. Which approach is most cost-effective and requires minimal operational overhead?

Question 52easymultiple choice

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An ML team uses Vertex AI Pipelines and wants to automatically generate model cards documenting model purpose, evaluation results, and intended use. Which approach should they take?

Question 53mediummultiple choice

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A team is using Vertex AI Feature Store with an online store for low-latency serving. They notice increasing latency during peak hours. The feature data is updated frequently and requires strong consistency. Which online store type should they use?

Question 54mediummultiple choice

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A data scientist needs to retrieve training data from Vertex AI Feature Store that exactly matches the feature values as they were at a specific historical timestamp to avoid label leakage. Which feature view configuration should they use?

Question 55easymultiple choice

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An ML team wants to monitor feature drift in their production model. Which Vertex AI Feature Store capability should they use?

Question 56hardmultiple choice

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A company uses Vertex AI Pipelines to orchestrate ML workflows. After a pipeline run, they want to query the lineage of a particular model artifact to find out which dataset and hyperparameters were used to produce it. Which API method should they use?

Question 57mediummultiple choice

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A team is using Delta Lake on Dataproc for their data lake with ACID transactions. They want to version data for ML experiments and roll back to a previous version if needed. Which Delta Lake feature should they use?

Question 58mediummulti select

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An organization wants to implement central governance for ML models across teams. Which TWO services should they use together to achieve model versioning, lineage, and deployment management? (Select 2)

Question 59hardmulti select

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An ML team uses Vertex AI Workbench managed notebooks and wants to version their notebook code and collaborate using Git. Which THREE steps are required to set up Git integration? (Select 3)

Question 60easymulti select

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A company wants to use DVC for data versioning alongside their ML code in Git. Which TWO statements about DVC are correct? (Select 2)

Question 61mediummultiple choice

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A team is building a fraud detection model that requires joining real-time transaction features with historical user features. They need to ensure that the training data does not use future information (data leakage). Which Vertex AI Feature Store capability should they use?

Question 62easymultiple choice

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An ML team wants to automatically track training runs, including hyperparameters and metrics, with minimal code changes. Which Vertex AI service should they use?

Question 63mediummultiple choice

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A data science team needs to share features across multiple ML models while ensuring consistency between training and serving. Which approach best achieves this?

Question 64hardmultiple choice

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An ML engineer trained a model and registered it in Vertex AI Model Registry. They want to assign the alias 'champion' to the best-performing version for production deployment. Which gcloud command should they use?

Question 65easymultiple choice

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Which Vertex AI service is used to track the lineage of ML pipeline components, artefacts, and executions?

Question 66mediummultiple choice

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A team uses Vertex AI Workbench managed notebooks. They want to version control their notebook files and collaborate using Git. What is the best way to integrate Git?

Question 67mediummultiple choice

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An ML team wants to implement data versioning for large datasets stored in Google Cloud Storage. They need to track changes over time and reproduce previous data states. Which tool is most appropriate?

Question 68hardmultiple choice

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A company uses Vertex AI Feature Store with an online store for low-latency serving. They observe high latency during peak hours. The feature values are small (< 1 KB each) and the workload is read-heavy. Which change would most effectively reduce latency?

Question 69easymultiple choice

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What is the primary benefit of using a centralised model registry in MLOps?

Question 70mediummultiple choice

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An ML engineer needs to deploy a model to an endpoint and gradually shift traffic from the previous version (champion) to a new version (challenger) for A/B testing. How should they configure the endpoint?

Question 71hardmultiple choice

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A team wants to implement automated model documentation that captures training data, feature importance, evaluation metrics, and intended use. Which Vertex AI feature supports this?

Question 72mediummulti select

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A company is implementing MLOps with Vertex AI. They need to ensure that only approved models can be deployed to production. Which TWO practices should they adopt?

Question 73mediummulti select

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An ML team uses Delta Lake on Dataproc for data versioning. Which THREE benefits does Delta Lake provide?

Question 74hardmulti select

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A team wants to monitor features in Vertex AI Feature Store for drift. Which TWO configurations are required?

Question 75easymulti select

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Which THREE are valid uses of Vertex AI Metadata?

Question 76mediummultiple choice

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A data science team wants to share a set of engineered features across multiple projects and teams to reduce training-serving skew and ensure consistency. They need low-latency serving (single-digit milliseconds) for online predictions and also need to retrieve historical feature values for training. Which approach should they take?

Question 77hardmulti select

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A team is operationalizing a machine learning pipeline using Vertex AI. They want to automatically track experiment runs, log model parameters and metrics, and store model artifacts for reproducibility. They also need to capture lineage between pipeline components (e.g., which dataset and hyperparameter tuning job produced a model). Which TWO services should they use together to achieve this? (Choose two.)

Question 78mediummulti select

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A company is implementing MLOps on Google Cloud and needs to manage model versions, assign aliases (e.g., 'champion' for production, 'challenger' for staging), store evaluation metrics alongside each model version, and deploy models to endpoints. Which service should they use? (Choose THREE that are part of the solution.)

Question 79mediummultiple choice

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A team uses Vertex AI Workbench notebooks for collaborative model development. They want to ensure that code changes are version-controlled, that multiple data scientists can work on the same notebook without conflicts, and that the environment is reproducible across team members. Which approach should they take?