Build and operate the data processing systems behind analytics and ML on Google Cloud, including BigQuery, Dataflow, and Pub/Sub, and track your readiness for the Professional Data Engineer exam.
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Google Cloud feature
reservation
Topic in 1 exam domain
- reservation · Feature
- reservation · Concept
Also called: attached reservation, child reservations, compute reservation, compute reservations, dedicated reservation, existing reservations, reservation resource, reserved capacity, slot reservations
A reserved allocation of Compute Engine capacity within a project or block.
Where this appears on the exam
1 objective in 1 of 5 domains
~18% of exam
Related topics
FoundationsBuilding blocksBuilding blocksThis topicWorks alongsideinstance templateFeatureApplication Default CredentialsFeatureIdentity and Access ManagementServiceVertex AIServiceBigQueryServiceGoogle Cloud StorageServiceCloud Run functionsService
reservationFeature
DataflowServiceCloud SQLServicePoints to what builds on itWorks with or alternative toHover or tap a line to see what connects the two
- reservation works with Vertex AI From the official documentation: “You can modify the reservation to allow Vertex AI training or prediction jobs to use it.”
- instance template configures reservation From the official documentation: “`INSTANCE_TEMPLATE_NAME`: the name of the instance template that you want to use to create your attached reservation.”
- reservation is part of BigQuery From the official documentation: “Reservations in BigQuery offer enhanced control over how reservations are used and provide additional security features.”
- Vertex AI depends on Application Default Credentials From the official documentation: “To authenticate to Vertex AI, set up Application Default Credentials.”
- Application Default Credentials secures BigQuery From the official documentation: “To authenticate to BigQuery, set up Application Default Credentials.”
- BigQuery depends on Application Default Credentials From the official documentation: “To authenticate to BigQuery, set up Application Default Credentials.”
- Dataflow depends on Application Default Credentials From the official documentation: “To authenticate to Dataflow, set up Application Default Credentials.”
- Application Default Credentials works with BigQuery From the official documentation: “Create a BigQuery client using application default credentials.”
- Application Default Credentials secures Vertex AI From the official documentation: “To authenticate to Vertex AI, set up Application Default Credentials.”
- BigQuery works with Dataflow From the official documentation: “Read from BigQuery to Dataflow”
- BigQuery works with Cloud SQL From the official documentation: “Add credentials to connect BigQuery to Cloud SQL.”
- Application Default Credentials works with Dataflow From the official documentation: “To authenticate to Dataflow, set up Application Default Credentials.”
- Application Default Credentials works with Google Cloud Storage From the official documentation: “To authenticate to Cloud Storage, set up Application Default Credentials.”
- Cloud SQL works with Vertex AI From the official documentation: “This page describes how to integrate Cloud SQL with [Vertex AI](/vertex-ai/docs).”
- Application Default Credentials works with Cloud Run functions From the official documentation: “To authenticate to Cloud Run functions, set up Application Default Credentials.”
- Cloud Run functions depends on Application Default Credentials From the official documentation: “To authenticate to Cloud Run functions, set up Application Default Credentials.”
- BigQuery is an alternative to Google Cloud Storage From the official documentation: “The training data can be either a CSV file in Cloud Storage or a table in BigQuery.”
- BigQuery works with Cloud Run functions From the official documentation: “BigQuery supports external function calls through Cloud Run functions.”
- BigQuery works with Google Cloud Storage From the official documentation: “Load Cloud Storage data into BigQuery”
- Cloud SQL works with Identity and Access Management From the official documentation: “This page describes how Cloud SQL is integrated with IAM .”
- Application Default Credentials secures Dataflow From the official documentation: “To authenticate to Dataflow, set up Application Default Credentials.”
- Application Default Credentials secures Cloud Run functions From the official documentation: “To authenticate to Cloud Run functions, set up Application Default Credentials.”
- Application Default Credentials works with Identity and Access Management From the official documentation: “To authenticate to IAM, set up Application Default Credentials.”
- Identity and Access Management secures BigQuery From the official documentation: “Access to BigQuery data from AlloyDB is managed through IAM.”
- Application Default Credentials works with Vertex AI From the official documentation: “To authenticate to Vertex AI, set up Application Default Credentials.”
- BigQuery works with Identity and Access Management From the official documentation: “BigQuery uses Identity and Access Management (IAM) for authorization.”
- Cloud Run functions works with Google Cloud Storage From the official documentation: “Service account for Dataminr Alerts Cloud Run function to write alert data to GCS”
- Cloud SQL works with Google Cloud Storage From the official documentation: “Imports data into a Cloud SQL instance from a SQL dump or CSV file in Cloud Storage.”
- Google Cloud Storage depends on Application Default Credentials From the official documentation: “To authenticate to Cloud Storage, set up Application Default Credentials.”
- Identity and Access Management secures Vertex AI From the official documentation: “A Vertex AI model with an active endpoint that you have Identity and Access Management (IAM) permission to access.”
- BigQuery works with Vertex AI From the official documentation: “The page provides guidance for getting predictions from Vertex AI, based on retail [data exported into BigQuery](/retail/docs/export-data-into-bq).”
- Dataflow depends on Google Cloud Storage From the official documentation: “- A gcs bucket for staging DataFlow files. Replace `<BUCKET_NAME>`: the name of a valid Google Cloud Storage bucket.”
- Dataflow works with Google Cloud Storage From the official documentation: “Data is transferred between Spanner and Cloud Storage using Dataflow.”
- Identity and Access Management depends on Application Default Credentials From the official documentation: “To authenticate to IAM, set up Application Default Credentials.”
- Application Default Credentials configures BigQuery From the official documentation: “To authenticate to BigQuery, set up Application Default Credentials.”
- Application Default Credentials works with Cloud SQL From the official documentation: “Application Default Credentials are part of the client libraries you can use to access Cloud SQL.”
- Application Default Credentials secures Google Cloud Storage From the official documentation: “To authenticate to Cloud Storage, set up Application Default Credentials.”
- BigQuery is an alternative to Dataflow From the official documentation: “To automate the EDA process, use BigQuery or a managed data processing service like Dataflow.”
- Cloud Run functions works with Cloud SQL From the official documentation: “[Cloud Run functions](/functions) is an event-driven serverless compute platform that integrates with Cloud SQL.”
- Cloud Run functions works with Vertex AI From the official documentation: “- **Vertex AI custom model** node: Use Cloud Run functions to post-process prediction results from the original Vertex AI custom model.”
- Dataflow depends on Identity and Access Management From the official documentation: “Dataflow uses Identity and Access Management (IAM) for authorization.”
- Dataflow works with Vertex AI From the official documentation: “Vertex AI models are subject to [Rate Limits and Quotas](https://cloud.google.com/vertex-ai/generative-ai/docs/quotas#view-the-quotas-by-region-and-by-model) and Dataflow automatically retries throttled requests with exponential backoff.”
- Google Cloud Storage works with Vertex AI From the official documentation: “Google Cloud's scalable and secure object storage service, recommended for storing large datasets used in training and verification with Vertex AI for optimal performance.”
- Identity and Access Management secures Cloud Run functions From the official documentation: “For Cloud Run functions backend services, [Identity and Access Management (IAM)](https://cloud.google.com/iam/docs) is used to control the ability to view, create, update and delete functions.”
- Identity and Access Management secures Dataflow From the official documentation: “To control access to Dataflow resources for users, groups, and service accounts in a project or organization, use Identity and Access Management (IAM).”
- Identity and Access Management configures Google Cloud Storage From the official documentation: “Grant permission for the service account to list and view files in the bucket: ```bash gcloud storage buckets add-iam-policy-binding gs://${PROJECT_ID}-bucket/ \ --member=serviceAccount:sa-test-app-team-x@${PROJECT_ID}.iam.gserviceaccount.com \ --role=roles/storage.objectViewer…”
- Identity and Access Management secures Google Cloud Storage From the official documentation: “These modifications to that file define a second backup repository in a Cloud Storage bucket, accessed through Identity and Access Management (IAM).”
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