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.
Advanced
Prepare and test your skills
Advanced
Prepare and test your skills
Google Cloud service
Vertex AI TensorBoard
Topic in 3 exam domains
- Vertex AI TensorBoard · Service · Google Cloud ML visualization service
- Vertex AI TensorBoard · Feature
Also called: managed TensorBoard, TensorBoard, TensorBoard web app, Vertex AI TensorBoard web app, Vertex TensorBoard
An enterprise-ready managed TensorBoard service for tracking, visualizing, and monitoring machine learning experiments and training jobs.
Where this appears on the exam
2 objectives in 2 of 5 domains
Related topics
FoundationsBuilding blocksBuilding blocksThis topicCMEKFeatureGoogle Cloud consoleFeatureApplication Default CredentialsFeatureCloud ProfilerServiceGoogle Cloud StorageServiceIdentity and Access ManagementServiceCompute EngineServiceCloud StorageServiceCloud SQLServiceDataflowServiceBigQueryService
Vertex AI TensorBoardService
Points to what builds on itWorks with or alternative toHover or tap a line to see what connects the two
- Vertex AI TensorBoard depends on Cloud Storage From the official documentation: “You must have a Cloud Storage bucket to store Vertex AI TensorBoard logs.”
- Cloud Profiler works with Vertex AI TensorBoard From the official documentation: “Once you have configured your training script to enable Profiler, [run the training script with a Vertex AI TensorBoard instance](/gemini-enterprise-agent-platform/machine-learning/experiments/tensorboard-training#create_a_custom_training_job).”
- CMEK secures Vertex AI TensorBoard From the official documentation: “Note: If CMEK (encryption keys) need to be associated with the TensorBoard instance, then `experiment_tensorboard` is no longer optional.”
- Google Cloud console configures Vertex AI TensorBoard From the official documentation: “The Google Cloud console is used to: - create or delete Vertex AI TensorBoard instances,”
- Vertex AI TensorBoard depends on Google Cloud Storage From the official documentation: “A Cloud Storage bucket is required to store the Vertex AI TensorBoard logs your training script generates.”
- Application Default Credentials works with Cloud Storage From the official documentation: “To authenticate to Cloud Storage, 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.”
- BigQuery is an alternative to Cloud Storage From the official documentation: “The `import_result_sink` must be a Cloud Storage path or a BigQuery table.”
- BigQuery works with Cloud Storage From the official documentation: “External BigQuery data source backed by Cloud Storage”
- Application Default Credentials works with BigQuery From the official documentation: “Create a BigQuery client using 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 Compute Engine From the official documentation: “To authenticate to Compute Engine, set up Application Default Credentials.”
- Application Default Credentials secures Cloud Storage From the official documentation: “To authenticate to Cloud Storage, set up Application Default Credentials.”
- Cloud Storage depends on Application Default Credentials From the official documentation: “To authenticate to Cloud Storage, 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 Cloud Storage From the official documentation: “When ingesting data from Cloud SQL, data is first staged to a Cloud Storage bucket.”
- Cloud SQL works with Compute Engine From the official documentation: “Create a Compute Engine VM Instance to host a sample web app that connects to Cloud SQL.”
- Application Default Credentials secures Compute Engine From the official documentation: “To authenticate to Compute Engine, 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 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 .”
- Cloud Storage works with Dataflow From the official documentation: “Write from Dataflow to Cloud Storage”
- CMEK secures Cloud SQL From the official documentation: “Enable CMEK for Cloud SQL Databases”
- Cloud SQL depends on Compute Engine From the official documentation: “Cloud SQL uses Compute Engine virtual machines (VMs) with persistent storage disks.”
- CMEK secures BigQuery From the official documentation: “Enable CMEK for BigQuery Tables”
- Application Default Credentials secures Dataflow From the official documentation: “To authenticate to Dataflow, set up Application Default Credentials.”
- Cloud Storage works with Identity and Access Management From the official documentation: “To learn how to use IAM with Cloud Storage, see [Using IAM Permissions](/storage/docs/access-control/using-iam-permissions).”
- CMEK secures Cloud Storage From the official documentation: “Enable CMEK for Cloud Storage Buckets”
- Application Default Credentials works with Identity and Access Management From the official documentation: “To authenticate to IAM, set up Application Default Credentials.”
- Cloud Profiler depends on Application Default Credentials From the official documentation: “To authenticate to Profiler, 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.”
- Identity and Access Management secures Cloud Storage From the official documentation: “Cloud Storage uses Identity and Access Management (IAM) for authorization.”
- BigQuery works with Identity and Access Management From the official documentation: “BigQuery uses Identity and Access Management (IAM) for authorization.”
- 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.”
- Cloud Storage works with Compute Engine From the official documentation: “You can use a web browser, the Google Cloud CLI, or a programming language to transfer files between Cloud Storage and Compute Engine VMs.”
- Compute Engine works with Google Cloud Storage From the official documentation: “The usage export bucket is a Google Cloud Storage bucket where Compute Engine usage reports are stored.”
- Google Cloud Storage depends on Application Default Credentials From the official documentation: “To authenticate to Cloud Storage, set up Application Default Credentials.”
- Cloud Profiler works with Compute Engine From the official documentation: “Shows how to initialize Cloud Profiler with Python on Google Compute Engine or Google Kubernetes Engine.”
- CMEK secures Compute Engine From the official documentation: “Enable CMEK on Compute Engine Persistent Disks”
- CMEK works with Dataflow From the official documentation: “The region for your CMEK and the [region](/dataflow/docs/resources/locations) for your Dataflow job must be the same.”
- Dataflow depends on Cloud Storage From the official documentation: “Begin by creating a Cloud Storage bucket using the Google Cloud console or Google Cloud CLI. The Dataflow pipeline uses this bucket as a temporary storage location.”
- Dataflow depends on Compute Engine From the official documentation: “When you run your pipeline on the Dataflow service, Dataflow creates Compute Engine instances to run your pipeline code.”
- 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.”
- Identity and Access Management secures Compute Engine From the official documentation: “Compute Engine uses Identity and Access Management (IAM) for authorization.”
- 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 configures Cloud Storage From the official documentation: “To authenticate to Cloud Storage, set up Application Default Credentials. For more information, see [Set up authentication for client libraries](/storage/docs/authentication#client-libs).”
- Application Default Credentials configures Compute Engine From the official documentation: “To authenticate to Compute Engine, set up Application Default Credentials.”
- Application Default Credentials secures Google Cloud Storage From the official documentation: “To authenticate to Cloud Storage, set up Application Default Credentials.”
- BigQuery depends on Cloud Storage From the official documentation: “BigQuery relies on Cloud Storage for staging file data and supports reading from and writing to several other sources and Google Cloud services.”
- BigQuery works with Compute Engine From the official documentation: “Compute Engine integrates with Google Cloud technologies such as Cloud Storage, Google Kubernetes Engine, and BigQuery, to extend beyond the basic computational capability to create more complex and sophisticated applications.”
- 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 Profiler works with Dataflow From the official documentation: “To troubleshoot or monitor pipeline performance, use Dataflow integration with Cloud Profiler to identify the parts of the pipeline code consuming the most resources.”
- Cloud SQL depends on Cloud Storage From the official documentation: “When ingesting data from Cloud SQL, data is first staged to a Cloud Storage bucket.”
How learners score on this topic
Last updated