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 service
BigQuery ML
Topic in 8 exam domains
- BigQuery ML · Service
- BigQuery ML · Feature
Also called: BigQuery Machine Learning, BQML
A service enabling machine learning modeling directly within BigQuery.
Where this appears on the exam
6 objectives in 4 of 5 domains
~22% of exam
~25% of exam
~15% of exam
~18% of exam
Related topics
FoundationsBuilding blocksBuilding blocksBuilding blocksBuilding blocksThis topicAppliedApplication Default CredentialsFeatureVPC Service ControlsServiceCloud Key Management ServiceServiceCloud StorageServiceSensitive Data ProtectionServicecustomer-managed encryption keysFeatureManaged Service for Apache SparkServiceCloud LoggingServiceCloud SQLServiceBigQueryService
BigQuery MLService
automatic preprocessingFeaturePoints to what builds on itWorks with or alternative toHover or tap a line to see what connects the two
- BigQuery ML works with Cloud Storage From the official documentation: “You can export BigQuery ML models to Cloud Storage, and use them for online prediction, or edit them in Python.”
- BigQuery ML is part of BigQuery From the official documentation: “BigQuery ML lets you use SQL in BigQuery to perform machine learning (ML) and predictive analytics.”
- automatic preprocessing is part of BigQuery ML From the official documentation: “BigQuery ML performs automatic preprocessing during training.”
- BigQuery works with BigQuery ML From the official documentation: “BigQuery ML lets you build and operationalize machine learning (ML) models over data in BigQuery by using SQL.”
- BigQuery ML is an alternative to Managed Service for Apache Spark From the official documentation: “Use BigQuery ML, BigQuery DataFrames, or Managed Service for Apache Spark to perform distributed data processing on large datasets.”
- Cloud Key Management Service secures BigQuery ML From the official documentation: “Along with the default encryption provided by BigQuery, you can use your own Cloud Key Management Service keys for encrypting machine learning models, including imported TensorFlow models.”
- Application Default Credentials works with Cloud Storage From the official documentation: “To authenticate to Cloud Storage, set up Application Default Credentials.”
- Sensitive Data Protection depends on Application Default Credentials From the official documentation: “To authenticate to Sensitive Data Protection, set up Application Default Credentials.”
- Application Default Credentials secures BigQuery From the official documentation: “To authenticate to BigQuery, set up Application Default Credentials.”
- customer-managed encryption keys depends on Cloud Key Management Service From the official documentation: “These keys are created and managed using Cloud KMS.”
- BigQuery depends on Application Default Credentials From the official documentation: “To authenticate to BigQuery, set up Application Default Credentials.”
- BigQuery works with Sensitive Data Protection From the official documentation: “Uses the Data Loss Prevention API to analyze BigQuery data.”
- 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 Logging From the official documentation: “BigQuery utilizes Cloud Logging for some of your log data.”
- 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.”
- Application Default Credentials secures Sensitive Data Protection From the official documentation: “To authenticate to Sensitive Data Protection, set up Application Default Credentials.”
- BigQuery works with Cloud SQL From the official documentation: “Add credentials to connect BigQuery to Cloud SQL.”
- VPC Service Controls secures BigQuery From the official documentation: “BigQuery data is protected with VPC Service Controls”
- 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.”
- Cloud Logging works with customer-managed encryption keys From the official documentation: “To use Cloud Logging with Customer-managed encryption keys (CMEK), you must complete the steps in the [Enable CMEK for an organization](/logging/docs/routing/managed-encryption#enable) page in the Cloud Logging documentation.”
- 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 Key Management Service works with customer-managed encryption keys From the official documentation: “With CMEK, Cloud Key Management Service wraps your stored volume keys.”
- Application Default Credentials works with Sensitive Data Protection From the official documentation: “To authenticate to Sensitive Data Protection, set up Application Default Credentials.”
- Cloud Storage works with Sensitive Data Protection From the official documentation: “Uses the Data Loss Prevention API to analyze a file on GCS.”
- customer-managed encryption keys secures Cloud Storage From the official documentation: “CMEKs encrypt data at rest in Cloud SQL, Cloud Storage, and within GKE clusters.”
- BigQuery works with Managed Service for Apache Spark From the official documentation: “This means you can quickly read and write BigQuery data to and from Managed Service for Apache Spark.”
- Cloud Logging works with Cloud SQL From the official documentation: “Cloud SQL uses Cloud Logging.”
- customer-managed encryption keys secures BigQuery From the official documentation: “You can either create CMEKs manually to protect your BigQuery resources or use Cloud KMS Autokey.”
- customer-managed encryption keys secures Cloud SQL From the official documentation: “Enable CMEK for Cloud SQL Databases”
- Cloud Storage works with customer-managed encryption keys From the official documentation: “Cloud Storage supports CMEK for routing logs.”
- VPC Service Controls secures Cloud Storage From the official documentation: “Storage Transfer Service supports transfers to Cloud Storage buckets protected by VPC Service Controls.”
- Application Default Credentials works with Cloud Key Management Service From the official documentation: “To authenticate to Cloud KMS, set up Application Default Credentials.”
- BigQuery is an alternative to Managed Service for Apache Spark From the official documentation: “Run queries against your federated data using BigQuery or Managed Service for Apache Spark.”
- Cloud Logging depends on Application Default Credentials From the official documentation: “To authenticate to Logging, set up Application Default Credentials.”
- Cloud Logging works with Cloud Storage From the official documentation: “Cloud Storage writes various log entries to Cloud Logging in the Google Cloud console.”
- Cloud SQL works with Sensitive Data Protection From the official documentation: “Sensitive Data Protection starts identifying your Cloud SQL instances and creating a default connection for each instance.”
- Cloud Storage works with Managed Service for Apache Spark From the official documentation: “Managed Service for Apache Spark accesses these files on Cloud Storage.”
- Application Default Credentials works with Managed Service for Apache Spark From the official documentation: “To authenticate to Managed Service for Apache Spark, set up Application Default Credentials.”
- Application Default Credentials secures Managed Service for Apache Spark From the official documentation: “To authenticate to Managed Service for Apache Spark, set up Application Default Credentials.”
- BigQuery works with Cloud Key Management Service From the official documentation: “Change the Cloud KMS key for a BigQuery table”
- BigQuery depends on Cloud Logging From the official documentation: “BigQuery utilizes Cloud Logging for some of your log data.”
- Cloud Key Management Service works with Cloud SQL From the official documentation: “Cloud KMS integrates with Cloud SQL.”
- customer-managed encryption keys is part of Cloud Key Management Service From the official documentation: “The Cloud KMS keys that you create are customer-managed keys.”
- Managed Service for Apache Spark depends on Cloud Storage From the official documentation: “Managed Service for Apache Spark uses Cloud Storage as its default distributed file system.”
- VPC Service Controls secures Cloud SQL From the official documentation: “VPC Service Controls perimeters protect the Cloud SQL Admin API.”
- Application Default Credentials configures Sensitive Data Protection From the official documentation: “To authenticate to Sensitive Data Protection, set up Application Default Credentials.”
- Cloud Key Management Service depends on Application Default Credentials From the official documentation: “To authenticate to Cloud KMS, set up Application Default Credentials.”
- Cloud Key Management Service secures BigQuery From the official documentation: “You can encrypt data at the dataset or project level with the default customer-managed Cloud Key Management Service keys in BigQuery.”
- Cloud Key Management Service is an alternative to customer-managed encryption keys From the official documentation: “Unless your control package mandates a certain encryption key strategy, you can use any Google key management service, including Cloud Key Management Service, Cloud External Key Manager, or CMEK.”
- customer-managed encryption keys secures Cloud Logging From the official documentation: “Cloud Logging supports encrypting Log storage with CMEK keys.”
- customer-managed encryption keys is part of Cloud Storage From the official documentation: “[Cloud Storage](/storage/docs/introduction) provides features such as customer-managed encryption, lifecycle management, and aging and retention policies.”
- customer-managed encryption keys secures Managed Service for Apache Spark From the official documentation: “For data encryption at rest, use the [Cloud Key Management Service](/kms/docs/key-management-service) (KMS) or [Customer Managed Encryption Keys](/managed-spark/docs/concepts/configuring-clusters/customer-managed-encryption) (CMEK).”
- Managed Service for Apache Spark depends on Application Default Credentials From the official documentation: “To authenticate to Managed Service for Apache Spark, set up Application Default Credentials.”
- Sensitive Data Protection works with VPC Service Controls From the official documentation: “For information about VPC Service Controls support for Sensitive Data Protection, see the [Supported products](/vpc-service-controls/docs/supported-products#table_dlp) table in the VPC Service Controls documentation.”
- Application Default Credentials configures BigQuery From the official documentation: “To authenticate to BigQuery, set up Application Default Credentials.”
- Application Default Credentials works with Cloud Logging From the official documentation: “To authenticate to Logging, 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).”
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