Professional-Data-Engineer 無料問題集「Google Certified Professional Data Engineer」

（A）Automate intents in places where common words such as "payment" appear only once so the software isn't confused

（B）Automate a blend of the shortest and longest intents to be representative of all intents

（C）Automate the 10 intents that cover 70% of the requests so that live agents can handle more complicated requests

（D）Automate the more complicated requests first because those require more of the agents' time

（A）Use Dialogflow to implement the chatbot. defining the intents based on the most common queries collected.

（B）Use the Speech-to-Text API to build a Python application in a Compute Engine instance.

（C）Use Dialogflow for simple queries and the Speech-to-Text API for complex queries.

（D）Use the Speech-to-Text API to build a Python application in App Engine.

（A）master key

（B）row key

（C）unique key

（D）primary key

（A）Increase the size of the training dataset, and increase the number of input features.

（B）Reduce the size of the training dataset, and decrease the number of input features.

（C）Reduce the size of the training dataset, and increase the number of input features.

（D）Increase the size of the training dataset, and decrease the number of input features.

（A）Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.

（B）Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.

（C）Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.

（D）Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.

（A）Use Cloud Monitoring to view BigQuery metrics and set up alerts that let you know when a certain percentage of slots were used.

（B）Use slot reservations for your project to ensure that you have enough query processing capacity and are able to allocate available slots to the slower queries.

（C）Use available administrative resource charts to determine how slots are being used and how jobs are performing over time. Run a query on the INFORMATION_SCHEMA to review query performance.

（D）Use Cloud Logging to determine if any users or downstream consumers are changing or deleting access grants on tagged resources.

（A）Write a shell script to migrate the Redis data, and create a new Memorystore for Redis instance.

（B）Create a Dataflow job to road the Redis database from the on-premises data center. and write the data to a Memorystore for Redis instance

（C）Make a secondary instance of the Redis database on a Compute Engine instance, and then perform a live cutover.

（D）Make an RDB backup of the Redis database, use the gsutil utility to copy the RDB file into a Cloud Storage bucket, and then import the RDB tile into the Memorystore for Redis instance.

（A）Use Dataflow for your workflow pipelines. Use Cloud Run triggers for scheduling.

（B）Use Cloud Composer in a Shared VPC configuration. Place the Cloud Composer resources in the service project.

（C）Use Cloud Composer in a Shared VPC configuration. Place the Cloud Composer resources in the host project.

（D）Use Dataflow for your workflow pipelines. Use shell scripts to schedule workflows.

（A）Trigger based on element count

（B）Trigger that is a combination of other triggers

（C）Trigger based on element size in bytes

（D）Trigger based on time

（A）Create a dummy user and grant dataset access to that user. Store the username and password for that user in a file on the files system, and use those credentials to access the BigQuery dataset

（B）Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request

（C）Create a service account and grant dataset access to that account. Use the service account's private key to access the dataset

（D）Create groups for your users and give those groups access to the dataset

（A）BigQuery cannot be accessed from local machines

（B）You are missing gcloud on your machine

（C）Pipelines cannot be run locally

（D）Your gcloud does not have access to the BigQuery resources

（A）Migrate the Hive partitioned data objects to a multi-region Cloud Storage bucket.

（B）Upgrade the external table to a BigLake table Enable metadata caching for the table.

（C）Change the storage class of the Hive partitioned data objects from Coldline to Standard.

（D）Create an individual external table for each Hive partition by using a common table name prefix Use wildcard table queries to reference the partitioned data.

（A）500 TB

（B）1 TB

（C）1 GB

（D）500 GB

（A）Implement BigQuery with no region selected for storage or processing.

（B）Implement Cloud Spanner with the leader in North America and read-only replicas in Asia and Europe.

（C）Implement Cloud Bigtable with the primary cluster in North America and secondary clusters in Asia and Europe.

（D）Implement Cloud SQL for PostgreSQL with the master in Norht America and read replicas in Asia and Europe.