PDE-6

You'd like to move your Teradata data warehouse to BigQuery. You're trying to move historical data to BigQuery by using the most efficient method, which requires the least programming, but local storage space in your existing data warehouse is limited. What are you supposed to do?

• Use BigQuery Data Transfer Service by using the Java Database Connectivity (JDBC) driver with FastExport connection

• Create a Teradata Parallel Transporter (TPT) export script to export the historical data, and import to BigQuery by using the bq commandline tool.

• Use BigQuery Data Transfer Service with the Teradata Parallel Transporter (TPT) tbuild utility.

• Create a script to export the historical data, and upload in batches to Cloud Storage. Set up a BigQuery Data Transfer Service instance from

  Cloud Storage to BigQuery

You're part of the Data Governance Team and you're implementing security requirements. By using an encryption key controlled by your team, all of your data in BigQuery needs to be encrypted. Only on your premises will you be able to implement a mechanism for generating and storing encryption material. Hardware security module HSM. Google's managed solutions are what you need to rely on. What are you supposed to do?

• Create the encryption key in the on-premises HSM, and import it into a Cloud Key Management Service (Cloud KMS) key. Associate the

  created Cloud KMS key while creating the BigQuery resources.

• Create the encryption key in the on-premises HSM and link it to a Cloud External Key Manager (Cloud EKM) key. Associate the created

  Cloud KMS key while creating the BigQuery resources

• Create the encryption key in the on-premises HSM, and import it into Cloud Key Management Service (Cloud HSM) key. Associate the

  created Cloud HSM key while creating the BigQuery resources.

• Create the encryption key in the on-premises HSM. Create BigQuery resources and encrypt data while ingesting them into BigQuery.

You're maintaining the ETL pipeline. You're noticing that it takes a long time for Incoming Data to be processed by the streaming pipeline on Dataflow, which is causing delays in output. You've also noticed that Dataflow has automatically optimized the pipeline graph, merging it into a single step. You'd like to see where the possible bottleneck is taking place. What are you supposed to do?

• Insert a Reshuffle operation after each processing step, and monitor the execution details in the Dataflow console

• Insert output sinks after each key processing step, and observe the writing throughput of each block.

• Log debug information in each ParDo function, and analyze the logs at execution time.

• Verify that the Dataflow service accounts have appropriate permissions to write the processed data to the output sinks.

You're running a BigQuery project in an ondemand billing model and are using the ChangeDataCaptureCDC process to capture data. Every 10 minutes, the CDC process loads 1 GB of data into a temporary table, which is then merged to a 10 TB target table. This process is You're scanning very hard and you want to explore options for enabling a predictable cost model. . You need to create a BigQuery reservation based on

utilization information gathered from BigQuery Monitoring and apply the reservation to the CDC process. What should you do?

• Create a BigQuery reservation for the dataset.

• Create a BigQuery reservation for the job.

• Create a BigQuery reservation for the service account running the job.

• Create a BigQuery reservation for the project.

To store data in a regional BigQuery database, you are developing a fault tolerant architecture. In order to recover from a corruption event in your tables that occurred within the last 7 days, you must ensure that your application is able to do so. You want to adopt managed services with the lowest RPO

and most cost-effective solution. What are you supposed to do?

• Access historical data by using time travel in BigQuery.

• Export the data from BigQuery into a new table that excludes the corrupted data

• Create a BigQuery table snapshot on a daily basis.

• Migrate your data to multi-region BigQuery buckets.

You're building a network of Dataflows that will capture noise level data from hundreds of sensors installed near construction sites in the city. Every ten seconds, the sensors are measuring noise level and transmitting that information to the pipeline when levels exceed 70dBA. You need to detect

the average noise level from a sensor when data is received for a duration of more than 30 minutes, but the window ends when no data has been received for 15 minutes. What are you supposed to do?

• Use session windows with a 15-minute gap duration.

• Use session windows with a 30-minute gap duration.

• Use hopping windows with a 15-minute window, and a thirty-minute period.

• Use tumbling windows with a 15-minute window and a fifteen-minute .withAllowedLateness operator.

You're building a data model for BigQuery, which stores the Retail Transaction Data. There is a tightly coupled, immutable relationship between your two largest tables, sales_transaction_header and sales_transaction_line. After loading, these tables are rarely modified and are frequently joined when queried. In order to improve the performance of data analysis queries, you must model the sales_transaction_header and sales_transaction_line tables. What are you supposed to do?

• Create a sales_transaction table that holds the sales_transaction_header information as rows and the sales_transaction_line rows as

  nested and repeated fields.

• Create a sales_transaction table that holds the sales_transaction_header and sales_transaction_line information as rows, duplicating the

  sales_transaction_header data for each line.

• Create a sales_transaction table that stores the sales_transaction_header and sales_transaction_line data as a JSON data type.

• Create separate sales_transaction_header and sales_transaction_line tables and, when querying, specify the sales_transaction_line first in

  the WHERE clause.

You created a new version of a Dataflow streaming data ingestion pipeline that reads from Pub/Sub and writes to BigQuery. A processing period of 5 minutes is used for the previous version of the pipeline that runs in production. Without losing any data, creating inconsistencies, or increasing the processing time by more than 10 minutes, you must install the new version of the pipeline. What are you supposed to do?

• Update the old pipeline with the new pipeline code.

• Snapshot the old pipeline, stop the old pipeline, and then start the new pipeline from the snapshot.

• Drain the old pipeline, then start the new pipeline.

• Cancel the old pipeline, then start the new pipeline.

Your organization's data assets are stored in BigQuery, Pub/Sub, and a PostgreSQL instance running on Compute Engine. Because there are

multiple domains and diverse teams using the data, teams in your organization are unable to discover existing data assets. You need to design a

solution to improve data discoverability while keeping development and configuration efforts to a minimum. What should you do?

• Use Data Catalog to automatically catalog BigQuery datasets. Use Data Catalog APIs to manually catalog Pub/Sub topics and PostgreSQL

  tables.

• Use Data Catalog to automatically catalog BigQuery datasets and Pub/Sub topics. Use Data Catalog APIs to manually catalog PostgreSQL

  tables.

• Use Data Catalog to automatically catalog BigQuery datasets and Pub/Sub topics. Use custom connectors to manually catalog PostgreSQL

  tables.

• Use customer connectors to manually catalog BigQuery datasets, Pub/Sub topics, and PostgreSQL tables.

The SQL pipeline must be created. Every two hours, the pipeline will run an aggregate SQL transformation on a BigQuery table and load this result into another database. If there are errors, you must configure the pipeline so that it can retry. After three consecutive failures, you want the pipeline to send an email alert. What are you supposed to do?

• Use the BigQueryUpsertTableOperator in Cloud Composer, set the retry parameter to three, and set the email_on_failure parameter to true.

• Use the BigQueryInsertJobOperator in Cloud Composer, set the retry parameter to three, and set the email_on_failure parameter to true

• Create a BigQuery scheduled query to run the SQL transformation with schedule options that repeats every two hours, and enable email

  notifications.

• Create a BigQuery scheduled query to run the SQL transformation with schedule options that repeats every two hours, and enable

  notification to Pub/Sub topic. Use Pub/Sub and Cloud Functions to send an email after three failed executions