You are integrating one of your internal IT applications and Google BigQuery, so users can query BigQuery from the application's interface. You do not want individual users to authenticate to BigQuery and you do not want to give them access to the dataset. You need to securely access BigQuery from your IT application. What should you do?

• A.Create groups for your users and give those groups access to the dataset
• B.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
• C.Integrate with a single sign-on (SSO) platform, and pass each user's credentials along with the query request
• D.Create a service account and grant dataset access to that account. Use the service account's private key to access the dataset

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Which of the following is not true about Dataflow pipelines?

• A.Pipelines are a set of operations
• B.Pipelines represent a data processing job
• C.Pipelines represent a directed graph of steps
• D.Pipelines can share data between instances

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Your company is using WILDCARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:
# Syntax error : Expected end of statement but got "-" at [4:11]
SELECT age
FROM
bigquery-public-data.noaa_gsod.gsod
WHERE
age != 99
AND_TABLE_SUFFIX = '1929'
ORDER BY
age DESC
Which table name will make the SQL statement work correctly?

• A.'bigquery-public-data.noaa_gsod.gsod'
• B.'bigquery-public-data.noaa_gsod.gsod'*
• C.bigquery-public-data.noaa_gsod.gsod*
• D.'bigquery-public-data.noaa_gsod.gsod*`

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Your company handles data processing for a number of different clients. Each client prefers to use their own suite of analytics tools, with some allowing direct query access via Google BigQuery. You need to secure the data so that clients cannot see each other's data. You want to ensure appropriate access to the data. Which three steps should you take? (Choose three.)

• A.Only allow a service account to access the datasets.
• B.Load data into different partitions.
• C.Load data into a different dataset for each client.
• D.Put each client's BigQuery dataset into a different table.
• E.Use the appropriate identity and access management (IAM) roles for each client's users.
• F.Restrict a client's dataset to approved users.

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You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud.
You want to support transactions that scale horizontally. You also want to optimize data for range queries on non-key columns. What should you do?

• A.Use Cloud SQL for storage. Add secondary indexes to support query patterns.
• B.Use Cloud Spanner for storage. Add secondary indexes to support query patterns.
• C.Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.
• D.Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.

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