| Question.31 You need to set access to BigQuery for different departments within your company. Your solution should comply with the following requirements: – Each department should have access only to their data. – Each department will have one or more leads who need to be able to create and update tables and provide them to their team. – Each department has data analysts who need to be able to query but not modify data. How should you set access to the data in BigQuery? (A) Create a dataset for each department. Assign the department leads the role of OWNER, and assign the data analysts the role of WRITER on their dataset. (B) Create a dataset for each department. Assign the department leads the role of WRITER, and assign the data analysts the role of READER on their dataset. (C) Create a table for each department. Assign the department leads the role of Owner, and assign the data analysts the role of Editor on the project the table is in. (D) Create a table for each department. Assign the department leads the role of Editor, and assign the data analysts the role of Viewer on the project the table is in. |
31. Click here to View Answer
Answer is (B) Create a dataset for each department. Assign the department leads the role of WRITER, and assign the data analysts the role of READER on their dataset.
The permissions are required at dataset levels hence READER, WRITER & OWNER which are the primitive roles for dataset to be used.
Reference:
https://cloud.google.com/bigquery/docs/access-control-primitive-roles#dataset-primitive-roles
| Question.32 You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time. Which two methods can accomplish this (Choose two.)? (A) Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class. (B) Use managed export, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export. (C) Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files. (D) Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column. (E) Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories. |
32. Click here to View Answer
Answers are;
(A) Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.
(B) Use managed export, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
Option A; Cheap storage and it is a supported meathod
https://cloud.google.com/datastore/docs/export-import-entities
Option B; Data exported from one Datastore mode database can be imported into another Datastore mode database, even one in another project.
https://cloud.google.com/datastore/docs/export-import-entities
| Question.33 You are designing a cloud-native historical data processing system to meet the following conditions: – The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine. – A streaming data pipeline stores new data daily. – Peformance is not a factor in the solution. – The solution design should maximize availability. How should you design data storage for this solution? (A) Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed. (B) Store the data in BigQuery. Access the data using the BigQuery Connector on Cloud Dataproc and Compute Engine. (C) Store the data in a regional Cloud Storage bucket. Access the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine. (D) Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine. |
33. Click here to View Answer
Answer is (D) Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.
Multi-region increases high availability and pdf can be stored in gcs
| Question.34 Your United States-based company has created an application for assessing and responding to user actions. The primary table’s data volume grows by 250,000 records per second. Many third parties use your application’s APIs to build the functionality into their own frontend applications. Your application’s APIs should comply with the following requirements: – Single global endpoint – ANSI SQL support – Consistent access to the most up-to-date data What should you do? (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 SQL for PostgreSQL with the master in Norht America and read replicas in Asia and Europe. (D) Implement Cloud Bigtable with the primary cluster in North America and secondary clusters in Asia and Europe. |
34. Click here to View Answer
Answer is (B) Implement Cloud Spanner with the leader in North America and read-only replicas in Asia and Europe.
Cloud Spanner has three types of replicas: read-write replicas, read-only replicas, and witness replicas. Bigquery cannot support 250K data ingestion/second , as ANSI SQL support is required , no other options left except Spanner.
| Question.35 You are building an application to share financial market data with consumers, who will receive data feeds. Data is collected from the markets in real time. Consumers will receive the data in the following ways: – Real-time event stream – ANSI SQL access to real-time stream and historical data – Batch historical exports Which solution should you use? (A) Cloud Dataflow, Cloud SQL, Cloud Spanner (B) Cloud Pub/Sub, Cloud Storage, BigQuery (C) Cloud Dataproc, Cloud Dataflow, BigQuery (D) Cloud Pub/Sub, Cloud Dataproc, Cloud SQL |
35. Click here to View Answer
Answer is (B) Cloud Pub/Sub, Cloud Storage, BigQuery
It says the data is collected from the market, and the problem is that the methods are defined as requirements. Therefore, a close answer is B.
Real-time Event Stream: Cloud Pub/Sub is a managed messaging service that can handle real-time event streams efficiently. You can use Pub/Sub to ingest and publish real-time market data to consumers.
ANSI SQL Access: BigQuery supports ANSI SQL queries, making it suitable for both real-time and historical data analysis. You can stream data into BigQuery tables from Pub/Sub and provide ANSI SQL access to consumers.
Batch Historical Exports: Cloud Storage can be used for batch historical exports. You can export data from BigQuery to Cloud Storage in batch, making it available for consumers to download.
Reference:
https://cloud.google.com/solutions/processing-logs-at-scale-using-dataflow?hl=ja
https://cloud.google.com/bigquery/docs/write-api#:~:text=You%20can%20use%20the%20Storage,in%20a%20single%20atomic%20operation.