GCDS syncs user accounts and some other LDAP attributes but not the passwords, with hybrid connectivity to GCP, SAML (or federation) is the preferred method.

Answer should be "B"

https://cloud.google.com/solutions/patterns-for-authenticating-corporate-users-in-a-hybrid-environment

https://cloud.google.com/architecture/identity/federating-gcp-with-active-directory-synchronizing-user-accounts#deciding_what_to_provision

All four options can accomplish what the question asks, in regards to batching and streaming processes. "A" is for Apache Spark and Hadoop, a juggernaut in speed of data processing. "B" is Google's best attempt at TIBCO, Ab Initio, and other processing technology, built explicity for visualizing batch operations and streams without through various labeled circuit boards. "C" and "D" are used within "A" and "B" and would require more work and higher risk.

But Google wants you to select "B"

=> B is correct answer

A (Incorrect): Problem began with new version.

B (Incorrect): Problem is about latency - not traffic restriction.

D (Incorrect): CI/CD Pipeline encourages easy rollbacks and troubleshooting in Dev/Test environment.

C is the most appropriate way to diagnose and troubleshoot.

A is the correct answer because the question says "with minimum downtime"

https://cloud.google.com/compute/docs/disks/working-with-persistent-disks#resize_pd

Correct Answer is (A):

Tokenization, when applied to data security, is the process of substituting a sensitive data element with a non-sensitive equivalent, referred to as a token, that has no extrinsic or exploitable meaning or value. The token is a reference that maps back to the sensitive data through a tokenization system.

Data is in IoT Nature and Big Table is the right choice. Cloud SQL is mainly for OLTP (Transactional, CRUD) not for taking and storing streaming data. It does not have the scalability and elasticity to absorb this amount of data in real-time.

SO the answer is without any doubt Big Table.

=> B is correct answer.

A is not reasonable because life cycle policies are not written in XML.

B is reasonable and is cloud native.

C requires a cron script which needs something to run the script and is a non-cloud native approach.

D requires a cron script which needs something to run the script and is a non-cloud native approach.

=> B is correct answer.

B is correct answer.

Dataproc is a managed Spark and Hadoop service that lets you take advantage of open source data tools for batch processing, querying, streaming, and machine learning. Dataproc automation helps you create clusters quickly, manage them easily, and save money by turning clusters off when you don't need them. With less time and money spent on administration, you can focus on your jobs and your data.

https://cloud.google.com/dataproc/docs/concepts/overview#:~:text=Dataproc%20is%20a%20managed%20Spark,%2C%20streaming%2C%20and%20machine%20learning.&text=With%20less%20time%20and%20money,your%20jobs%20and%20your%20data.

Answer is C.

A is NOT correct because increasing the memory size requires a VM restart.

B is NOT correct because the DB administration team is requesting help with their MySQL instance. Migration to a different product should not be the solution when other optimization techniques can still be applied first.

C is CORRECT because persistent disk performance is based on the total persistent disk capacity attached to an instance and the number of vCPUs that the instance has. Incrementing the persistent disk capacity will increment its throughput and IOPS, which in turn improve the performance of MySQL.

D is NOT correct because the DB administration team is requesting help with their MySQL instance. Migration to a different product should not be the solution when other optimization techniques can still be applied first.

C is correct.

A. Big Query Seems good. But keyword 'IOT' is here.

B. Google Cloud SQL doesn't work this case.

D. Google Cloud Storage doesn't support realtime analytics.