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NEW QUESTION 23
You need to ensure reliability for your application and operations by supporting reliable task a scheduling for compute on GCP. Leveraging Google best practices, what should you do?
- A. Using the Cron service provided by App Engine, publish messages to a Cloud Pub/Sub topic. Subscribe to that topic using a message-processing utility service running on Compute Engine instances.
- B. Using the Cron service provided by App Engine, publishing messages directly to a message-processing utility service running on Compute Engine instances.
- C. Using the Cron service provided by Google Kubernetes Engine (GKE), publish messages directly to a message-processing utility service running on Compute Engine instances.
- D. Using the Cron service provided by GKE, publish messages to a Cloud Pub/Sub topic. Subscribe to that topic using a message-processing utility service running on Compute Engine instances.
Answer: A
Explanation:
Explanation
https://cloud.google.com/solutions/reliable-task-scheduling-compute-engine
NEW QUESTION 24
For this question, refer to the Helicopter Racing League (HRL) case study. HRL is looking for a cost-effective approach for storing their race data such as telemetry. They want to keep all historical records, train models using only the previous season’s data, and plan for data growth in terms of volume and information collected.
You need to propose a data solution. Considering HRL business requirements and the goals expressed by CEO S. Hawke, what should you do?
- A. Use Cloud SQL for its ability to automatically manage storage increases and compatibility with MySQL. Use separate database instances for each season.
- B. Use BigQuery for its scalability and ability to add columns to a schema. Partition race data based on season.
- C. Use Cloud Spanner for its scalability and ability to version schemas with zero downtime. Split race data using season as a primary key.
- D. Use Firestore for its scalable and flexible document-based database. Use collections to aggregate race data by season and event.
Answer: B
NEW QUESTION 25
Your company has sensitive data in Cloud Storage buckets. Data analysts have Identity Access Management (IAM) permissions to read the buckets. You want to prevent data analysts from retrieving the data in the buckets from outside the office network. What should you do?
- A. 1. Create a VPC Service Controls perimeter that includes the projects with the buckets.
2. Create an access level with the CIDR of the office network. - B. 1. Create a Cloud Function to remove IAM permissions from the buckets, and another Cloud Function to add IAM permissions to the buckets.
2. Schedule the Cloud Functions with Cloud Scheduler to add permissions at the start of business and remove permissions at the end of business. - C. 1. Create a Cloud VPN to the office network.
2. Configure Private Google Access for on-premises hosts. - D. 1. Create a firewall rule for all instances in the Virtual Private Cloud (VPC) network for source range.
2. Use the Classless Inter-domain Routing (CIDR) of the office network.
Answer: A
NEW QUESTION 26
Your company wants to start using Google Cloud resources but wants to retain their on-premises Active Directory domain controller for identity management. What should you do?
- A. Use Compute Engine to create an Active Directory (AD) domain controller that is a replica of the onpremises AD domain controller using Google Cloud Directory Sync.
- B. Use Google Cloud Directory Sync to synchronize Active Directory usernames with cloud identities and configure SAML SSO.
- C. Use Cloud Identity-Aware Proxy configured to use the on-premises Active Directory domain controller as an identity provider.
- D. Use the Admin Directory API to authenticate against the Active Directory domain controller.
Answer: B
Explanation:
Explanation
https://cloud.google.com/solutions/federating-gcp-with-active-directory-introduction#implementing_federation
NEW QUESTION 27
Your company has successfully migrated to the cloud and wants to analyze their data stream to optimize
operations. They do not have any existing code for this analysis, so they are exploring all their options.
These options include a mix of batch and stream processing, as they are running some hourly jobs and
live-processing some data as it comes in.
Which technology should they use for this?
- A. Google Cloud Dataflow
- B. Google Container Engine with Bigtable
- C. Google Cloud Dataproc
- D. Google Compute Engine with Google BigQuery
Answer: A
Explanation:
Explanation/Reference:
Explanation:
Cloud Dataflow is a fully-managed service for transforming and enriching data in stream (real time) and
batch (historical) modes with equal reliability and expressiveness — no more complex workarounds or
compromises needed.
References: https://cloud.google.com/dataflow/
NEW QUESTION 28
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