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Achieving the Google Professional-Cloud-Architect Certification demonstrates to potential employers and clients that an individual has the skills and knowledge necessary to design and implement effective cloud architecture solutions using GCP. Google Certified Professional - Cloud Architect (GCP) certification is particularly valuable for individuals who work in roles such as cloud architects, cloud engineers, and solutions architects.

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Google Certified Professional - Cloud Architect (GCP) Sample Questions (Q141-Q146):

NEW QUESTION # 141
Mountkirk Games wants you to design their new testing strategy. How should the test coverage differ from
their existing backends on the other platforms?

A. Tests should be applied after the release is in the production environment
B. Tests should scale well beyond the prior approaches
C. Tests should include directly testing the Google Cloud Platform (GCP) infrastructure
D. Unit tests are no longer required, only end-to-end tests

Answer: B

Explanation:
Explanation/Reference:
Explanation:
From Scenario:
A few of their games were more popular than expected, and they had problems scaling their application
servers, MySQL databases, and analytics tools.
Requirements for Game Analytics Platform include: Dynamically scale up or down based on game activity

NEW QUESTION # 142
You have deployed an application to Kubernetes Engine, and are using the Cloud SQL proxy container to make the Cloud SQL database available to the services running on Kubernetes. You are notified that the application is reporting database connection issues. Your company policies require a post-mortem. What should you do?

A. In the GCP Console, navigate to Stackdriver Logging. Consult logs for Kubernetes Engine and Cloud SQL.
B. Validate that the Service Account used by the Cloud SQL proxy container still has the Cloud Build Editor role.
C. In the GCP Console, navigate to Cloud SQL. Restore the latest backup. Use kubect1 to restart all pods.
D. Use gcloud sql instances restart.

Answer: A

NEW QUESTION # 143
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 Container Engine with Bigtable
B. Google Cloud Dataflow
C. Google Cloud Dataproc
D. Google Compute Engine with Google BigQuery

Answer: B

Explanation:
Dataflow is for processing both the Batch and Stream.
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 # 144
Your company has a Google Cloud project that uses BlgQuery for data warehousing There are some tables that contain personally identifiable information (PI!) Only the compliance team may access the PH. The other information in the tables must be available to the data science team. You want to minimize cost and the time it takes to assign appropriate access to the tables What should you do?

A. 1 Create a dataset for the data science team
2 Create views of tables that you want to share excluding Pll
3 Assign an appropriate project-level IAM role to the members of the data science team
4 Assign access controls to the dataset that contains the view
5 Authorize the view to access the source dataset
B. 1 From the dataset where you have the source data, create views of tables that you want to share, excluding Pll
2 Assign an appropriate project-level IAM role to the members of the data science team
3 Assign access controls to the dataset that contains the view
C. 1 From the dataset where you have the source data, create materialized views of tables that you want to share excluding Pll
2 Assign an appropriate project-level IAM role to the members of the data science team 3. Assign access controls to the dataset that contains the view.
D. 1. Create a dataset for the data science team.
2. Create materialized views of tables that you want to share, excluding Pll
3. Assign an appropriate project-level IAM role to the members of the data science team
4 Assign access controls to the dataset that contains the view
5 Authorize the view to access the source dataset

Answer: A

Explanation:
Explanation
This option can help minimize cost and time by using views and authorized datasets. Views are virtual tables defined by a SQL query that can exclude PII columns from the source tables. Views do not incur storage costs and do not duplicate data. Authorized datasets are datasets that have access to another dataset's data without granting direct access to individual users or groups. By creating a dataset for the data science team and creating views of tables that exclude PII, you can share only the relevant information with the team. By assigning an appropriate project-level IAM role to the members of the data science team, you can grant them access to the BigQuery service and resources. By assigning access controls to the dataset that contains the view, you can grant them access to query the views. By authorizing the view to access the source dataset, you can enable the view to read data from the source tables without exposing PII. The other options are not optimal for this scenario, because they either use materialized views instead of views, which incur storage costs and duplicate data (B, D), or do not create a separate dataset for the data science team, which makes it harder to manage access controls (A). References:
https://cloud.google.com/bigquery/docs/views
https://cloud.google.com/bigquery/docs/authorized-datasets

NEW QUESTION # 145
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 BigQuery for its scalability and ability to add columns to a schema. Partition race data based on season.
B. Use Firestore for its scalable and flexible document-based database. Use collections to aggregate race data by season and event.
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 Cloud SQL for its ability to automatically manage storage increases and compatibility with MySQL. Use separate database instances for each season.

Answer: A

Explanation:
Reference: https://cloud.google.com/bigquery/public-data
Topic 9, EHR Health Care
Company overview
EHR Healthcare is a leading provider of electronic health record software to the medical industry. EHR Healthcare provides their software as a service to multi-national medical offices, hospitals, and insurance providers.
Solution concept
Due to rapid changes in the healthcare and insurance industry, EHR Healthcare's business has been growing exponentially year over year. They need to be able to scale their environment, adapt their disaster recovery plan, and roll out new continuous deployment capabilities to update their software at a fast pace. Google Cloud has been chosen to replace their current colocation facilities.
Existing technical environment
EHR's software is currently hosted in multiple colocation facilities. The lease on one of the data centers is about to expire.
Customer-facing applications are web-based, and many have recently been containerized to run on a group of Kubernetes clusters. Data is stored in a mixture of relational and NoSQL databases (MySQL, MS SQL Server, Redis, and MongoDB).
EHR is hosting several legacy file- and API-based integrations with insurance providers on-premises. These systems are scheduled to be replaced over the next several years. There is no plan to upgrade or move these systems at the current time.
Users are managed via Microsoft Active Directory. Monitoring is currently being done via various open source tools. Alerts are sent via email and are often ignored.
Business requirements
* On-board new insurance providers as quickly as possible.
* Provide a minimum 99.9% availability for all customer-facing systems.
* Provide centralized visibility and proactive action on system performance and usage.
* Increase ability to provide insights into healthcare trends.
* Reduce latency to all customers.
* Maintain regulatory compliance.
* Decrease infrastructure administration costs.
* Make predictions and generate reports on industry trends based on provider data.
Technical requirements
* Maintain legacy interfaces to insurance providers with connectivity to both on-premises systems and cloud providers.
* Provide a consistent way to manage customer-facing applications that are container-based.
* Provide a secure and high-performance connection between on-premises systems and Google Cloud.
* Provide consistent logging, log retention, monitoring, and alerting capabilities.
* Maintain and manage multiple container-based environments.
* Dynamically scale and provision new environments.
* Create interfaces to ingest and process data from new providers.
Executive statement
Our on-premises strategy has worked for years but has required a major investment of time and money in training our team on distinctly different systems, managing similar but separate environments, and responding to outages. Many of these outages have been a result of misconfigured systems, inadequate capacity to manage spikes in traffic, and inconsistent monitoring practices. We want to use Google Cloud to leverage a scalable, resilient platform that can span multiple environments seamlessly and provide a consistent and stable user experience that positions us for future growth.

NEW QUESTION # 146
......

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