Associate-Cloud-Engineer Exam Dumps - Google Cloud Certified - Associate Cloud Engineer

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Question # 17

You are running a data warehouse on BigQuery. A partner company is offering a recommendation engine based on the data in your data warehouse. The partner company is also running their application on Google Cloud. They manage the resources in their own project, but they need access to the BigQuery dataset in your project. You want to provide the partner company with access to the dataset What should you do?

Create a Service Account in your own project, and grant this Service Account access to BigGuery in your project

Create a Service Account in your own project, and ask the partner to grant this Service Account access to BigQuery in their project

Ask the partner to create a Service Account in their project, and have them give the Service Account access to BigQuery in their project

Ask the partner to create a Service Account in their project, and grant their Service Account access to the BigQuery dataset in your project

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Question # 18

You are the organization and billing administrator for your company. The engineering team has the Project Creator role on the organization. You do not want the engineering team to be able to link projects to the billing account. Only the finance team should be able to link a project to a billing account, but they should not be able to make any other changes to projects. What should you do?

Assign the finance team only the Billing Account User role on the billing account.

Assign the engineering team only the Billing Account User role on the billing account.

Assign the finance team the Billing Account User role on the billing account and the Project Billing Manager role on the organization.

Assign the engineering team the Billing Account User role on the billing account and the Project Billing Manager role on the organization.

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Question # 19

(You are developing an internet of things (IoT) application that captures sensor data from multiple devices that have already been set up. You need to identify the global data storage product your company should use to store this data. You must ensure that the storage solution you choose meets your requirements of sub-millisecond latency. What should you do?)

Store the IoT data in Spanner. Use caches to speed up the process and avoid latencies.

Store the IoT data in Bigtable.

Capture IoT data in BigQuery datasets.

Store the IoT data in Cloud Storage. Implement caching by using Cloud CDN.

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Answer:

Explanation:

Let's evaluate each option based on the requirement of sub-millisecond latency for globally stored IoT data:

A. Spanner with Caching: While Spanner offers strong consistency and global scalability, the base latency might not consistently be sub-millisecond for all read/write operations globally. Introducing caching adds complexity and doesn't guarantee sub-millisecond latency for all initial reads or cache misses.

B. Bigtable: Bigtable is a highly scalable NoSQL database service designed for low-latency, high-throughput workloads. It excels at storing and retrieving large volumes of time-series data, which is typical for IoT sensor data. Its architecture is optimized for single-key lookups and scans, providing consistent sub-millisecond latency, making it a strong candidate for this use case.

C. BigQuery: BigQuery is a fully managed, serverless data warehouse designed for analytical queries on large datasets. While it's excellent for analyzing IoT data in batch, it's not optimized for the low-latency, high-throughput ingestion and retrieval required for real-time IoT applications with sub-millisecond latency needs.

D. Cloud Storage with Cloud CDN: Cloud Storage is object storage and is not designed for low-latency transactional workloads. Cloud CDN is a content delivery network that caches content closer to users for faster delivery, but it's not suitable for the primary storage of rapidly incoming IoT sensor data requiring sub-millisecond write latency.

Google Cloud Documentation References:

Cloud Bigtable Overview: https://cloud.google.com/bigtable/docs/overview - This document highlights Bigtable 's suitability for low-latency and high-throughput applications, including IoT. It mentions its ability to handle massive amounts of data with consistent performance.

Spanner Overview: https://cloud.google.com/spanner/docs/overview - While Spanner offers low latency, Bigtable is generally preferred for extremely high-throughput, low-latency use cases like raw sensor data ingestion due to its optimized architecture for such workloads.

BigQuery Overview: https://cloud.google.com/bigquery/docs/introduction - This emphasizes BigQuery 's analytical capabilities rather than low-latency operational workloads.

Cloud Storage Overview: https://cloud.google.com/storage/docs/overview - This describes Cloud Storage as object storage, not ideal for sub-millisecond latency reads and writes required for real-time IoT data.

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Question # 20

You are a Google Cloud organization administrator. You need to configure organization policies and log sinks on Google Cloud projects that cannot be removed by project users to comply with your company's security policies. The security policies are different for each company department Each company department has a user with the Project Owner role assigned to their projects. What should you do?

Organize projects under folders for each department. Configure both organization policies and log sinks on the folders

Organize projects under folders for each department. Configure organization policies on the organization and log sinks on the folders.

Use a standard naming convention for projects that includes the department name. Configure organization policies on the organization and log sinks on the projects.

Use a standard naming convention for projects that includes the department name. Configure both organization policies and log sinks on the projects.

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Question # 21

Your company stores data from multiple sources that have different data storage requirements. These data include:

1. Customer data that is structured and read with complex queries

2. Historical log data that is large in volume and accessed infrequently

3. Real-time sensor data with high-velocity writes, which needs to be available for analysis but can tolerate some data loss

You need to design the most cost-effective storage solution that fulfills all data storage requirements. What should you do?

Use Spanner for all data.

Use Cloud SQL for customer data, Cloud Storage (Coldline) for historical logs, and BigQuery for sensor data.

Use Cloud SQL for customer data, Cloud Storage (Archive) for historical logs, and Bigtable for sensor data.

Use Firestore for customer data, Cloud Storage (Nearline) for historical logs, and Bigtable for sensor data.

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Question # 22

You have deployed an application on a single Compute Engine instance. The application writes logs to disk. Users start reporting errors with the application. You want to diagnose the problem. What should you do?

Navigate to Cloud Logging and view the application logs.

Connect to the instanceâ€™s serial console and read the application logs.

Configure a Health Check on the instance and set a Low Healthy Threshold value.

Install and configure the Cloud Logging Agent and view the logs from Cloud Logging.

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Question # 23

You create a Deployment with 2 replicas in a Google Kubernetes Engine cluster that has a single preemptible node pool. After a few minutes, you use kubectl to examine the status of your Pod and observe that one of them is still in Pending status:

What is the most likely cause?

The pending Pod's resource requests are too large to fit on a single node of the cluster.

Too many Pods are already running in the cluster, and there are not enough resources left to schedule the pending Pod.

The node pool is configured with a service account that does not have permission to pull the container image used by the pending Pod.

The pending Pod was originally scheduled on a node that has been preempted between the creation of the Deployment and your verification of the Podsâ€™ status. It is currently being rescheduled on a new node.

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Question # 24

You have a managed instance group comprised of preemptible VM's. All of the VM's keepdeleting and recreating themselves every minute. What is a possible cause of thisbehavior?

Your zonal capacity is limited, causing all preemptible VM's to be shutdown torecover capacity. Try deploying your group to another zone.

You have hit your instance quota for the region.

Your managed instance group's VM's are toggled to only last 1 minute inpreemptible settings.

Your managed instance group's health check is repeatedly failing, either to amisconfigured health check or misconfigured firewall rules not allowing the healthcheck to access the instance

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