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

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Question # 81

Your organization is a financial company that needs to store audit log files for 3 years. Your organization has hundreds of Google Cloud projects. You need to implement a cost-effective approach for log file retention. What should you do?

Create an export to the sink that saves logs from Cloud Audit to BigQuery.

Create an export to the sink that saves logs from Cloud Audit to a Coldline Storage bucket.

Write a custom script that uses logging API to copy the logs from Stackdriver logs to BigQuery.

Export these logs to Cloud Pub/Sub and write a Cloud Dataflow pipeline to store logs to Cloud SQL.

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Question # 82

You need to create a copy of a custom Compute Engine virtual machine (VM) to facilitate an expected increase in application traffic due to a business acquisition. What should you do?

Create a Compute Engine snapshot of your base VM. Create your images from that snapshot.

Create a Compute Engine snapshot of your base VM. Create your instances from that snapshot.

Create a custom Compute Engine image from a snapshot. Create your images from that image.

Create a custom Compute Engine image from a snapshot. Create your instances from that image.

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Question # 83

(You need to migrate multiple PostgreSQL databases from your on-premises data center to Google Cloud. You want to significantly improve the performance of your databases while minimizing changes to your data schema and application code. You expect to exceed 150 TB of data per geographical region. You want to follow Google-recommended practices and minimize your operational costs. What should you do?)

Migrate your data to AlloyDB.

Migrate your data to Spanner.

Migrate your data to Firebase.

Migrate your data to Bigtable.

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Answer:

Explanation:

Let's analyze each option based on the requirements: PostgreSQL compatibility, significant performance improvement, minimal schema/code changes, handling large data volumes, Google-recommended practices, and cost minimization:

A. Migrate your data to AlloyDB: AlloyDB for PostgreSQL is a fully managed, PostgreSQL-compatible database service that offers significant performance improvements over standard PostgreSQL due to its architectural optimizations. It is designed to handle large data volumes and minimizes the need for schema and application code changes as it's wire-compatible with PostgreSQL. This aligns well with the requirements for performance improvement, minimal changes, large data, and being a Google-recommended option for PostgreSQL workloads.

B. Migrate your data to Spanner: Spanner is a globally distributed, horizontally scalable database with strong consistency. While it offers excellent scalability and performance, it's not directly PostgreSQL-compatible. Migrating to Spanner would likely require significant schema and application code changes due to differences in data modeling and SQL dialect.

C. Migrate your data to Firebase: Firebase is a suite of mobile and web development tools, with its primary database offering being Firestore (a NoSQL document database) and Realtime Database. These are not PostgreSQL-compatible and would require substantial changes to the data model and application code.

D. Migrate your data to Bigtable: Bigtable is a highly scalable NoSQL wide-column store. It's not compatible with PostgreSQL and requires a completely different data model and application logic.

Therefore, AlloyDB is the most suitable option as it provides PostgreSQL compatibility for minimal migration effort, significant performance improvements, scalability for large data volumes, and is a recommended Google Cloud database service for PostgreSQL workloads.

Google Cloud Documentation References:

AlloyDB for PostgreSQL Overview: https://cloud.google.com/alloydb/docs/overview - This document highlights AlloyDB 's PostgreSQL compatibility, performance benefits, scalability, and suitability for migrating existing PostgreSQL workloads.

Spanner Overview: https://cloud.google.com/spanner/docs/overview - This emphasizes Spanner 's unique features and differences from traditional relational databases like PostgreSQL.

Firebase Documentation: https://firebase.google.com/docs - This outlines the features of Firebase, including Firestore and Realtime Database, highlighting their NoSQL nature and incompatibility with PostgreSQL.

Cloud Bigtable Overview: https://cloud.google.com/bigtable/docs/overview - This describes Bigtable as a NoSQL database, emphasizing its differences from relational databases like PostgreSQL.

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Question # 84

You have a web application deployed as a managed instance group. You have a new version of the application to gradually deploy. Your web application is currently receiving live web traffic. You want to ensure that the available capacity does not decrease during the deployment. What should you do?

Perform a rolling-action start-update with maxSurge set to 0 and maxUnavailable set to 1.

Perform a rolling-action start-update with maxSurge set to 1 and maxUnavailable set to 0.

Create a new managed instance group with an updated instance template. Add the group to the backend service for the load balancer. When all instances in the new managed instance group are healthy, delete the old managed instance group.

Create a new instance template with the new application version. Update the existing managed instance group with the new instance template. Delete the instances in the managed instance group to allow the managed instance group to recreate the instance using the new instance template.

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Question # 85

Your web application is hosted on Cloud Run and needs to query a Cloud SOL database. Every morning during a traffic spike, you notice API quota errors in Cloud SOL logs. The project has already reached the maximum API quota. You want to make a configuration change to mitigate the issue. What should you do?

Modify the minimum number of Cloud Run instances.

Set a minimum concurrent requests environment variable for the application.

Modify the maximum number of Cloud Run instances.

Use traffic splitting.

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Question # 86

A company wants to build an application that stores images in a Cloud Storage bucket and wants to generate thumbnails as well as resize the images. They want to use a google managed service that can scale up and scale down to zero automatically with minimal effort. You have been asked to recommend a service. Which GCP service would you suggest?

Google Compute Engine

Google App Engine

Cloud Functions

Google Kubernetes Engine

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Question # 87

Your company publishes large files on an Apache web server that runs on a Compute Engine instance. The Apache web server is not the only application running in the project. You want to receive an email when the egress network costs for the server exceed 100 dollars for the current month as measured by Google Cloud Platform (GCP). What should you do?

Set up a budget alert on the project with an amount of 100 dollars, a threshold of 100%, and notification type of â€œemail.â€

Set up a budget alert on the billing account with an amount of 100 dollars, a threshold of 100%, and notification type of â€œemail.â€

Export the billing data to BigQuery. Create a Cloud Function that uses BigQuery to sum the egress network costs of the exported billing data for the Apache web server for the current month and sends an email if it is over 100 dollars. Schedule the Cloud Function using Cloud Scheduler to run hourly.

Use the Stackdriver Logging Agent to export the Apache web server logs to Stackdriver Logging. Create a Cloud Function that uses BigQuery to parse the HTTP response log data in Stackdriver for the current month and sends an email if the size of all HTTP responses, multiplied by current GCP egress prices, totals over 100 dollars. Schedule the Cloud Function using Cloud Scheduler to run hourly.

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Question # 88

You are asked to set up application performance monitoring on Google Cloud projects A, B, and C as a single pane of glass. You want to monitor CPU, memory, and disk. What should you do?

Enable API and then share charts from project A, B, and C.

Enable API and then give the metrics.reader role to projects A, B, and C.

Enable API and then use default dashboards to view all projects in sequence.

Enable API, create a workspace under project A, and then add project B and C.

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