[Apr-2026] Latest Google Professional-Cloud-DevOps-Engineer Certification Practice Test Questions
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Google Professional-Cloud-DevOps-Engineer exam is a certification program designed to validate the skills and knowledge of professionals in the field of DevOps engineering on the Google Cloud Platform. Professional-Cloud-DevOps-Engineer exam is intended for individuals who are experienced in DevOps practices and have expertise in designing, building, and maintaining scalable and reliable cloud solutions.
NEW QUESTION # 68
Your company has a Google Cloud resource hierarchy with folders for production test and development Your cyber security team needs to review your company's Google Cloud security posture to accelerate security issue identification and resolution You need to centralize the logs generated by Google Cloud services from all projects only inside your production folder to allow for alerting and near-real time analysis. What should you do?
- A. Create an aggregated log sink associated with the production folder that uses a Pub Sub topic as the destination
- B. Create an aggregated log sink associated with the production folder that uses a Cloud Logging bucket as the destination
- C. Enable the Workflows API and route all the logs to Cloud Logging
- D. Create a central Cloud Monitoring workspace and attach all related projects
Answer: B
Explanation:
The best option for centralizing the logs generated by Google Cloud services from all projects only inside your production folder is to create an aggregated log sink associated with the production folder that uses a Cloud Logging bucket as the destination. An aggregated log sink is a log sink that collects logs from multiple sources, such as projects, folders, or organizations. A Cloud Logging bucket is a storage location for logs that can be used as a destination for log sinks. By creating an aggregated log sink with a Cloud Logging bucket, you can collect and store all the logs from the production folder in one place and allow for alerting and near-real time analysis using Cloud Monitoring and Cloud Operations.
NEW QUESTION # 69
You are running a web application deployed to a Compute Engine managed instance group Ops Agent is installed on all instances You recently noticed suspicious activity from a specific IP address You need to configure Cloud Monitoring to view the number of requests from that specific IP address with minimal operational overhead. What should you do?
- A. Create a script to scrape the web server log Export the IP address request metrics to the Cloud Monitoring API
- B. Configure the Ops Agent with a logging receiver Create a logs-based metric
- C. Update the application to export the IP address request metrics to the Cloud Monitoring API
- D. Configure the Ops Agent with a metrics receiver
Answer: B
Explanation:
The best option for configuring Cloud Monitoring to view the number of requests from a specific IP address with minimal operational overhead is to configure the Ops Agent with a logging receiver and create a logs-based metric. The Ops Agent is an agent that collects system metrics and logs from your VM instances and sends them to Cloud Monitoring and Cloud Logging. A logging receiver is a configuration that specifies which logs are collected by the Ops Agent and how they are processed. You can use a logging receiver to collect web server logs from your VM instances and send them to Cloud Logging. A logs-based metric is a metric that is extracted from log entries in Cloud Logging. You can use a logs-based metric to count the number of requests from a specific IP address by using a filter expression. You can then use Cloud Monitoring to view and analyze the logs-based metric.
NEW QUESTION # 70
Your organization recently adopted a container-based workflow for application development. Your team develops numerous applications that are deployed continuously through an automated build pipeline to a Kubernetes cluster in the production environment. The security auditor is concerned that developers or operators could circumvent automated testing and push code changes to production without approval. What should you do to enforce approvals?
- A. Use an Admission Controller to verify that incoming requests originate from approved sources.
- B. Configure the build system with protected branches that require pull request approval.
- C. Leverage Kubernetes Role-Based Access Control (RBAC) to restrict access to only approved users.
- D. Enable binary authorization inside the Kubernetes cluster and configure the build pipeline as an attestor.
Answer: D
Explanation:
Explanation
The keywords here is "developers or operators". Option A the operators could push images to production without approval (operators could touch the cluster directly and the cluster cannot do any action against them).
Rest same as francisco_guerra.
NEW QUESTION # 71
You support an application running on App Engine. The application is used globally and accessed from various device types. You want to know the number of connections. You are using Stackdriver Monitoring for App Engine. What metric should you use?
- A. flex/connections/current
- B. flex/instance/connections/current
- C. tcp_ssl_proxy/open_connections
- D. tcp_ssl_proxy/new_connections
Answer: A
NEW QUESTION # 72
Your team is preparing to launch a new API in Cloud Run. The API uses an OpenTelemetry agent to send distributed tracing data to Cloud Trace to monitor the time each request takes. The team has noticed inconsistent trace collection. You need to resolve the issue. What should you do?
- A. Use an HTTP health check.
- B. Configure CPU to be always-allocated.
- C. Configure CPU to be allocated only during request processing.
- D. Increase the CPU limit in Cloud Run from 2 to 4.
Answer: B
Explanation:
Comprehensive and Detailed Explanation:
Cloud Run scales down to zero when idle. If CPU is allocated only during request processing, background telemetry tasks (like OpenTelemetry traces) may not complete before the container shuts down.
Solution: Configure CPU to be always-allocated # This ensures that background tasks (like trace exports) continue even when the service is idle.
#Why not other options?
A (Increasing CPU to 4 cores)## More CPU won't help if traces are being cut off due to shutdown.
B (Using an HTTP health check)## Doesn't affect OpenTelemetry traces.
C (CPU only during request processing)## This causes the problem because traces need CPU after request completion to be exported.
#Official Reference:
Cloud Run Tracing with OpenTelemetry
Cloud Run Always-Allocated CPU
NEW QUESTION # 73
Your company runs applications in Google Kubernetes Engine (GKE) that are deployed following a GitOps methodology.
Application developers frequently create cloud resources to support their applications. You want to give developers the ability to manage infrastructure as code, while ensuring that you follow Google-recommended practices. You need to ensure that infrastructure as code reconciles periodically to avoid configuration drift.
What should you do?
- A. Configure Cloud Build with a Terraform builder to execute plan and apply commands.
- B. Create a Pod resource with a Terraform docker image to execute terraform plan and terraform apply commands.
- C. Create a Job resource with a Terraform docker image to execute terraforrm plan and terraform apply commands.
- D. Install and configure Config Connector in Google Kubernetes Engine (GKE).
Answer: D
Explanation:
The best option to give developers the ability to manage infrastructure as code, while ensuring that you follow Google-recommended practices, is to install and configure Config Connector in Google Kubernetes Engine (GKE).
Config Connector is a Kubernetes add-on that allows you to manage Google Cloud resources through Kubernetes. You can use Config Connector to create, update, and delete Google Cloud resources using Kubernetes manifests.Config Connector also reconciles the state of the Google Cloud resources with the desired state defined in the manifests, ensuring that there is no configuration drift1.
Config Connector follows the GitOps methodology, as it allows you to store your infrastructure configuration in a Git repository, and use tools such as Anthos Config Management or Cloud Source Repositories to sync the configuration to your GKE cluster.This way, you can use Git as the source of truth for your infrastructure, and enable reviewable and version-controlled workflows2.
Config Connector can be installed and configured in GKE using either the Google Cloud Console or the gcloud command-line tool. You need to enable the Config Connector add-on for your GKE cluster, and create a Google Cloud service account with the necessary permissions to manage the Google Cloud resources.You also need to create a Kubernetes namespace for each Google Cloud project that you want to manage with Config Connector3.
By using Config Connector in GKE, you can give developers the ability to manage infrastructure as code, while ensuring that you follow Google-recommended practices.You can also benefit from the features and advantages of Kubernetes, such as declarative configuration, observability, and portability4.
References:
1:Overview | Artifact Registry Documentation | Google Cloud
2: Deploy Anthos on GKE with Terraform part 1: GitOps with Config Sync | Google Cloud Blog
3: Installing Config Connector | Config Connector Documentation | Google Cloud
4: Why use Config Connector? | Config Connector Documentation | Google Cloud
NEW QUESTION # 74
You are creating a CI/CD pipeline to perform Terraform deployments of Google Cloud resources Your CI/CD tooling is running in Google Kubernetes Engine (GKE) and uses an ephemeral Pod for each pipeline run You must ensure that the pipelines that run in the Pods have the appropriate Identity and Access Management (1AM) permissions to perform the Terraform deployments You want to follow Google-recommended practices for identity management What should you do?
Choose 2 answers
- A. Create a new Kubernetes service account, and assign the service account to the Pods Use Workload Identity to authenticate as the Google service account
- B. Assign the appropriate 1AM permissions to the Google service account associated with the Compute Engine VM instances that run the Pods
- C. Create a new JSON service account key for the Google service account store the key in the secret management store for the CI/CD tool and configure Terraform to use this key for authentication
- D. Create a new Google service account, and assign the appropriate 1AM permissions
- E. Create a new JSON service account key for the Google service account store the key as a Kubernetes secret, inject the key into the Pods, and set the boogle_application_credentials environment variable
Answer: A,D
NEW QUESTION # 75
You support a multi-region web service running on Google Kubernetes Engine (GKE) behind a Global HTTP'S Cloud Load Balancer (CLB). For legacy reasons, user requests first go through a third-party Content Delivery Network (CDN). which then routes traffic to the CLB. You have already implemented an availability Service Level Indicator (SLI) at the CLB level. However, you want to increase coverage in case of a potential load balancer misconfiguration. CDN failure, or other global networking catastrophe. Where should you measure this new SLI?
Choose 2 answers
- A. A synthetic client that periodically sends simulated user requests
- B. Metrics exported from the application servers
- C. Your application servers' logs
- D. GKE health checks for your application servers
- E. Instrumentation coded directly in the client
Answer: A,D
NEW QUESTION # 76
You support an application running on GCP and want to configure SMS notifications to your team for the most critical alerts in Stackdriver Monitoring. You have already identified the alerting policies you want to configure this for. What should you do?
- A. Configure a Slack notification for each alerting policy. Set up a Slack-to-SMS integration to send SMS messages when Slack messages are received. Ensure that your team members add their SMS/phone numbers to the external integration.
- B. Download and configure a third-party integration between Stackdriver Monitoring and an SMS gateway. Ensure that your team members add their SMS/phone numbers to the external tool.
- C. Select the Webhook notifications option for each alerting policy, and configure it to use a third-party integration tool. Ensure that your team members add their SMS/phone numbers to the external tool.
- D. Ensure that your team members set their SMS/phone numbers in their Stackdriver Profile. Select the SMS notification option for each alerting policy and then select the appropriate SMS/phone numbers from the list.
Answer: D
NEW QUESTION # 77
You support an application that stores product information in cached memory. For every cache miss, an entry is logged in Stackdriver Logging. You want to visualize how often a cache miss happens over time. What should you do?
- A. Configure Stackdriver Profiler to identify and visualize when the cache misses occur based on the logs.
- B. Configure BigOuery as a sink for Stackdriver Logging. Create a scheduled query to filter the cache miss logs and write them to a separate table
- C. Link Stackdriver Logging as a source in Google Data Studio. Filler (he logs on the cache misses.
- D. Create a logs-based metric in Stackdriver Logging and a dashboard for that metric in Stackdriver Monitoring.
Answer: D
Explanation:
Explanation
https://cloud.google.com/logging/docs/logs-based-metrics#counter-metric
NEW QUESTION # 78
You are the Site Reliability Engineer responsible for managing your company's data services and products. You regularly navigate operational challenges, such as unpredictable data volume and high cost, with your company's data ingestion processes. You recently learned that a new data ingestion product will be developed in Google Cloud. You need to collaborate with the product development team to provide operational input on the new product. What should you do?
- A. Review the design of the product with the product development team to provide feedback early in the design phase.
- B. When the new product is used by at least one internal customer in production, share error logs and monitoring metrics with the product development team.
- C. Deploy the prototype product in a test environment, run a load test, and share the results with the product development team.
- D. When the initial product version passes the quality assurance phase and compliance assessments, deploy the product to a staging environment. Share error logs and performancemetrics with the product development team.
Answer: A
NEW QUESTION # 79
You are running an experiment to see whether your users like a new feature of a web application. Shortly after deploying the feature as a canary release, you receive a spike in the number of 500 errors sent to users, and your monitoring reports show increased latency. You want to quickly minimize the negative impact on users.
What should you do first?
- A. Trace the origin of 500 errors and the root cause of increased latency.
- B. Roll back the experimental canary release.
- C. Start monitoring latency, traffic, errors, and saturation.
- D. Record data for the postmortem document of the incident.
Answer: B
NEW QUESTION # 80
You recently migrated an ecommerce application to Google Cloud. You now need to prepare the application for the upcoming peak traffic season. You want to follow Google-recommended practices. What should you do first to prepare for the busy season?
- A. Pre-provision the additional compute power that was used last season, and expect growth.
- B. Load test the application to profile its performance for scaling.
- C. Migrate the application to Cloud Run, and use autoscaling.
- D. Create a Terraform configuration for the application's underlying infrastructure to quickly deploy to additional regions.
Answer: B
Explanation:
The first thing you should do to prepare your ecommerce application for the upcoming peak traffic season is to load test the application to profile its performance for scaling. Load testing is a process of simulating high traffic or user demand on your application and measuring how it responds.Load testing can help you identify any bottlenecks, errors, or performance issues that might affect your application during the busy season1.Load testing can also help you determine the optimal scaling strategy for your application, such as horizontal scaling (adding more instances) or vertical scaling (adding more resources to each instance)2.
There are different tools and methods for load testing your ecommerce application on Google Cloud, depending on the type and complexity of your application.For example, you can use Cloud Load Balancing to distribute traffic across multiple instances of your application, and use Cloud Monitoring to measure the latency, throughput, and error rate of your application3.You can also use Cloud Functions or Cloud Run to create serverless load generators that can simulate user requests and send them to your application4.
Alternatively, you can use third-party tools such as Apache JMeter or Locust to create and run load tests on your application.
By load testing your ecommerce application before the peak traffic season, you can ensure that your application is ready to handle the expected load and provide a good user experience. You can also use the results of your load tests to plan and implement other steps to prepare your application for the busy season, such as migrating to a more scalable platform, creating a Terraform configuration for deploying to additional regions, or pre-provisioning additional compute power.
References:
1:Load Testing 101: How To Test Website Performance | BlazeMeter
2: Scaling applications | Google Cloud
3:Load testing using Google Cloud | Solutions | Google Cloud
4: Serverless load testing using Cloud Functions | Solutions | Google Cloud
NEW QUESTION # 81
You are designing a deployment technique for your applications on Google Cloud. As part Of your deployment planning, you want to use live traffic to gather performance metrics for new versions Ofyour applications. You need to test against the full production load before your applications are launched. What should you do?
- A. Use canary testing with rolling updates deployment,
- B. Use A/B testing with blue/green deployment.
- C. Use shadow testing with continuous deployment.
- D. Use canary testing with continuous deployment.
Answer: C
Explanation:
The correct answer is B. Use shadow testing with continuous deployment.
Shadow testing is a deployment technique that involves routing a copy of the live traffic to a new version of the application, without affecting the production environment. This way, you can gather performance metrics and compare them with the current version, without exposing the new version to the users. Shadow testing can help you test against the full production load and identify any issues or bottlenecks before launching the new version. You can use continuous deployment to automate the process of deploying the new version after it passes the shadow testing.
NEW QUESTION # 82
You manage several production systems that run on Compute Engine in the same Google Cloud Platform (GCP) project. Each system has its own set of dedicated Compute Engine instances. You want to know how must it costs to run each of the systems. What should you do?
- A. Assign all instances a label specific to the system they run. Configure BigQuery billing export and query costs per label.
- B. Name each virtual machine (VM) after the system it runs. Set up a usage report export to a Cloud Storage bucket. Configure the bucket as a source in BigQuery to query costs based on VM name.
- C. In the Google Cloud Platform Console, use the Cost Breakdown section to visualize the costs per system.
- D. Enrich all instances with metadata specific to the system they run. Configure Stackdriver Logging to export to BigQuery, and query costs based on the metadata.
Answer: A
Explanation:
Explanation
https://cloud.google.com/billing/docs/how-to/export-data-bigquery
NEW QUESTION # 83
You need to define Service Level Objectives (SLOs) for a high-traffic multi-region web application. Customers expect the application to always be available and have fast response times. Customers are currently happy with the application performance and availability. Based on current measurement, you observe that the 90th percentile of latency is 120ms and the 95th percentile of latency is 275ms over a 28-day window. What latency SLO would you recommend to the team to publish?
- A. 90th percentile - 150ms
95th percentile - 300ms - B. 90th percentile - 100ms
95th percentile - 250ms - C. 90th percentile - 120ms
95th percentile - 275ms - D. 90th percentile - 250ms
95th percentile - 400ms
Answer: C
NEW QUESTION # 84
Your application artifacts are being built and deployed via a CI/CD pipeline. You want the CI/CD pipeline to securely access application secrets. You also want to more easily rotate secrets in case of a security breach. What should you do?
- A. Prompt developers for secrets at build time. Instruct developers to not store secrets at rest.
- B. Store secrets in a separate configuration file on Git. Provide select developers with access to the configuration file.
- C. Store secrets in Cloud Storage encrypted with a key from Cloud KMS. Provide the CI/CD pipeline with access to Cloud KMS via IAM.
- D. Encrypt the secrets and store them in the source code repository. Store a decryption key in a separate repository and grant your pipeline access to it
Answer: C
NEW QUESTION # 85
You are managing an MLOps platform on a Google Kubernetes Engine (GKE) cluster that serves two teams:
* The Research team runs hundreds of small, daily experiments that can run as capacity allows and are highly cost-sensitive.
* The Production team must retrain the company's main language model every Friday at 10:00 PM. This job is business-critical, cannot be interrupted, and must start on time.
You need to configure the modes for Dynamic Workload Scheduler to meet each team's requirements. What should you do?
- A. Use Flex Start mode for both the Research and Production teams.
- B. Use Flex Start mode for the Research team and Calendar mode for the Production team.
- C. Use Calendar mode for both the Research and Production teams.
- D. Use Calendar mode for the Research team and Flex Start mode for the Production team.
Answer: B
Explanation:
Comprehensive and Detailed 150 to 200 words of Explanation From Google Cloud DevOps guides documents:
Google Cloud's Dynamic Workload Scheduler offers two distinct modes designed to balance resource availability with cost and criticality. Flex Start mode is optimized for cost-sensitive, asynchronous workloads that do not have a strict start time. It allows the scheduler to obtain capacity when it becomes available within a 7-day window, making it the ideal choice for the Research team's hundreds of small experiments. This mode prioritizes resource optimization and lower costs over immediate execution.
Conversely, Calendar mode is designed for business-critical jobs that require guaranteed capacity at a specific date and time. By using Calendar mode, the Production team can reserve the necessary GPU or TPU resources for their Friday 10:00 PM retraining session. This ensures the job starts exactly on time and runs without interruption, satisfying the "business-critical" requirement. Combining these two modes allows an organization to maximize cluster utilization: using Flex Start to fill the gaps with non-urgent research while ensuring that high-priority production tasks have the "just-in-time" resource guarantees they need to maintain SLOs (Service Level Objectives).
NEW QUESTION # 86
You have an application running in Google Kubernetes Engine. The application invokes multiple services per request but responds too slowly. You need to identify which downstream service or services are causing the delay. What should you do?
- A. Create a Dataflow pipeline to analyze service metrics in real time.
- B. Analyze VPC flow logs along the path of the request.
- C. Investigate the Liveness and Readiness probes for each service.
- D. Use a distributed tracing framework such as OpenTelemetry or Stackdriver Trace.
Answer: A
NEW QUESTION # 87
You have a pool of application servers running on Compute Engine. You need to provide a secure solution that requires the least amount of configuration and allows developers to easily access application logs for troubleshooting. How would you implement the solution on GCP?
- A. * Deploy the Stackdriver logging agent to the application servers.
* Give the developers the IAM Logs Private Logs Viewer role to access Stackdriver and view logs. - B. * Install the gsutil command line tool on your application servers.
* Write a script using gsutil to upload your application log to a Cloud Storage bucket, and then schedule it to run via cron every 5 minutes. - C. * Deploy the Stackdriver logging agent to the application servers.
* Give the developers the IAM Logs Viewer role to access Stackdriver and view logs. - D. * Deploy the Stackdriver monitoring agent to the application servers.
* Give the developers the IAM Monitoring Viewer role to access Stackdriver and view metrics.
Answer: C
Explanation:
* Give the developers IAM Object Viewer access to view the logs in the specified bucket.
Explanation:
https://cloud.google.com/logging/docs/audit#access-control
NEW QUESTION # 88
You are running a web application that connects to an AlloyDB cluster by using a private IP address in your default VPC. You need to run a database schema migration in your CI/CD pipeline by using Cloud Build before deploying a new version of your application. You want to follow Google-recommended security practices. What should you do?
- A. Create a service account that has permission to access the database. Configure Cloud Build to use this service account and execute the schema migration script in a private pool.
- B. Set up a Cloud Build private pool to access the database through a static external IP address. Configure the database to only allow connections from this IP address. Execute the schema migration script in the private pool.
- C. Add the database username and password to Secret Manager. When running the schema migration script, retrieve the username and password from Secret Manager.
- D. Add the database username and encrypted password to the application configuration file. Use these credentials in Cloud Build to execute the schema migration script.
Answer: A
Explanation:
To securely connect Cloud Build to an AlloyDB cluster using a private IP address and adhere to Google- recommended security practices, you need to address two main aspects:
Network Connectivity:Ensuring Cloud Build can reach the private IP of the AlloyDB cluster.
Authentication/Credential Management:Securely authenticating Cloud Build to the AlloyDB cluster.
Let's break down why Option B is the most suitable:
Cloud Build Private Pool:AlloyDB is accessed via a private IP in your VPC. Cloud Build's default build environment runs on Google-managed infrastructure outside your VPC and cannot directly access private IP addresses. To enable this, you must use aCloud Build private pool. A private pool can be configured with VPC peering to your default VPC, allowing build steps running within that pool to access resources like your AlloyDB cluster via their private IPs. Option B correctly includes "execute the schema migration script in a private pool." Service Account with Permissions (IAM Database Authentication):AlloyDB supports IAM database authentication. This is a Google-recommended security practice because it allows you to manage database access using Google Cloud's Identity and Access Management (IAM) rather than relying on traditional database passwords.
You would create a dedicated service account for Cloud Build (or use the private pool's service account).
This service account would be granted the necessary IAM roles to connect to the AlloyDB instance (e.g., roles
/alloydb.client) and a database-level IAM role for login (e.g., roles/alloydb.user or roles/alloydb.admin depending on the permissions needed for schema migration).
Cloud Build would then be configured to use this service account. The "permission to access the database" in Option B refers to these IAM permissions. This method avoids managing and distributing database passwords.
Analyzing the options:
A: Set up a Cloud Build private pool to access the database through a static external IP address...
While using a private pool is correct for network access, routing this through a staticexternalIP for a resource that has aprivateIP is generally not the first-choice secure pattern if direct private access is feasible. It adds complexity and a potential external exposure point, even if firewalled. The aim is to keep traffic within the private network as much as possible.
B: Create a service account that has permission to access the database. Configure Cloud Build to use this service account and execute the schema migration script in a private pool.
This option correctly combines the use of aprivate pool(for private IP network access) with aservice account having permissions(strongly implying IAM database authentication for AlloyDB, which is a best practice).
This is a secure and robust approach.
C: Add the database username and encrypted password to the application configuration file...
Storing credentials, even if "encrypted" (the method and key management for encryption are unspecified and problematic), in application configuration files checked into source control or packaged with the application is a significant security risk and not a recommended practice.
D: Add the database username and password to Secret Manager. When running the schema migration script, retrieve the username and password from Secret Manager.
UsingSecret Managerto store database usernames and passwords is a Google-recommended practiceifyou are using password-based authentication. However, this optionalonedoes not solve the network connectivity issue for Cloud Build to reach the private IP of AlloyDB. You would still need a private pool. While D is good for secret management, B offers a more comprehensive solution that includes both the network aspect and implies a more modern authentication method (IAM database auth). If the question forced a choice between only doing secure credential storage (D) or doing IAM auth + private networking (B), B is more complete for the overall task.
Conclusion:Option B is the most aligned with Google-recommended security practices as it addresses both the necessary private network connectivity via a Cloud Build private pool and promotes the use of IAM-based database authentication for AlloyDB, which is generally preferred over managing passwords.
References (General Concepts):
Cloud Build Private Pools for VPC Access:Google Cloud documentation for Cloud Build explicitly details using private pools to connect to resources in a VPC network.
See:https://www.google.com/search?q=https://cloud.google.com/build/docs/private-pools/accessing-private- resources-with-private-pools AlloyDB IAM Database Authentication:Google Cloud documentation for AlloyDB highlights IAM database authentication as a secure method.
See:https://www.google.com/search?q=https://cloud.google.com/alloydb/docs/iam-authentication Secret Manager:If password authentication were the only option, Secret Manager would be the recommended way to store those credentials.
See:https://cloud.google.com/secret-manager
Option B synergizes the benefits of private networking and modern IAM-based authentication for a comprehensive secure solution.
NEW QUESTION # 89
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