PCDE Practice Test 44 — 15 Questions

Question 1

You need to create a custom metric in Cloud Monitoring that measures the number of HTTP requests to your application each second. Which metric type should you use?

Accepted Answer

DELTA. A DELTA metric reports the change in a value over time, which is appropriate for counting events per second. GAUGE reports a value at a point in time, and CUMULATIVE reports an accumulating total.

Answer

GAUGE

Answer

CUMULATIVE

Answer

COUNT

Question 2

A DevOps engineer is optimizing a GKE workload that is CPU-bound. They want to ensure proper resource allocation to improve performance. Which three actions should they take? (Choose THREE).

Accepted Answer

Set Horizontal Pod Autoscaler based on CPU utilization. VPA recommends resource requests/limits; cluster autoscaler ensures node capacity; HPA handles scaling based on CPU.

Answer

Set min instances on Cloud Run

Answer

Use preemptible nodes

Question 3

Which TWO metrics are examples of GAUGE metric types? (Select 2)

Accepted Answer

CPU utilization. GAUGE metrics represent a value at a point in time, such as CPU usage or memory usage. Request count is a DELTA or CUMULATIVE metric, and disk read bytes per second is a DELTA metric.

Answer

Total number of active users

Answer

Disk read bytes per second

Answer

Request count per second

Question 4

A company uses Cloud Profiler to analyze CPU usage of a Java application running on GKE. They see a function consuming 40% of CPU time. To investigate further, they want to see the exact line numbers in the source code. What should they do?

Accepted Answer

Enable debug symbols in the application and upload source code to Cloud Profiler. Cloud Profiler requires the source code to be available and configured for symbolication. The flame graph shows function names, but line numbers require source context.

Answer

Use Cloud Trace to trace the function call

Answer

Enable structured logging for the application

Answer

Switch to a different profiling tool

Question 5

You are designing a monitoring strategy for a hybrid cloud environment where on-premises services send metrics to Cloud Monitoring via the Monitoring API. The on-premises network has limited outbound connectivity and can only reach Google Cloud through a few static IPs. You need to ensure that custom metrics are ingested reliably and cost-effectively. Which THREE actions should you take? (Choose 3)

Accepted Answer

Use a service account with roles/monitoring.metricWriter on the on-premises application. To handle limited connectivity, using a Cloud VPN tunnel ensures secure and reliable connectivity. The Monitoring API requires authentication via a service account, and a VPC Service Controls perimeter can protect data exfiltration. Pushing metrics from on-premises is the standard approach. Pulling is not supported.

Answer

Enable Cloud NAT to provide outbound connectivity

Answer

Use Cloud Monitoring's Agent to pull metrics from on-premises

Question 6

You need to reduce the cost of Cloud Monitoring custom metrics while retaining the ability to create alerts on those metrics. Which THREE actions should you take? (Choose three.)

Accepted Answer

Use GAUGE metric kind instead of DELTA or CUMULATIVE. Custom metrics are charged per sample. Using GAUGE metrics reduces sample count as DELTA and CUMULATIVE require periodic reporting. Increasing the sampling interval reduces volume. Using OpenTelemetry allows batching. Setting longer alignment periods in alerts reduces query cost. Using distribution metrics may increase cost due to multiple buckets.

Answer

Set a longer alignment period in your alerting policy

Answer

Use distribution metric kind to aggregate data

Question 7

You need to monitor the number of ERROR-level log entries in Cloud Logging and trigger an alert when the count exceeds 100 in 5 minutes. Which approach should you use?

Accepted Answer

Create a log-based counter metric with filter 'severity=ERROR' and then set an alert on that metric. Log-based metrics allow you to define a counter metric that counts log entries matching a filter. You can then create an alerting policy on that metric. Logs-based alerting directly from logs is not available; you must use a log-based metric.

Answer

Create a log-based distribution metric and set an alert on the count

Answer

Use a logs-based alerting policy directly from Cloud Logging

Answer

Export logs to BigQuery and run a scheduled query to trigger a custom alert

Question 8

You want to visualize the 99th percentile latency of a service on a dashboard. The metric is a distribution metric. Which reducer should you use in the chart configuration?

Accepted Answer

99TH_PERCENTILE. The 99th percentile is a specific percentile, not a simple aggregation like MEAN, SUM, or COUNT. Cloud Monitoring charts can use percentile reducers. Among the options, '99TH_PERCENTILE' is the correct one.

Answer

MEAN

Answer

SUM

Answer

COUNT

Question 9

A company uses Cloud Spanner for a global application. They notice high write latency and occasional hotspotting on a table with a monotonically increasing integer primary key. Which schema design change would best prevent hotspotting while maintaining read performance?

Accepted Answer

Use bit-reversed indexes for the primary key.. Using a hash prefix of the primary key distributes writes across different splits, preventing hotspots. UUIDs also work, but bit-reverse is better for sequential keys. Interleaved tables do not help with hotspotting.

Answer

Use a UUID as the primary key.

Answer

Create an interleaved table with the primary key as the parent.

Answer

Add a secondary index on the primary key.

Question 10

A DevOps engineer wants to create a dashboard that shows the number of 5xx errors per service over time. The errors are logged in Cloud Logging. What is the most efficient way to create this dashboard?

Accepted Answer

Create a log-based counter metric for 5xx errors, then add a chart in Cloud Monitoring using that metric.. First, create a log-based metric that counts log entries with severity ERROR and status code 5xx. Then, in Cloud Monitoring, create a dashboard with a chart using that metric, grouped by service. This avoids querying logs directly and provides a reusable metric.

Answer

Write a custom application that sends the error count to Cloud Monitoring as a custom metric via API.

Answer

Export logs to BigQuery and use Data Studio to create a dashboard.

Answer

Use Cloud Logging's metrics explorer to create a chart and embed it in a dashboard.

Question 11

Which notification channel is supported by Cloud Monitoring for alerting without additional third-party integrations?

Accepted Answer

Email

Answer

PagerDuty

Answer

Slack

Answer

SMS

Question 12

Your team is using Cloud Monitoring dashboards to monitor a multi-service architecture. You want to manage dashboards as code using Terraform. Which approach should you use to create a dashboard?

Accepted Answer

Use the google_monitoring_dashboard Terraform resource with a dashboard JSON configuration. Cloud Monitoring dashboards can be managed via the Dashboard API using a JSON configuration. Terraform has a google_monitoring_dashboard resource that accepts a JSON or YAML representation of the dashboard. The gcloud command only supports exporting/importing, not declarative management.

Answer

Use the Monitoring API directly from Terraform with a custom provider

Answer

Create the dashboard manually and export it as a JSON file, then import into Terraform

Answer

Use gcloud alpha monitoring dashboards create command with a YAML file

Question 13

You want to continuously profile the CPU usage of a production application running on Compute Engine to identify performance bottlenecks. Which Google Cloud service should you use?

Accepted Answer

Cloud Profiler. Cloud Profiler provides continuous, low-overhead profiling for CPU, heap, threads, and contention. It uses statistical sampling and presents results in flame graphs. Cloud Trace is for distributed tracing, not profiling.

Answer

Cloud Trace

Answer

Cloud Monitoring

Answer

Error Reporting

Question 14

An application running on Cloud Run is experiencing high tail latency. The team wants to visualize which functions are consuming the most CPU time. Which Google Cloud tool should they use and how should they instrument the application?

Accepted Answer

Cloud Profiler with OpenTelemetry or Profiler agent. Cloud Profiler provides continuous profiling (CPU, heap, etc.) with low overhead. For Cloud Run, the Profiler can be enabled via the Cloud Profiler Java agent or OpenTelemetry.

Answer

Cloud Monitoring with custom metrics for CPU

Answer

Cloud Trace with automatic instrumentation

Answer

Cloud Logging with structured logs

Question 15

Which Google Cloud service can be used to inject artificial delays into HTTP traffic to test service resilience?

Accepted Answer

Traffic Director. Traffic Director supports fault injection, including delay and abort faults, for HTTP traffic. This is used in chaos engineering to test service resilience. Chaos Mesh is for Kubernetes, but Traffic Director is the managed service for traffic management.

Answer

Cloud Endpoints

Answer

Cloud Armor

Answer

Cloud Load Balancing