Monitoring with Atlas metrics in MongoDB - Practice Problems & Coding Challenges

Challenge - 5 Problems

💻 Command Output

intermediate

Understanding Atlas Metrics Query Output

You run the following Atlas API command to fetch CPU usage metrics for your cluster over the last hour:

curl -X GET 'https://cloud.mongodb.com/api/atlas/v1.0/groups/{GROUP-ID}/processes/{HOST}:{PORT}/measurements?granularity=PT1M&period=PT1H&metrics=CPU_USER'

What type of data will the response contain?

A A JSON object containing only the cluster name and status without metrics.

B A JSON object with timestamped CPU usage percentages sampled every minute for the last hour.

C An error message indicating invalid API endpoint.

D A plain text list of CPU usage values without timestamps.

💡 Hint

Think about what 'measurements' and 'granularity' parameters imply in the API call.

The Atlas API returns metrics data as JSON with timestamps and values. The granularity=PT1M means data points every minute, and period=PT1H means last hour.

🧠 Conceptual

intermediate

Choosing Metrics for Performance Monitoring

Which Atlas metric is best to monitor if you want to detect slow query performance caused by disk I/O bottlenecks?

A DISK_QUEUE_DEPTH

B CONNECTIONS_CURRENT

C NETWORK_BYTES_IN

D OPCOUNTERS_COMMAND

💡 Hint

Disk queue depth indicates how many operations are waiting for disk access.

DISK_QUEUE_DEPTH shows how many disk operations are waiting, which directly relates to disk I/O bottlenecks affecting query speed.

❓ Troubleshoot

advanced

Diagnosing Missing Metrics in Atlas Dashboard

You notice that your Atlas cluster dashboard is not showing any data for the 'MEMORY_RESIDENT' metric for the past day. What is the most likely cause?

A Your API key lacks permissions to view memory metrics.

B The MEMORY_RESIDENT metric is deprecated and no longer available.

C The cluster is paused or stopped, so no metrics are collected.

D The Atlas UI has a bug and needs a browser refresh.

💡 Hint

Think about what happens to metrics collection when the cluster is not running.

If the cluster is paused or stopped, Atlas cannot collect or report metrics, so the dashboard will show no data.

🔀 Workflow

advanced

Setting Up Alerts for High CPU Usage

You want to create an alert in Atlas that triggers when CPU usage exceeds 80% for more than 5 minutes. Which steps correctly describe this setup?

A Enable cluster auto-scaling to automatically reduce CPU usage above 80%.

B Modify the cluster configuration to limit CPU usage to 80%.

C Set up a backup schedule to run when CPU usage is above 80%.

D Create an alert configuration with metric CPU_USER, threshold 80%, duration 5 minutes, and notification channel.

💡 Hint

Alerts are based on metric thresholds and notification settings.

Atlas alerts are configured by selecting a metric, setting a threshold and duration, and linking to notification channels like email or Slack.

✅ Best Practice

expert

Optimizing Atlas Monitoring Costs

To reduce costs while maintaining effective monitoring in Atlas, which practice is recommended?

A Adjust metric granularity to a coarser level like 5 minutes instead of 1 minute for less frequent data points.

B Disable all metrics except CPU usage to minimize data collection.

C Increase the retention period of metrics to keep data longer for analysis.

D Run multiple monitoring agents on the same cluster to ensure data accuracy.

💡 Hint

Less frequent data points reduce storage and processing costs.

Using coarser granularity reduces the number of data points collected and stored, lowering monitoring costs without losing essential trend information.