[FA] Support ansible apm

[coignetp]

What does this PR do?

Enable apm packages tests now that the fix is released https://github.com/DataDog/ansible-datadog/releases/tag/4.27.0

Motivation

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

[agent-platform-auto-pr[bot]]

[Fast Unit Tests Report]

On pipeline 44894018 (CI Visibility). The following jobs did not run any unit tests:

Jobs:

• tests_deb-arm64-py3
• tests_deb-x64-py3
• tests_flavor_dogstatsd_deb-x64
• tests_flavor_heroku_deb-x64
• tests_flavor_iot_deb-x64
• tests_rpm-arm64-py3
• tests_rpm-x64-py3
• tests_windows-x64

If you modified Go files and expected unit tests to run in these jobs, please double check the job logs. If you think tests should have been executed reach out to #agent-devx-help

[pr-commenter[bot]]

Test changes on VM

Use this command from test-infra-definitions to manually test this PR changes on a VM:

inv create-vm --pipeline-id=44894018 --os-family=ubuntu

Note: This applies to commit 9b07edfe

[pr-commenter[bot]]

Regression Detector

Regression Detector Results

Run ID: 54111320-b305-431e-af54-dade768eeac7 Metrics dashboard Target profiles

Baseline: 06338e9bfc7594ed45344e69ff29a01945bacded Comparison: 9b07edfea5945d97e373f16c633ee40d51fa4455

Performance changes are noted in the perf column of each table:

• ✅ = significantly better comparison variant performance
• ❌ = significantly worse comparison variant performance
• ➖ = no significant change in performance

No significant changes in experiment optimization goals

Confidence level: 90.00% Effect size tolerance: |Δ mean %| ≥ 5.00%

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	trials	links
➖	basic_py_check	% cpu utilization	+1.61	[-1.08, +4.30]	1	Logs
➖	pycheck_lots_of_tags	% cpu utilization	+1.59	[-0.92, +4.10]	1	Logs
➖	otel_to_otel_logs	ingress throughput	+0.48	[-0.32, +1.29]	1	Logs
➖	idle	memory utilization	+0.23	[+0.18, +0.28]	1	Logs
➖	file_tree	memory utilization	+0.15	[+0.05, +0.26]	1	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	1	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.01	[-0.09, +0.08]	1	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	-0.26	[-0.31, -0.21]	1	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	-0.41	[-1.16, +0.35]	1	Logs

Bounds Checks

perf	experiment	bounds_check_name	replicates_passed
✅	idle	memory_usage	10/10

Explanation

A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".

For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:

1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

2. Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.

3. Its configuration does not mark it "erratic".