ZopNight Engineer
Operate the 450+ rule recommendations engine.
- Engineer
- 15
- 72
- 15
- ~11 hours
15 modules. 72 lessons. 15 quizzes.
M2.1 The 450+ rule library, explained
Navigate the 450+ recommendation rules.
The 450+ rule library, explained
Navigate the 450+ recommendation rules.
The 450+ rule library, explained: module quiz
M2.2 Reading evidence
Read the Metrics drawer.
Reading evidence
Read the Metrics drawer.
Reading evidence: module quiz
M2.3 Auto-remediation
Trace a recommendation through the 3-step remediation workflow.
Auto-remediation
Trace a recommendation through the 3-step remediation workflow.
Auto-remediation: module quiz
M2.4 VM autoscaling
Configure VM autoscaling policies for AWS ASG, Azure VMSS, GCP MIG, and AWS ECS Application Auto Scaling.
VM autoscaling
Configure VM autoscaling policies for AWS ASG, Azure VMSS, GCP MIG, and AWS ECS Application Auto Scaling.
VM autoscaling: module quiz
M2.5 Adopt-or-replace existing cloud scaling
Distinguish adopt from replace.
Adopt-or-replace existing cloud scaling
Distinguish adopt from replace.
Adopt-or-replace existing cloud scaling: module quiz
M2.6 K8s workload scheduling
Schedule individual K8s workloads across EKS/GKE/AKS without affecting the cluster itself.
K8s workload scheduling
Schedule individual K8s workloads across EKS/GKE/AKS without affecting the cluster itself.
K8s workload scheduling: module quiz
M2.7 Databricks scheduling
Put Databricks workspaces, clusters, instance pools and SQL warehouses on schedules, and know which of them cannot be.
Databricks scheduling
Put Databricks workspaces, clusters, instance pools and SQL warehouses on schedules, and know which of them cannot be.
Databricks scheduling: module quiz
M2.8 Smart Tags
Derive virtual tags from tagging policies, accept or revoke them, apply them back to the cloud, and keep them reconciled.
Smart Tags
Derive virtual tags from tagging policies, accept or revoke them, apply them back to the cloud, and keep them reconciled.
Smart Tags: module quiz
M2.9 Event Readiness
Scale up in advance for a traffic event you know is coming, using the Event Readiness wizard.
Event Readiness
Scale up in advance for a traffic event you know is coming, using the Event Readiness wizard.
Event Readiness: module quiz
M2.10 Cost anomaly detection
Spot a cost spike, work out what caused it, and act on it, across all seven levels the system watches.
Cost anomaly detection
Spot a cost spike, work out what caused it, and act on it, across all seven levels the system watches.
Cost anomaly detection: module quiz
M2.11 Bedrock + ML cost
Identify ML/Bedrock cost patterns.
Bedrock + ML cost
Identify ML/Bedrock cost patterns.
Bedrock + ML cost: module quiz
M2.12 Snowflake
Connect a Snowflake account, read its usage-based cost model, schedule warehouses off-hours, and work the V2-only recommendation catalog.
Snowflake
Connect a Snowflake account, read its usage-based cost model, schedule warehouses off-hours, and work the V2-only recommendation catalog.
Snowflake: module quiz
M2.13 Recommendation lifecycle and verification
Read a recommendation's timeline, separate what the customer claimed from what the estate proves, and understand how ZopNight verifies adoption instead of trusting a status field.
Recommendation lifecycle and verification
Read a recommendation's timeline, separate what the customer claimed from what the estate proves, and understand how ZopNight verifies adoption instead of trusting a status field.
Recommendation lifecycle and verification: module quiz
M2.14 Watch policies
Author your own metric-threshold recommendations when no built-in rule covers the pattern, and understand how they are priced and validated.
Watch policies
Author your own metric-threshold recommendations when no built-in rule covers the pattern, and understand how they are priced and validated.
Watch policies: module quiz
M2.15 Blast radius analysis
See which connected resources an action will affect, and at what severity, before applying a recommendation, an autoscaler change, or a schedule edit.
Blast radius analysis
See which connected resources an action will affect, and at what severity, before applying a recommendation, an autoscaler change, or a schedule edit.