Cost optimization at scale in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to set the budget alert threshold to 80%.

MLOps

budget_alert_threshold = [1]

Drag options to blanks, or click blank then click option'

A8

B80

C1.2

D0.8

Attempts:

3 left

💡 Hint

Common Mistakes

Using 80 instead of 0.8 causes errors because the system expects a decimal.

Using 8 or 1.2 are invalid percentages.

The budget alert threshold is a decimal representing the percentage. 0.8 means 80%.

2fill in blank

medium

Complete the code to create a cost optimization rule that stops idle compute instances after 30 minutes.

MLOps

optimization_rule = {'action': 'stop', 'condition': 'idle_time > [1]'}

Drag options to blanks, or click blank then click option'

A30

B60

C15

D45

Attempts:

3 left

💡 Hint

Common Mistakes

Using 60 or 45 minutes delays stopping too long.

Using 15 minutes stops instances too early.

The rule stops instances after 30 minutes of idle time, so the condition must be 'idle_time > 30'.

3fill in blank

hard

Fix the error in the code to calculate monthly cost savings by multiplying hourly savings by hours in a month.

MLOps

monthly_savings = hourly_savings [1] 720

Drag options to blanks, or click blank then click option'

A-

B+

C*

D/

Attempts:

3 left

💡 Hint

Common Mistakes

Using + or - changes the meaning and causes wrong results.

Using / divides instead of multiplying.

To get monthly savings, multiply hourly savings by total hours (720) in a 30-day month.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps instance IDs to their cost if cost is greater than 100.

MLOps

costly_instances = {id: cost for id, cost in instances.items() if cost [1] [2]

Drag options to blanks, or click blank then click option'

A>

B100

C<

D50

Attempts:

3 left

💡 Hint

Common Mistakes

Using < or 50 filters the wrong instances.

Swapping the operator and value causes logic errors.

The comprehension filters instances with cost greater than 100, so use > and 100.

5fill in blank

hard

Fill all three blanks to create a dictionary of instance names in uppercase mapped to their usage hours, filtering only those with usage over 200 hours.

MLOps

filtered_usage = [1]: [2] for [3], usage in usage_data.items() if usage > 200

Drag options to blanks, or click blank then click option'

Aname.upper()

Busage

Cname

Dinstance

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'instance' instead of 'name' causes variable errors.

Not converting name to uppercase changes the key format.

Using wrong values for keys or values breaks the dictionary.

The dictionary keys are uppercase names, values are usage hours, and the loop variable is 'name'.

Practice

1. What is the main goal of cost optimization at scale in MLOps?

easy

A. To increase the number of servers regardless of workload

B. To avoid monitoring costs after deployment

C. To use only the most expensive cloud resources

D. To save money by matching resource use to workload needs

Solution

1. Step 1: Understand cost optimization purpose

   Cost optimization means using resources efficiently to reduce expenses.

2. Step 2: Match resources to workload needs

   Adjusting resources based on workload avoids waste and saves money.

3. Final Answer:

   To save money by matching resource use to workload needs -> Option D

4. Quick Check:

   Cost optimization = save money by matching resources [OK]

Hint: Cost optimization means using just enough resources [OK]

Common Mistakes:

• Thinking more servers always means better
• Ignoring cost monitoring after deployment
• Assuming expensive resources are always best

2. Which of the following is a correct way to specify a spot instance in a Kubernetes pod spec for cost savings?

easy

A. affinity:
nodeAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
nodeSelectorTerms:
- matchExpressions:
- key: "kubernetes.io/lifecycle"
operator: In
values:
- spot

B. tolerations:
- key: "spot-instance"
operator: Exists
effect: NoSchedule

C. nodeSelector:
kubernetes.io/instance-type: spot

D. resources:
requests:
cpu: "spot"
memory: "spot"

Solution

1. Step 1: Understand spot instance labeling in Kubernetes

   Spot instances are often labeled with lifecycle=spot to identify cheaper nodes.

2. Step 2: Check node affinity syntax

   affinity: nodeAffinity: requiredDuringSchedulingIgnoredDuringExecution: nodeSelectorTerms: - matchExpressions: - key: "kubernetes.io/lifecycle" operator: In values: - spot correctly uses nodeAffinity with matchExpressions to select nodes labeled as spot.

3. Final Answer:

   affinity with nodeSelectorTerms matching lifecycle=spot label -> Option A

4. Quick Check:

   Spot instance selection uses nodeAffinity with lifecycle=spot label [OK]

Hint: Use nodeAffinity with lifecycle=spot label for spot nodes [OK]

Common Mistakes:

• Using nodeSelector with wrong label key
• Setting resource requests to 'spot' (invalid)
• Confusing tolerations with node affinity

3. Given this autoscaling configuration snippet for a Kubernetes deployment:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: ml-model-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: ml-model
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 50

What happens when CPU usage rises to 75%?

medium

A. The number of pods will increase up to a maximum of 10

B. The number of pods will decrease to 2

C. The deployment will restart pods

D. Nothing changes because CPU target is 50%

Solution

1. Step 1: Understand Horizontal Pod Autoscaler (HPA) behavior

   HPA increases pods when CPU usage exceeds target utilization to balance load.

2. Step 2: Analyze CPU usage vs target

   CPU is at 75%, above the 50% target, so HPA will scale up pods up to maxReplicas (10).

3. Final Answer:

   The number of pods will increase up to a maximum of 10 -> Option A

4. Quick Check:

   CPU > target utilization triggers pod scaling up [OK]

Hint: CPU above target utilization triggers scaling up [OK]

Common Mistakes:

• Thinking pods scale down when CPU rises
• Confusing pod restart with scaling
• Assuming no change if CPU exceeds target

4. You have a cloud cost alert system but it keeps sending false alarms about overspending. What is the most likely cause?

medium

A. The cloud provider is charging incorrectly

B. The alert thresholds are set too low or too sensitive

C. The system is not connected to the billing API

D. The cost data is updated only once a year

Solution

1. Step 1: Understand alert system sensitivity

   Alerts trigger when costs exceed set thresholds; too low thresholds cause false alarms.

2. Step 2: Evaluate other options

   Incorrect charges or missing billing data cause different issues, not false alarms.

3. Final Answer:

   The alert thresholds are set too low or too sensitive -> Option B

4. Quick Check:

   Low alert thresholds cause false alarms [OK]

Hint: Check alert thresholds if false alarms occur [OK]

Common Mistakes:

• Blaming cloud provider without proof
• Ignoring alert configuration
• Assuming billing API is always connected

5. You want to reduce costs for a large ML training job that runs daily on cloud GPUs. Which combined strategy best optimizes cost at scale?

hard

A. Run training on CPUs to avoid GPU costs without changing code

B. Use only on-demand GPU instances and disable autoscaling

C. Use spot GPU instances with checkpointing and autoscaling to handle interruptions

D. Schedule training during peak hours to use full capacity

Solution

1. Step 1: Identify cost-saving options for GPU jobs

   Spot instances are cheaper but can be interrupted; checkpointing saves progress.

2. Step 2: Combine autoscaling with spot instances and checkpointing

   Autoscaling adjusts resources; checkpointing prevents data loss on interruptions.

3. Step 3: Evaluate other options

   On-demand is costly; CPUs are slower; peak hours usually cost more.

4. Final Answer:

   Use spot GPU instances with checkpointing and autoscaling to handle interruptions -> Option C

5. Quick Check:

   Spot + checkpoint + autoscale = best cost optimization [OK]

Hint: Combine spot instances with checkpointing and autoscaling [OK]

Common Mistakes:

• Ignoring interruptions on spot instances
• Using expensive on-demand only
• Running on CPUs without code changes
• Scheduling during costly peak hours