### **Top 100 Flask Developer Interview Questions & Answers (Part 4: Questions 301–400)**

---

#### **301. How do you deploy Flask on AWS Elastic Beanstalk with Docker?**
**Answer:**
1. Create `Dockerfile` (see Q145)
2. Add `elasticbeanstalk` config:

# .ebextensions/flask.config
option_settings:
  aws:elasticbeanstalk:container:docker:environment:
    FLASK_ENV: production
  aws:elasticbeanstalk:application:environment:
    SECRET_KEY: <%= ENV['SECRET_KEY'] %>

3. Deploy: `eb deploy --staged`

---

#### **302. How do you configure AWS RDS IAM authentication for Flask?**
**Answer:**

import boto3
from sqlalchemy.engine import URL

client = boto3.client('rds')
token = client.generate_db_auth_token(
    DBHostname=os.getenv('RDS_HOST'),
    Port=5432,
    DBUsername='iam_user',
    Region='us-east-1'
)

app.config['SQLALCHEMY_DATABASE_URI'] = URL.create(
    "postgresql+psycopg2",
    username="iam_user",
    password=token,
    host=os.getenv('RDS_HOST'),
    database="mydb"
)

---

#### **303. How do you implement Cloudflare rate limiting for Flask apps?**
**Answer:** Configure in Cloudflare dashboard:
1. **Rules > Rate limiting**:
- Path: `/api/*`
- Threshold: `100 requests/5 minutes`
- Action: `Block`
2. Verify headers in Flask:

@app.before_request
def check_cloudflare():
    if request.headers.get('CF-RAY'):
        g.cloudflare = True

---

#### **304. How do you set up AWS X-Ray tracing for Flask?**
**Answer:**

from aws_xray_sdk.core import xray_recorder
from aws_xray_sdk.ext.flask.middleware import XRayMiddleware

xray_recorder.configure(service='Flask App')
XRayMiddleware(app, xray_recorder)

# Add plugins for AWS services
from aws_xray_sdk.ext.dbapi import XRayTracedConn
conn = XRayTracedConn(psycopg2.connect, 'postgresql://...')

# Manual subsegments
with xray_recorder.in_subsegment('external_call'):
    requests.get('https://api.example.com')

---

#### **305. How do you handle "cold starts" in AWS Lambda Flask deployments?**
**Answer:** Mitigation strategies:
1. **Provisioned concurrency**: Keep warm instances
2. **Optimize package size**: Remove unused dependencies
3. **Lazy imports**:

def lambda_handler(event, context):
    from app import create_app  # Import inside handler
    return Mangum(create_app())(event, context)

4. **Keep-alive pings**: Scheduled CloudWatch Events

---

#### **306. How do you configure GCP Cloud SQL Auth Proxy for Flask?**
**Answer:**
1. Start proxy:

cloud_sql_proxy -instances=my-project:us-central1:my-db=tcp:5432

2. Configure Flask:

app.config['SQLALCHEMY_DATABASE_URI'] = (
    f"postgresql+pg8000://user:password@127.0.0.1:5432/mydb"
)

*Alternative:* Use [Cloud SQL Python Connector](https://pypi.org/project/cloud-sql-python-connector/)

---

#### **307. How do you implement Azure Active Directory auth in Flask?**
**Answer:** Use `flask-oidc`:

oidc = OpenIDConnect(app)
@app.route('/login')
@oidc.require_login
def login():
    return redirect(url_for('dashboard'))

@app.route('/dashboard')
@oidc.accept_token(True)
def dashboard():
    return f"Hello {g.oidc_token_info['sub']}"

*Configure:*

OIDC_CLIENT_SECRETS = {
  "web": {
    "client_id": "AZURE_CLIENT_ID",
    "client_secret": "AZURE_CLIENT_SECRET",
    "auth_uri": "https://login.microsoftonline.com/common/oauth2/v2.0/authorize",
    "token_uri": "https://login.microsoftonline.com/common/oauth2/v2.0/token"
  }
}

---

#### **308. How do you set up Google Cloud Trace for Flask?**
**Answer:**

from opentelemetry import trace
from opentelemetry.exporter.cloud_trace import CloudTraceSpanExporter
from opentelemetry.instrumentation.flask import FlaskInstrumentor
from opentelemetry.propagate import set_global_textmap
from opentelemetry.propagators.cloud_trace_propagator import (
    CloudTraceFormatPropagator
)

set_global_textmap(CloudTraceFormatPropagator())
FlaskInstrumentor().instrument_app(app)
trace.get_tracer_provider().add_span_processor(
    CloudTraceSpanExporter()
)

---

#### **309. How do you configure AWS ALB sticky sessions for Flask?**
**Answer:**
1. Enable **stickiness** in ALB target group:
- Stickiness type: `app_cookie`
- Cookie name: `session_id`
2. Configure Flask to use cookie-based sessions:

app.config.update(
    SESSION_COOKIE_NAME='session_id',
    SESSION_COOKIE_SECURE=True
)

---

#### **310. How do you implement "canary deployments" for Flask apps on Kubernetes?**
**Answer:**
1. Deploy new version with `canary` label:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: flask-app-canary
spec:
  replicas: 2
  selector:
    matchLabels:
      app: flask-app
      version: canary

2. Configure Istio route rules:

apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
spec:
  http:
  - route:
    - destination:
        host: flask-app
        subset: v1
      weight: 90
    - destination:
        host: flask-app
        subset: canary
      weight: 10

---

#### **311. How do you configure Prometheus metrics for Flask?**
**Answer:** Use `prometheus_flask_exporter`:

from prometheus_flask_exporter import PrometheusMetrics

metrics = PrometheusMetrics(app)
metrics.info('app_info', 'Application info', version='1.0.0')

@metrics.do_not_track()
@metrics.summary('request_latency_seconds', 'Request latency')
@app.route('/data')
def data():
    # ...

*Scrape via:*

# prometheus.yml
scrape_configs:
  - job_name: 'flask'
    static_configs:
      - targets: ['flask-app:5000']

---

#### **312. How do you track custom metrics in AWS CloudWatch?**
**Answer:**

import boto3
from flask import g

cloudwatch = boto3.client('cloudwatch')

@app.before_request
def start_timer():
    g.start = time.time()

@app.after_request
def log_request(response):
    duration = time.time() - g.start
    cloudwatch.put_metric_data(
        Namespace='FlaskApp',
        MetricData=[{
            'MetricName': 'RequestDuration',
            'Value': duration,
            'Unit': 'Seconds',
            'Dimensions': [{'Name': 'Endpoint', 'Value': request.path}]
        }]
    )
    return response

---

#### **313. How do you implement "blue-green deployments" on Heroku?**
**Answer:**
1. Deploy to staging app: `git push heroku-staging main`
2. Run migrations: `heroku run flask db upgrade --app staging-app`
3. Swap URLs:

heroku pipelines:promote --app production-pipeline --to production

4. Verify then destroy old release

---

#### **314. How do you configure Datadog APM for Flask?**
**Answer:**

from ddtrace import tracer, patch_all
patch_all()

@app.route('/process')
def process():
    with tracer.trace("custom_operation"):
        # ...
    return "Done"

*Environment:*

DD_SERVICE=flask-app
DD_ENV=production
DD_TRACE_AGENT_URL=http://datadog:8126

---

#### **315. How do you set up GitHub Actions CI/CD for Flask?**
**Answer:** `.github/workflows/ci.yml`:

name: Flask CI/CD
on:
  push:
    branches: [main]

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.10'
      - name: Install dependencies
        run: |
          python -m pip install --upgrade pip
          pip install -r requirements.txt
      - name: Run tests
        run: pytest
      - name: Deploy to AWS
        if: github.ref == 'refs/heads/main'
        env:
          AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
        run: eb deploy production

---

#### **316. How do you implement "feature branch deployments" on Vercel?**
**Answer:** For Flask APIs (via serverless functions):
1. Structure project:

/api
  /__vercel
    output
      static
      config.json
  hello.py

2. `hello.py`:

def handler(request):
    return {"body": "Hello from feature branch!", "statusCode": 200}

3. Configure `vercel.json`:

{
  "builds": [
    { "src": "api/hello.py", "use": "@vercel/python" }
  ]
}

---

#### **317. How do you configure log rotation for Gunicorn?**
**Answer:**

gunicorn app:app \
  --access-logfile - \
  --error-logfile gunicorn.log \
  --log-config <(cat <<EOF
[loggers]
keys = root

[handlers]
keys = file

[formatters]
keys = generic

[logger_root]
level = INFO
handlers = file

[handler_file]
class = handlers.RotatingFileHandler
args = ('gunicorn.log', 'a', 100*1024*1024, 10)
formatter = generic

[formatter_generic]
format = %(asctime)s [%(process)d] [%(levelname)s] %(message)s
EOF
)

---

#### **318. How do you implement "circuit breakers" for external APIs in production?**
**Answer:** With `pybreaker` + statsd:

import statsd
from pybreaker import CircuitBreaker

breaker = CircuitBreaker(
    fail_max=5,
    reset_timeout=60,
    exclude=[requests.HTTPError]
)

@breaker
def call_external():
    statsd.increment("external_api.calls")
    return requests.get("https://api.example.com")

# Track state changes
breaker.state_change += lambda old, new: statsd.gauge(
    "external_api.circuit_breaker", 
    1 if new.name == "open" else 0
)

---

#### **319. How do you set up Grafana alerts for Flask errors?**
**Answer:**
1. Push logs to Loki:

from grafana_loki import LokiHandler
handler = LokiHandler(url="http://loki:3100/loki/api/v1/push", tags={"app": "flask"})
app.logger.addHandler(handler)

2. Create alert rule in Grafana:

count_over_time(
  {app="flask"} |~ "ERROR" [5m]
) > 10

---

#### **320. How do you configure Kubernetes liveness probes for Flask?**
**Answer:**

livenessProbe:
  httpGet:
    path: /health
    port: 5000
  initialDelaySeconds: 15
  periodSeconds: 20
  failureThreshold: 3

# In Flask:
@app.route('/health')
def health():
    try:
        db.engine.execute('SELECT 1')
        return 'OK', 200
    except Exception:
        return 'DB connection failed', 500

---

#### **321. How do you implement "distributed tracing" across Flask and Celery?**
**Answer:** Propagate trace context:

# In Flask
from opentelemetry.propagate import inject

@app.route('/task')
def task():
    headers = {}
    inject(headers)  # Inject trace context
    process_task.apply_async(kwargs={'headers': headers})

# In Celery task
from opentelemetry.propagate import extract
from opentelemetry.trace import set_span_in_context

@celery.task
def process_task(headers):
    ctx = extract(headers)
    tracer = trace.get_tracer(__name__)
    with tracer.start_as_current_span("process_task", context=ctx):
        # Task logic

---

#### **322. How do you configure AWS CloudFront for Flask static files?**
**Answer:**
1. Set S3 bucket as origin
2. Configure cache behaviors:
- Path pattern: `/static/*`
- TTL: `31536000` (1 year)
- Query string: `Forward all, cache based on all`
3. In Flask:

app.config['STATIC_URL_PATH'] = os.getenv('CLOUDFRONT_URL', '/static')

---

#### **323. How do you implement "real-time logs" with Papertrail?**
**Answer:**

import logging
from logging.handlers import SysLogHandler

handler = SysLogHandler(address=('logsN.papertrailapp.com', PORT))
formatter = logging.Formatter('flask-app: %(message)s')
handler.setFormatter(formatter)
app.logger.addHandler(handler)
app.logger.setLevel(logging.INFO)

---

#### **324. How do you set up "golden signals" monitoring for Flask?**
**Answer:** Track:
1. **Traffic**: `requests_total` (counter)
2. **Errors**: `request_errors_total` (counter)
3. **Latency**: `request_duration_seconds` (histogram)
4. **Saturation**: `worker_utilization` (gauge)
*Implementation:*

from prometheus_client import Counter, Histogram

REQUESTS = Counter('flask_http_requests_total', 'Total HTTP requests', ['method', 'endpoint'])
LATENCY = Histogram('flask_http_request_duration_seconds', 'Request duration', ['endpoint'])

@app.before_request
def before_request():
    g.start = time.time()

@app.after_request
def after_request(response):
    latency = time.time() - g.start
    LATENCY.labels(request.path).observe(latency)
    REQUESTS.labels(request.method, request.path).inc()
    return response

---

#### **325. How do you configure "zero-downtime" deployments on Kubernetes?**
**Answer:**

apiVersion: apps/v1
kind: Deployment
spec:
  replicas: 3
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 25%
      maxUnavailable: 0  # Critical for zero downtime
  template:
    spec:
      containers:
      - name: flask
        readinessProbe:
          httpGet:
            path: /ready
            port: 5000
          initialDelaySeconds: 5
          periodSeconds: 5

---

#### **326. How do you implement "chaos engineering" tests for Flask?**
**Answer:** Use Chaos Toolkit:

# experiment.yml
version: "1.0"
title: "Flask Resilience Test"
method:
  - type: action
    name: "inject-latency"
    provider:
      type: process
      path: "tc"
      arguments: "qdisc add dev eth0 root netem delay 1000ms"
  - type: probe
    name: "check-availability"
    provider:
      type: http
      url: "http://flask-app:5000/health"
      timeout: 30
  - type: action
    name: "restore-network"
    provider:
      type: process
      path: "tc"
      arguments: "qdisc del dev eth0 root netem"

---

#### **327. How do you configure "autoscaling" for Flask on GKE?**
**Answer:**

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: flask-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: flask-app
  minReplicas: 2
  maxReplicas: 10
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Pods
    pods:
      metric:
        name: flask_http_request_duration_seconds
      target:
        type: AverageValue
        averageValue: 100m  # 100ms latency

---

#### **328. How do you implement "log-based metrics" in Google Cloud?**
**Answer:**
1. Create log-based metric in Cloud Logging:
- Name: `flask_errors`
- Filter: `resource.type="gae_app" jsonPayload.level="ERROR"`
2. Alert when:
- `Metric: flask_errors`
- `Condition: > 10 for 5 minutes`

---

#### **329. How do you configure "service mesh" for Flask microservices?**
**Answer:** Istio sidecar configuration:

apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
spec:
  hosts:
  - flask-service
  http:
  - route:
    - destination:
        host: flask-service
        subset: v1
      weight: 90
    - destination:
        host: flask-service
        subset: v2
      weight: 10
    retries:
      attempts: 3
      perTryTimeout: 2s

---

#### **330. How do you implement "database connection pooling" for serverless Flask?**
**Answer:** Use **RDS Proxy** for AWS Lambda:

# In Lambda handler
def lambda_handler(event, context):
    conn = pymysql.connect(
        host=os.getenv('RDS_PROXY_ENDPOINT'),
        user='lambda_user',
        password=os.getenv('DB_PASSWORD'),
        database='mydb',
        cursorclass=pymysql.cursors.DictCursor
    )
    # Execute query...

*Configure RDS Proxy with IAM authentication*

---

#### **331. How do you set up "infrastructure as code" for Flask deployments?**
**Answer:** Terraform example:

# main.tf
module "flask_app" {
  source = "terraform-aws-modules/elastic-beanstalk-environment/aws"

  application_name = "flask-app"
  environment_name = "production"

  solution_stack_name = "64bit Amazon Linux 2 v5.6.4 running Python 3.8"

  setting = [
    {
      namespace = "aws:autoscaling:asg"
      name      = "MinSize"
      value     = "2"
    },
    {
      namespace = "aws:elasticbeanstalk:application:environment"
      name      = "SECRET_KEY"
      value     = var.secret_key
    }
  ]
}

---

#### **332. How do you implement "canary analysis" with Flagger?**
**Answer:**

apiVersion: flagger.app/v1beta1
kind: Canary
spec:
  analysis:
    interval: 1m
    threshold: 10
    maxWeight: 50
    stepWeight: 5
    metrics:
    - name: error-rate
      thresholdRange:
        max: 0.01
      interval: 1m
    - name: latency
      thresholdRange:
        max: 500
      interval: 1m

---

#### **333. How do you configure "distributed tracing" with Jaeger?**
**Answer:**

from jaeger_client import Config

def init_tracer():
    config = Config(
        config={
            'sampler': {'type': 'const', 'param': 1},
            'logging': True,
        },
        service_name='flask-app'
    )
    return config.initialize_tracer()

tracer = init_tracer()

@app.route('/traced')
def traced():
    with tracer.start_span('web_request') as span:
        span.set_tag('http.url', request.url)
        # ...

---

#### **334. How do you implement "real-time performance monitoring" with Sentry?**
**Answer:**

import sentry_sdk
from sentry_sdk.integrations.flask import FlaskIntegration

sentry_sdk.init(
    dsn="https://[e-mail]/0",
    integrations=[FlaskIntegration()],
    traces_sample_rate=0.1  # 10% of transactions
)

@app.route('/transaction')
def transaction():
    with sentry_sdk.start_transaction(op="request", name="Custom Transaction"):
        # ...

---

#### **335. How do you configure "log aggregation" with ELK Stack?**
**Answer:**
1. Filebeat config:

filebeat.inputs:
- type: filestream
  paths:
    - /var/log/gunicorn.log

output.logstash:
  hosts: ["logstash:5044"]

2. Logstash pipeline:

filter {
  grok {
    match => { "message" => "%{TIMESTAMP_ISO8601:timestamp} \[%{NUMBER:pid}\] %{LOGLEVEL:level} %{GREEDYDATA:msg}" }
  }
}

---

#### **336. How do you implement "chaos monkey" for Flask?**
**Answer:** Randomly kill workers:

import os
import random

@app.before_request
def chaos_monkey():
    if random.random() < 0.01:  # 1% chance
        os._exit(1)  # Simulate crash

*Better:* Use controlled chaos via service mesh (Istio fault injection)

---

#### **337. How do you configure "multi-region failover" on AWS?**
**Answer:**
1. Route53 latency-based routing
2. Global Application Load Balancer
3. Aurora Global Database (primary → secondary replication)
4. Health checks:

@app.route('/health')
def health():
    if not db.is_primary():
        return "Read-only replica", 503
    return "OK", 200

---

#### **338. How do you implement "real-time anomaly detection" for Flask metrics?**
**Answer:** Use Prometheus + ML:
1. Export metrics to Prometheus
2. Train model with PyOD:

from pyod.models.knn import KNN
anomaly_detector = KNN()
anomaly_detector.fit(latency_data)

# In alert manager
if anomaly_detector.predict(new_latency) == 1:
    trigger_alert()

---

#### **339. How do you configure "infrastructure monitoring" with Datadog?**
**Answer:**
1. Install Datadog agent:

# datadog-agent.yaml
apiVersion: apps/v1
kind: DaemonSet
spec:
  template:
    spec:
      containers:
      - name: agent
        image: datadog/agent:latest
        env:
        - name: DD_API_KEY
          value: "<YOUR_API_KEY>"

2. Flask integration auto-discovers via service tags

---

#### **340. How do you implement "distributed configuration" with Consul?**
**Answer:**

import consul

c = consul.Consul()
index, config = c.kv.get("flask/config", recurse=True)

app.config.update(
    {item['Key'].split('/')[-1]: item['Value'] for item in config}
)

# Watch for changes
def watch_config():
    index = None
    while True:
        index, data = c.kv.get("flask/config", index=index, recurse=True)
        if data:
            app.config.update(process_config(data))

Thread(target=watch_config).start()

---

#### **341. How do you configure "service discovery" for Flask microservices?**
**Answer:** Use Consul DNS:

import dns.resolver

def get_service_url(service_name):
    answers = dns.resolver.resolve(f'{service_name}.service.consul', 'A')
    ip = random.choice(answers).to_text()
    return f"http://{ip}:5000"

*Alternative:* Use `requests` with Consul template:

consul-template -template "template.ctmpl:service_url" -once

---

#### **342. How do you implement "blueprint-level metrics" in Prometheus?**
**Answer:**

from prometheus_client import Counter

def create_blueprint(name):
    bp = Blueprint(name, __name__)
    counter = Counter(f'{name}_requests_total', 'Requests per blueprint')
    
    @bp.before_request
    def count_requests():
        counter.inc()
    
    return bp

# Usage
admin_bp = create_blueprint('admin')

---

#### **343. How do you configure "encrypted secrets" with HashiCorp Vault?**
**Answer:**

import hvac

client = hvac.Client(url='http://vault:8200')
client.auth.approle.login(
    role_id=os.environ['VAULT_ROLE_ID'],
    secret_id=os.environ['VAULT_SECRET_ID']
)

secret = client.secrets.kv.v2.read_secret_version(
    path='flask-secrets'
)['data']['data']

app.config['SECRET_KEY'] = secret['secret_key']

---

#### **344. How do you implement "real-time config reload" with etcd?**
**Answer:**

import etcd3

client = etcd3.client()
watch_id = None

def watch_config():
    global watch_id
    watch_id = client.add_watch_callback(
        'flask/config', 
        lambda events: update_config(events)
    )

def update_config(events):
    for event in events:
        if isinstance(event, etcd3.events.PutEvent):
            key = event.key.decode()
            value = event.value.decode()
            app.config[key] = value

watch_config()

---

#### **345. How do you configure "distributed tracing" with Zipkin?**
**Answer:**

from flask import request
from py_zipkin.zipkin import zipkin_span, create_http_headers_for_new_span

@app.before_request
def trace_before():
    headers = {k: v for k, v in request.headers if k.lower() in ('x-b3-traceid', 'x-b3-spanid', 'x-b3-parentspanid', 'x-b3-sampled')}
    g.zipkin_trace_id = headers.get('x-b3-traceid', create_zipkin_id())

@zipkin_span(service_name='flask-app', span_name='web_request')
def traced_function():
    # ...

@app.route('/traced')
def traced():
    with traced_function():
        # ...

---

#### **346. How do you implement "real-time error tracking" with Rollbar?**
**Answer:**

import rollbar
from rollbar.contrib.flask import Rollbar

rollbar.init(
    os.getenv('ROLLBAR_ACCESS_TOKEN'),
    environment=os.getenv('FLASK_ENV', 'development')
)

@app.before_first_request
def init_rollbar():
    Rollbar(app)

@app.route('/error')
def error():
    raise Exception("Test error")

---

#### **347. How do you configure "infrastructure as code" with AWS CDK?**
**Answer:** Python CDK example:

from aws_cdk import (
    aws_ecs as ecs,
    aws_ecs_patterns as ecs_patterns
)

class FlaskStack(cdk.Stack):
    def __init__(self, scope: cdk.Construct, id: str, **kwargs) -> None:
        super().__init__(scope, id, **kwargs)
        
        flask_service = ecs_patterns.ApplicationLoadBalancedFargateService(
            self, "FlaskFargateService",
            image=ecs.ContainerImage.from_registry("my-flask-app:latest"),
            memory_limit_mib=1024,
            cpu=512
        )
        
        flask_service.target_group.configure_health_check(
            path="/health"
        )

---

#### **348. How do you implement "real-time log analysis" with Splunk?**
**Answer:**
1. Configure Flask logging:

import splunk_hec_handler

handler = splunk_hec_handler.SplunkHecHandler(
    host='splunk', 
    port=8088,
    token=os.getenv('SPLUNK_TOKEN'),
    index='main'
)
app.logger.addHandler(handler)

2. Create Splunk alerts for error patterns

---

#### **349. How do you configure "multi-cloud deployments" for Flask?**
**Answer:**
1. Abstract cloud services:

class CloudStorage:
    def __init__(self, provider):
        if provider == 'aws':
            self.client = boto3.client('s3')
        elif provider == 'gcp':
            self.client = storage.Client()
    
    def upload(self, file):
        # Provider-specific logic

2. Use feature flags to switch providers per environment

---

#### **350. How do you implement "real-time capacity planning" for Flask?**
**Answer:** Track metrics + predictive scaling:

from sklearn.linear_model import LinearRegression

# Historical data: [timestamp, requests, cpu]
model = LinearRegression().fit(X_train, y_train)

def predict_load(minutes_ahead=10):
    future_time = time.time() + minutes_ahead * 60
    predicted_load = model.predict([[future_time]])
    if predicted_load > current_capacity * 0.8:
        trigger_scale_up()

---

#### **351. How do you configure "serverless Flask" with AWS API Gateway?**
**Answer:** Use `aws-wsgi` adapter:

# lambda_handler.py
import aws_wsgi
from app import app

def lambda_handler(event, context):
    return aws_wsgi.response(app, event, context)

*Deploy with:*

# serverless.yml
functions:
  app:
    handler: lambda_handler.lambda_handler
    events:
      - http: ANY /
      - http: 'ANY {proxy+}'

---

#### **352. How do you implement "real-time dependency tracking" for Flask?**
**Answer:**

import requests
from prometheus_client import Gauge

DEPENDENCY_STATUS = Gauge('dependency_status', 'Dependency health', ['name'])

def check_dependency(name, url):
    try:
        requests.get(url, timeout=2)
        DEPENDENCY_STATUS.labels(name).set(1)
    except:
        DEPENDENCY_STATUS.labels(name).set(0)

# In background thread
def monitor_dependencies():
    while True:
        check_dependency('db', 'http://db:5432/health')
        check_dependency('redis', 'http://redis:6379/ping')
        time.sleep(10)

---

#### **353. How do you configure "infrastructure monitoring" with New Relic?**
**Answer:**

import newrelic.agent

newrelic.agent.initialize('newrelic.ini')
application = newrelic.agent.WSGIApplicationWrapper(app)

# In newrelic.ini
[newrelic]
license_key = YOUR_LICENSE_KEY
app_name = Flask Application
monitor_toolkit = true
transaction_tracer.enabled = true

---

#### **354. How do you implement "real-time feature flag analytics" with StatsD?**
**Answer:**

import statsd

def is_feature_enabled(feature, user):
    enabled = feature_flags.is_enabled(feature, user)
    statsd.increment(f'feature.{feature}.{"enabled" if enabled else "disabled"}')
    return enabled

*Visualize in Grafana:*

sum(rate(feature_my_feature_enabled[5m])) 
  / 
sum(rate(feature_my_feature_total[5m]))

---

#### **355. How do you configure "distributed tracing" with Honeycomb?**
**Answer:**

from honeycomb.opentelemetry import configure_opentelemetry

configure_opentelemetry(
    service_name="flask-app",
    write_key=os.getenv("HONEYCOMB_WRITEKEY"),
    dataset="flask-traces"
)

# Auto-instruments Flask

---

#### **356. How do you implement "real-time canary analysis" with Prometheus?**
**Answer:**

# In metrics
CANARY_REQUESTS = Counter('canary_requests_total', 'Canary requests', ['version'])
CANARY_ERRORS = Counter('canary_errors_total', 'Canary errors', ['version'])

@app.route('/canary')
def canary():
    version = os.getenv('APP_VERSION')
    CANARY_REQUESTS.labels(version).inc()
    try:
        # Test logic
    except Exception as e:
        CANARY_ERRORS.labels(version).inc()
        raise

*Alert rule:*

rate(canary_errors_total{version="v2"}[5m]) 
  / 
rate(canary_requests_total{version="v2"}[5m]) 
  > 
0.01

---

#### **357. How do you configure "infrastructure as code" with Pulumi?**
**Answer:** Python example:

import pulumi
from pulumi_aws import ec2, ecs, elbv2

# Create VPC
vpc = ec2.Vpc("flask-vpc", cidr_block="10.0.0.0/16")

# Create ECS cluster
cluster = ecs.Cluster("flask-cluster")

# Create load balancer
lb = elbv2.LoadBalancer("flask-lb", 
    subnets=[vpc.public_subnet_ids[0], vpc.public_subnet_ids[1]],
    security_groups=[...]
)

# Create service
service = ecs.Service("flask-svc",
    cluster=cluster.arn,
    task_definition=task_definition.arn,
    desired_count=2,
    load_balancers=[{
        "targetGroupArn": target_group.arn,
        "containerName": "flask",
        "containerPort": 5000
    }]
)

---

#### **358. How do you implement "real-time anomaly detection" for error rates?**
**Answer:** Use Prophet forecasting:

from fbprophet import Prophet

def detect_anomalies():
    # Get last 7 days of error data
    df = get_error_metrics()
    
    # Train model
    model = Prophet()
    model.fit(df)
    
    # Predict next hour
    future = model.make_future_dataframe(periods=6, freq='H')
    forecast = model.predict(future)
    
    # Check if current error rate > upper bound
    return current_error_rate > forecast['yhat_upper'].iloc[-1]

---

#### **359. How do you configure "infrastructure monitoring" with Sysdig?**
**Answer:**
1. Install Sysdig agent:

apiVersion: apps/v1
kind: DaemonSet
spec:
  template:
    spec:
      containers:
      - name: sysdig-agent
        image: sysdig/agent
        env:
        - name: ACCESS_KEY
          value: "<YOUR_ACCESS_KEY>"

2. Create alert rules for Flask-specific metrics

---

#### **360. How do you implement "real-time capacity forecasting" for database?**
**Answer:**

import pandas as pd
from statsmodels.tsa.arima.model import ARIMA

def forecast_db_growth():
    # Historical data: date, size_mb
    model = ARIMA(df['size_mb'], order=(1,1,0))
    results = model.fit()
    forecast = results.forecast(steps=30)  # 30 days
    
    if forecast.iloc[-1] > current_capacity * 0.9:
        trigger_resize()

---

#### **361. How do you configure "distributed tracing" with Lightstep?**
**Answer:**

from opentelemetry import trace
from opentelemetry.exporter.lightstep import LightstepOTLPSpanExporter
from opentelemetry.sdk.trace import TracerProvider
from opentelemetry.sdk.trace.export import BatchSpanProcessor

trace.set_tracer_provider(TracerProvider())
trace.get_tracer_provider().add_span_processor(
    BatchSpanProcessor(
        LightstepOTLPSpanExporter(
            access_token=os.getenv("LIGHTSTEP_ACCESS_TOKEN")
        )
    )
)

# Auto-instruments Flask

---

#### **362. How do you implement "real-time feature flag rollback"?**
**Answer:**

from ldclient import get()

@app.route('/flags/<flag_key>/rollback', methods=['POST'])
@require_admin
def rollback_flag(flag_key):
    flag = get().variation_detail(flag_key, None, None)
    if flag.value == "v2":
        get().variation(flag_key, None, "v1")  # Rollback to v1
        statsd.increment(f'flag.{flag_key}.rollback')
        return "Rolled back", 200
    return "Already on v1", 200

---

#### **363. How do you configure "infrastructure monitoring" with Netdata?**
**Answer:**
1. Install Netdata agent:

docker run -d --name=netdata \
  -p 19999:19999 \
  -v /proc:/host/proc:ro \
  -v /sys:/host/sys:ro \
  -v /var/run/docker.sock:/var/run/docker.sock:ro \
  netdata/netdata

2. Configure Flask-specific alerts in `health.d/python.conf`:

alarm: flask_high_latency
  on: flask_http_request_duration_seconds
  units: seconds
  info: High request latency
  warn: $this > 1
  crit: $this > 2

---

#### **364. How do you implement "real-time dependency health checks"?**
**Answer:**

from flask import Blueprint

health_bp = Blueprint('health', __name__)

@health_bp.route('/dependencies')
def dependencies():
    results = {}
    for name, check in DEPENDENCY_CHECKS.items():
        try:
            results[name] = {"status": "ok", "data": check()}
        except Exception as e:
            results[name] = {"status": "error", "message": str(e)}
    
    overall = "ok" if all(r["status"] == "ok" for r in results.values()) else "error"
    return jsonify(status=overall, dependencies=results), 200 if overall == "ok" else 503

---

#### **365. How do you configure "infrastructure as code" with Ansible?**
**Answer:**

# deploy.yml
- name: Deploy Flask app
  hosts: webservers
  vars:
    app_version: "1.2.3"
  tasks:
    - name: Install dependencies
      apt:
        name: "{{ packages }}"
      vars:
        packages:
          - python3-pip
          - gunicorn

    - name: Deploy app
      unarchive:
        src: "https://s3.amazonaws.com/flask-app/{{ app_version }}.zip"
        dest: /opt/flask-app
        remote_src: yes

    - name: Start Gunicorn
      systemd:
        name: flask-app
        state: restarted
        daemon_reload: yes

---

#### **366. How do you implement "real-time canary analysis" with Grafana ML?**
**Answer:**
1. Store metrics in Prometheus
2. Create Grafana ML panel:
- **Algorithm**: Prophet
- **Forecast horizon**: 1h
- **Anomaly threshold**: 2 standard deviations
3. Alert when:

   | actual - predicted | > threshold

---

#### **367. How do you configure "distributed tracing" with Instana?**
**Answer:**

# Automatically instruments Flask when INSTANA_AGENT_KEY is set
import instana

# Environment variables:
# INSTANA_AGENT_KEY=YOUR_KEY
# INSTANA_SERVICE_NAME=flask-app
# INSTANA_ZONE=production

---

#### **368. How do you implement "real-time infrastructure cost monitoring"?**
**Answer:**

import boto3

def get_daily_cost():
    client = boto3.client('ce')
    response = client.get_cost_and_usage(
        TimePeriod={
            'Start': (datetime.today() - timedelta(days=1)).strftime('%Y-%m-%d'),
            'End': datetime.today().strftime('%Y-%m-%d')
        },
        Granularity='DAILY',
        Metrics=['AmortizedCost'],
        GroupBy=[{'Type': 'DIMENSION', 'Key': 'SERVICE'}]
    )
    return response['ResultsByTime'][0]['Groups']

@app.route('/cost')
def cost():
    return jsonify(get_daily_cost())

---

#### **369. How do you configure "infrastructure monitoring" with Grafana Cloud?**
**Answer:**
1. Add remote write to `prometheus.yml`:

remote_write:
  - url: "https://prometheus-us-central1.grafana.net/api/prom/push"
    basic_auth:
      username: YOUR_INSTANCE_ID
      password: YOUR_API_KEY

2. Configure Flask metrics as in Q324

---

#### **370. How do you implement "real-time feature impact analysis"?**
**Answer:**

from statsmodels.stats.proportion import proportions_ztest

def analyze_feature_impact(feature, metric, variant="B"):
    # Get data for control (A) and variant (B)
    a_data = get_metric_data(feature, "A", metric)
    b_data = get_metric_data(feature, variant, metric)
    
    # Z-test for proportions
    count = [sum(a_data), sum(b_data)]
    nobs = [len(a_data), len(b_data)]
    z_stat, p_value = proportions_ztest(count, nobs)
    
    return {
        "p_value": p_value,
        "significant": p_value < 0.05,
        "effect_size": (b_data.mean() - a_data.mean()) / a_data.std()
    }

---

#### **371. How do you configure "serverless tracing" with AWS X-Ray for Lambda?**
**Answer:**

from aws_xray_sdk.core import xray_recorder
from aws_xray_sdk.ext.flask.middleware import XRayMiddleware

# In Lambda handler
def lambda_handler(event, context):
    xray_recorder.configure(service='Flask Lambda')
    XRayMiddleware(app, xray_recorder)
    
    # Manual subsegment for cold start detection
    with xray_recorder.in_subsegment('cold_start_check'):
        if context.get_remaining_time_in_millis() > 90000:
            xray_recorder.put_annotation('cold_start', True)
    
    return Mangum(app)(event, context)

---

#### **372. How do you implement "real-time database load forecasting"?**
**Answer:**

from sklearn.ensemble import RandomForestRegressor

def forecast_db_load():
    # Features: hour_of_day, day_of_week, recent_load
    X, y = get_historical_data()
    
    model = RandomForestRegressor()
    model.fit(X, y)
    
    # Predict next hour
    next_hour = get_next_hour_features()
    predicted_load = model.predict([next_hour])[0]
    
    if predicted_load > MAX_CONNECTIONS * 0.8:
        trigger_read_replicas()

---

#### **373. How do you configure "infrastructure as code" with Terraform Cloud?**
**Answer:**
1. Create `main.tf`:

terraform {
  cloud {
    organization = "my-org"
    workspaces {
      name = "flask-production"
    }
  }
}

module "flask" {
  source = "git::https://github.com/my-org/terraform-flask.git?ref=v1.0.0"
  env    = "prod"
}

2. Set secrets in Terraform Cloud workspace variables

---

#### **374. How do you implement "real-time anomaly detection" for user behavior?**
**Answer:**

from sklearn.ensemble import IsolationForest

def detect_anomalous_users():
    # Features: request_count, error_rate, session_duration
    X = get_user_behavior_data()
    
    model = IsolationForest(contamination=0.01)
    model.fit(X)
    
    anomalies = model.predict(X)
    return User.query.filter(User.id.in_(anomalies == -1)).all()

---

#### **375. How do you configure "distributed tracing" with Signalfx?**
**Answer:**

from signalfx_tracing import auto_instrument

auto_instrument()

# Environment variables:
# SIGNALFX_ACCESS_TOKEN=YOUR_TOKEN
# SIGNALFX_SERVICE_NAME=flask-app
# SIGNALFX_TRACING_ENABLED=true

---

#### **376. How do you implement "real-time canary analysis" with Keptn?**
**Answer:**
1. Define quality gates in `slo.yaml`:

spec_version: '1.0'
comparison:
  compare_with: "single_result"
  include_result_with_score: "pass"
  number_of_comparison_results: 1
objectives:
  - sli: "error_rate"
    key_sli: false
    pass: [ { "criteria": [ "<=+10%" ] } ]
    warning: [ { "criteria": [ "<=800ms" ] } ]

2. Trigger evaluation after canary deployment

---

#### **377. How do you configure "infrastructure monitoring" with LogicMonitor?**
**Answer:**
1. Install LogicMonitor collector
2. Create Python script datasource:

import requests
from logicmonitor_sdk import Collector

def get_flask_metrics():
    resp = requests.get("http://localhost:5000/metrics")
    return {
        "request_rate": resp.json()["requests_per_sec"],
        "error_rate": resp.json()["errors_per_sec"]
    }

Collector().send(get_flask_metrics())

---

#### **378. How do you implement "real-time feature flag impact analysis"?**
**Answer:**

def analyze_flag_impact(flag, metric):
    # Get data for enabled/disabled groups
    enabled = get_metric_data(flag, enabled=True, metric=metric)
    disabled = get_metric_data(flag, enabled=False, metric=metric)
    
    # T-test for means
    t_stat, p_value = ttest_ind(enabled, disabled)
    
    return {
        "p_value": p_value,
        "significant": p_value < 0.05,
        "lift": (enabled.mean() - disabled.mean()) / disabled.mean()
    }

---

#### **379. How do you configure "distributed tracing" with Datadog APM?**
**Answer:**

from ddtrace import tracer

@tracer.wrap(name="custom_operation", service="flask-app")
def process_data():
    # ...

@app.route('/traced')
def traced():
    with tracer.trace("web_request"):
        process_data()
        return "Done"

*Environment:*

DD_SERVICE=flask-app
DD_ENV=production
DD_TRACE_AGENT_URL=http://datadog:8126

---

#### **380. How do you implement "real-time infrastructure cost optimization"?**
**Answer:**

def optimize_costs():
    # Get current utilization
    cpu_util = get_cpu_utilization()
    mem_util = get_memory_utilization()
    
    # Check if over-provisioned
    if cpu_util < 0.3 and mem_util < 0.4:
        current_size = get_instance_size()
        next_size = DOWNSCALE_MAP.get(current_size)
        if next_size:
            trigger_resize(next_size)
    
    # Check for idle resources
    if get_request_count(last_hour=1) == 0:
        trigger_suspend()

---

#### **381. How do you configure "serverless Flask" with Google Cloud Run?**
**Answer:**
1. `Dockerfile`:

FROM python:3.10-slim
WORKDIR /app
COPY requirements.txt .
RUN pip install -r requirements.txt
COPY . .
CMD exec gunicorn --bind :$PORT --workers 1 --threads 8 --timeout 0 app:app

2. Deploy:

gcloud run deploy flask-app \
  --image gcr.io/project-id/flask-app \
  --platform managed \
  --region us-central1 \
  --allow-unauthenticated

---

#### **382. How do you implement "real-time dependency vulnerability scanning"?**
**Answer:**

import requests

def scan_dependencies():
    deps = get_requirements()
    response = requests.post(
        "https://api.snyk.io/test",
        json={"dependencies": deps},
        headers={"Authorization": f"token {SNYK_TOKEN}"}
    )
    return response.json()["vulnerabilities"]

@app.route('/vulnerabilities')
@require_admin
def vulnerabilities():
    return jsonify(scan_dependencies())

---

#### **383. How do you configure "infrastructure as code" with Crossplane?**
**Answer:**

apiVersion: database.example.org/v1alpha1
kind: PostgreSQLInstance
metadata:
  name: flask-db
spec:
  parameters:
    storageGB: 20
    engineVersion: "12"
  writeConnectionSecretToRef:
    name: flask-db-conn
---
apiVersion: compute.example.org/v1alpha1
kind: Deployment
metadata:
  name: flask-app
spec:
  template:
    spec:
      containers:
      - name: flask
        image: flask-app:1.0
        env:
        - name: DB_HOST
          valueFrom:
            secretKeyRef:
              name: flask-db-conn
              key: host

---

#### **384. How do you implement "real-time user behavior clustering"?**
**Answer:**

from sklearn.cluster import KMeans

def cluster_users():
    # Features: session_duration, page_views, conversion
    X = get_user_features()
    
    model = KMeans(n_clusters=3)
    clusters = model.fit_predict(X)
    
    # Assign clusters to users
    for user, cluster in zip(User.query.all(), clusters):
        user.behavior_cluster = cluster
    db.session.commit()

---

#### **385. How do you configure "distributed tracing" with Jaeger in Kubernetes?**
**Answer:**
1. Deploy Jaeger operator:

kubectl create -f https://raw.githubusercontent.com/jaegertracing/jaeger-operator/master/deploy/crds/jaegertracing.io_jaegers_crd.yaml

2. Create Jaeger instance:

apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: flask-jaeger
spec:
  strategy: production
  collector:
    options:
      log-level: info

3. Configure Flask as in Q333

---

#### **386. How do you implement "real-time feature flag risk assessment"?**
**Answer:**

def assess_flag_risk(flag):
    # Factors: user impact, error rate, business criticality
    user_impact = get_affected_users(flag) / total_users()
    error_rate = get_error_rate(flag)
    criticality = FEATURE_CRITICALITY.get(flag, 1.0)
    
    # Risk score (0-10)
    risk = min(10, (user_impact * 5) + (error_rate * 30) + (criticality * 2))
    return {
        "risk_score": risk,
        "high_risk": risk > 7,
        "factors": {
            "user_impact": user_impact,
            "error_rate": error_rate,
            "criticality": criticality
        }
    }

---

#### **387. How do you configure "infrastructure monitoring" with Prometheus Operator?**
**Answer:**

apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: flask-monitor
spec:
  selector:
    matchLabels:
      app: flask-app
  endpoints:
  - port: web
    interval: 15s
    path: /metrics
---
apiVersion: monitoring.coreos.com/v1
kind: PrometheusRule
metadata:
  name: flask-alerts
spec:
  groups:
  - name: flask
    rules:
    - alert: HighErrorRate
      expr: rate(flask_http_request_errors_total[5m]) / rate(flask_http_requests_total[5m]) > 0.05
      for: 10m
      labels:
        severity: critical

---

#### **388. How do you implement "real-time capacity planning" for Redis?**
**Answer:**

def forecast_redis_usage():
    # Get historical memory usage
    timestamps, usage = get_redis_memory_history()
    
    # Linear regression
    model = LinearRegression().fit(
        np.array(timestamps).reshape(-1, 1),
        usage
    )
    
    # Predict when 80% capacity is reached
    current = get_redis_memory()
    capacity = get_redis_maxmemory()
    days_to_full = (capacity * 0.8 - current) / model.coef_[0]
    
    if days_to_full < 7:
        trigger_resize()

---

#### **389. How do you configure "distributed tracing" with OpenTelemetry Collector?**
**Answer:**
1. `otel-collector.yaml`:

receivers:
  otlp:
    protocols:
      grpc:
      http:

exporters:
  jaeger:
    endpoint: "jaeger:14250"
  logging:

service:
  pipelines:
    traces:
      receivers: [otlp]
      exporters: [jaeger, logging]

2. Flask configuration as in Q201

---

#### **390. How do you implement "real-time feature flag compliance checks"?**
**Answer:**

def check_compliance(flag):
    # Check against regulatory requirements
    requirements = get_regulatory_requirements()
    violations = []
    
    for req in requirements:
        if req.applies_to(flag) and not req.is_satisfied(flag):
            violations.append({
                "requirement": req.id,
                "description": req.description
            })
    
    return {
        "compliant": len(violations) == 0,
        "violations": violations
    }

---

#### **391. How do you configure "infrastructure as code" with AWS SAM?**
**Answer:**

# template.yaml
AWSTemplateFormatVersion: '2010-09-09'
Transform: AWS::Serverless-2016-10-31

Resources:
  FlaskFunction:
    Type: AWS::Serverless::Function
    Properties:
      CodeUri: app/
      Handler: lambda_handler.lambda_handler
      Runtime: python3.9
      Events:
        Api:
          Type: Api
          Properties:
            Path: /{proxy+}
            Method: ANY
      Environment:
        Variables:
          SECRET_KEY: !Ref SecretKey
      Tracing: Active

  SecretKey:
    Type: AWS::SSM::Parameter
    Properties:
      Type: SecureString
      Value: !Ref SecretKeyValue

---

#### **392. How do you implement "real-time anomaly detection" for API usage?**
**Answer:**

from sklearn.covariance import EllipticEnvelope

def detect_anomalous_api_usage():
    # Features: requests_per_min, endpoints_used, user_agents
    X = get_api_usage_data()
    
    model = EllipticEnvelope(contamination=0.01)
    model.fit(X)
    
    anomalies = model.predict(X)
    return APIKey.query.filter(APIKey.id.in_(anomalies == -1)).all()

---

#### **393. How do you configure "distributed tracing" with AWS X-Ray for ECS?**
**Answer:**
1. Add X-Ray daemon sidecar:

# task-definition.json
"containerDefinitions": [
  {
    "name": "xray-daemon",
    "image": "amazon/aws-xray-daemon",
    "cpu": 32,
    "memory": 256,
    "portMappings": [{
      "containerPort": 2000,
      "protocol": "udp"
    }]
  },
  {
    "name": "flask-app",
    "image": "flask-app:latest",
    "environment": [{
      "name": "AWS_XRAY_DAEMON_ADDRESS",
      "value": "xray-daemon:2000"
    }]
  }
]

2. Configure Flask as in Q304

---

#### **394. How do you implement "real-time feature flag dependency analysis"?**
**Answer:**

def analyze_flag_dependencies(flag):
    # Get all flags used in same code paths
    dependencies = set()
    for endpoint in get_endpoints_using_flag(flag):
        for other_flag in get_flags_in_endpoint(endpoint):
            if other_flag != flag:
                dependencies.add(other_flag)
    
    # Calculate impact score
    impact = 0
    for dep in dependencies:
        impact += get_affected_users(dep) * FLAG_CRITICALITY[dep]
    
    return {
        "dependencies": list(dependencies),
        "impact_score": impact,
        "high_impact": impact > 1000
    }

---

#### **395. How do you configure "infrastructure monitoring" with Sysdig Secure?**
**Answer:**
1. Install Sysdig agent with policy:

apiVersion: apps/v1
kind: DaemonSet
spec:
  template:
    spec:
      containers:
      - name: sysdig-agent
        env:
        - name: SECURE_POLICY_FILE
          value: |
            - name: Flask Security Policy
              description: Security rules for Flask apps
              rules:
                - rule: "Flask Admin Access"
                  condition: kubernetes.pod.name matches "flask-*" and proc.name = "flask" and fd.name = "/admin"
                  actions: 
                    - log
                    - alert

---

#### **396. How do you implement "real-time database schema drift detection"?**
**Answer:**

def detect_schema_drift():
    # Get current schema
    current_schema = get_db_schema()
    
    # Compare with golden schema
    with open('golden_schema.json') as f:
        golden_schema = json.load(f)
    
    drift = []
    for table in golden_schema:
        if table not in current_schema:
            drift.append(f"Missing table: {table}")
            continue
        
        for column in golden_schema[table]:
            if column not in current_schema[table]:
                drift.append(f"Missing column: {table}.{column}")
    
    if drift:
        send_alert(f"Schema drift detected: {drift}")
    return drift

---

#### **397. How do you configure "distributed tracing" with Azure Application Insights?**
**Answer:**

from opencensus.ext.azure.trace_exporter import AzureExporter
from opencensus.trace.samplers import ProbabilitySampler
from opencensus.trace.tracer import Tracer

tracer = Tracer(
    exporter=AzureExporter(
        instrumentation_key=os.getenv('APPINSIGHTS_INSTRUMENTATIONKEY')
    ),
    sampler=ProbabilitySampler(1.0)
)

@app.route('/traced')
def traced():
    with tracer.span(name="web_request"):
        # ...

---

#### **398. How do you implement "real-time infrastructure compliance checks"?**
**Answer:**

def check_infra_compliance():
    checks = [
        SecurityCheck(
            name="SSH Restriction",
            description="SSH only from bastion",
            check=lambda: get_security_groups().all(sg.ports_open('22') == {'bastion'})
        ),
        SecurityCheck(
            name="Encryption at Rest",
            description="All databases encrypted",
            check=lambda: get_databases().all(db.encrypted)
        )
    ]
    
    results = [check.run() for check in checks]
    return {
        "compliant": all(r.passed for r in results),
        "results": [r.to_dict() for r in results]
    }

---

#### **399. How do you configure "serverless Flask" with Azure Functions?**
**Answer:**
1. `function_app/__init__.py`:

import azure.functions as func
from flask import Flask

app = Flask(__name__)

@app.route('/')
def home():
    return "Hello from Flask!"

def main(req: func.HttpRequest) -> func.HttpResponse:
    return func.WsgiMiddleware(app).handle(req, None)

2. `host.json`:

{
  "version": "2.0",
  "extensions": {
    "http": {
      "routePrefix": ""
    }
  }
}

---

#### **400. How do you implement "real-time cost anomaly detection" for cloud resources?**
**Answer:**

from sklearn.ensemble import IsolationForest

def detect_cost_anomalies():
    # Features: daily_cost, resource_count, usage_hours
    X = get_historical_costs()
    
    model = IsolationForest(contamination=0.05)
    model.fit(X)
    
    anomalies = model.predict(X)
    current_cost = get_current_cost()
    
    if anomalies[-1] == -1 and current_cost > X['cost'].mean() * 2:
        trigger_investigation()
    return anomalies[-1] == -1

---

**Part 5 (Questions 401–500) is the final installment!**