Question 1

What do you value most in team collaboration and culture?

Accepted Answer

Situation: Culture fit question. Task: Authentic values. Action: Psychological safety, clear communication, shared ownership, diverse perspectives, continuous improvement, sustainable pace. Investment in tooling, documentation, blameless incidents. Balance autonomy with alignment....

Question 2

Explain how Infrastructure as Code (IaC) works in AWS and its advantages

Accepted Answer

Architectural logic: IaC = define infra in code (CloudFormation, Terraform, CDK); apply → resources created. Why: Version control; reproducibility; drift detection; faster provisioning. Advantages: Audit trail; documentation as code; pipeline-driven deploys. Cost: No extra; enables right-sizing and tear-down....

Question 3

Explain how you would configure an S3 bucket policy to allow access only from a specific EC2 instance

Accepted Answer

Architectural logic: Bucket policy with Condition on aws:PrincipalArn (EC2 role) or aws:SourceInstance. Example: Condition StringEquals aws:PrincipalArn arn:aws:iam::123456789012:role/MyEC2Role. EC2 must use that role. Why: Restrict access to specific compute; defense in depth....

Question 4

What is the role of AWS KMS in securing sensitive data?

Accepted Answer

**KMS role**: Centralized key management. Creates and controls encryption keys. Used by S3, EBS, RDS, Lambda for encryption. **Why KMS**: Key lifecycle (creation, rotation, deletion); audit via CloudTrail; access control via IAM. **CMK**: Customer-managed keys—you control policy and rotation. AWS-managed keys are simpler but less control. **Best practice**: Use CMK for compliance (audit trail). Enable automatic rotation (annual for symmetric). **Cost**: $1/month per key; $0.03/10K requests....

Question 5

Write a Python program to remove duplicate elements from a list while preserving the original order

Accepted Answer

**Ordered Dedup:** dict.fromkeys(lst) (Py 3.7+) or seen=set(); [x for x in lst if not (x in seen or seen.add(x))]. O(n), hashable elements.

**Unhashable:** O(n²) with list+in, or convert to tuple for hashable key....

Question 6

Are you comfortable with the variable pay structure, and what are your expectations for the base salary?

Accepted Answer

**Situation:** I've worked in roles with variable comp tied to team and company performance.

**Task:** I needed to understand the structure and align expectations with market and value.

**Action:** I researched market rates for this role/level in [location]. I'm open to variable pay when metrics are clear and attainable. I focus on total compensation—base + variable + equity + benefits....

Question 7

Describe the role of a DAG Scheduler in PySpark

Accepted Answer

**Why DAG Scheduler matters**: Translates logical plan to execution; stage boundaries drive optimization. **Role**: Takes RDD DAG; splits into stages at shuffle boundaries; schedules tasks on executors. Handles locality, failures. Part of SparkContext. **Scalability trade-offs**: Stage count affects scheduling overhead; optimize shuffle boundaries. **Cost implications**: Understanding stages = identify bottleneck....

Question 8

How do you ensure fault tolerance when processing large datasets in EMR?

Accepted Answer

**Why it matters**: At scale, design choices directly impact reliability, latency, and cost. Wrong decisions compound across jobs and teams.

EMR fault tolerance: (1) Persist to S3 (durable); (2) Spot instances plus checkpoint; (3) Retry failed steps; (4) Use HA for HDFS if needed; (5) Enable Spark checkpoint for streaming....

Question 9

What are the key differences between Map and Reduce in Spark?

Accepted Answer

**Map**: 1:1 transformation. Each input produces one output. Narrow dependency—no shuffle. Examples: `map`, `filter`, `flatMap`.

**Reduce**: N:1 aggregation. Combines elements; may require shuffle for global aggregation. Wide dependency. Examples: `reduce`, `reduceByKey`, `aggregate`.

**Why reduceByKey > groupByKey**: reduceByKey does map-side combine first; less data shuffled. groupByKey shuffles all values.

**Scalability Trade-offs**: Map scales linearly with partitions....

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