Vmware Interview Questions (8+ Questions)

Protect your Linux server from SSH brute-force attacks using log analysis and Fail2Ban. Learn how to identify unauthorized login attempts in /var/log/auth.log, extract and count failed login IPs, detect dictionary attack patterns, and automate IP blocking. This guide covers analyzing authentication logs for security threats, sorting top attackers by failure count, configuring Fail2Ban to ban malicious IPs automatically, and verifying firewall protection. Essential for securing servers against automated botnets, preventing unauthorized access, and maintaining robust system security.

Implement round-robin load balancing in Linux using iptables DNAT and the statistic module. Learn how to distribute network traffic evenly across multiple backend ports (e.g., 8081 and 8082) from a single entry point (8080) without external load balancers like Nginx or HAProxy. This guide covers configuring iptables NAT rules, using the statistic match extension with nth mode for deterministic packet distribution, setting up backend test services, and verifying load balancing behavior. Essential for simulating microservices environments, testing client-side failover, and understanding network packet manipulation at the kernel level.

Use pandas time-based interpolation to fill missing temperature and humidity values in irregular time-series IoT sensor data and save the cleaned dataset.

Join call records with customer data and compute daily aggregates using countDistinct and sum.

WITH usage_agg AS (
SELECT
Model_ID,
SUM(Uses) AS Total_Uses
FROM {{ ref("usage_logs") }}
GROUP BY Model_ID
)
SELECT
m.Model_ID,
m.Model_Name,
m.Model_Type,
m.Accuracy,
u.Total_Uses,
ROUND(AVG(m.Accuracy) OVER (PARTITION BY m.Model_Type), 2) AS Average_Accuracy
FROM {{ ref("models") }} m
INNER JOIN usage_agg u ON m.Model_ID = u.Model_ID

Practice standardizing and combining datasets in PySpark. Learn how to rename columns, inject literal values to tag data sources, and use the unionByName function to stack DataFrames vertically.

Practice PySpark data aggregation. Learn how to join relational tables, group by multiple categorical columns, compute mathematical averages, and safely truncate decimals by casting to integers.

Master data wrangling in PySpark. Learn how to execute multiple inner joins to combine normalized tables, and use the split function alongside getItem to extract specific parts of delimited text columns.