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.

We will interpolate missing values in irregular time series sensor data using Pandas. We are given one CSV file that is called sensor_data. It contains temperature and humidity readings from IoT collected over time. Some of the readings are missing because of network issues or sensor failures, so those cells are empty. Our job is to fill in those missing values using interpolation and save the clean data. Interpolation is a way of estimating missing values based on the records around them. In our case, we will use the time-based interpolation that also accounts the irregular time intervals. Before the interpolation itself, we need to convert the timestamp column from a plain string into a proper date-time type. Then we set the timestamp column as the index of our DataFrame. It is very important specifically for time-based interpolation because it requires the date-time to be indexed. We reference to the temperature column and implement the interpolate method. We set method to time to use the time-based interpolation. We were required to round the interpolated values to one decimal place. Finally, we save the result as a CSV file using to_csv function.

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

This is a Snowflake question, which is a cloud-based data warehouse that uses SQL and all its main concepts. We have two tables, models and usage logs. Both of the tables are connected through a model ID, which is a common column. The requirements are calculating the total users by summing all the usage log entries, and at the same time, to find the average accuracy for each model. We will store the results in sort of a temporary table that is called CTE. CTE is short for Common Table Expression. It's a temporary result set that we define at the top of our query inside of WITH clause. From usage logs table, we first select model ID, and then we build a new column called total users and implement sum function within users column. Grouping by model ID would collapse all rows sharing the same ID into one single group. For this last column, we will implement window function with average. Partition by model type will divide all rows into separate groups based on their model type. We implement the round function and wrap the window function inside of it.

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.