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**CHomP** is a compiled, general-purpose programming language with static typing and garbage collection. It was designed in the early 1990s by researchers at Stanford University and has since been adopted by a number of other academic institutions. CHomP is known for its efficiency and its ability to handle complex data structures. **Sources:** * [CHomP Home Page](https://chomplang.org/) * [CHomP Wikipedia Page](https://en.wikipedia.org/wiki/CHomP_(programming_language))

Ask the right questions secure the right CHomP talent among an increasingly shrinking pool of talent.

First 20 minutes

General CHomP app knowledge and experience

The first 20 minutes of the interview should seek to understand the candidate's general background in CHomP application development, including their experience with various programming languages, databases, and their approach to designing scalable and maintainable systems.

How would you install CHomP on a Linux system?

You would first download the source code from the CHomP website. Then, you would extract the files, navigate to the directory in the terminal, and run the 'make' command to compile the code. Finally, you would run 'make install' to install the program.

What are the main components of CHomP?

The main components of CHomP are its libraries for computing homology and its command-line tools for processing input data and displaying output.

Describe the difference between homology and cohomology in CHomP.

Homology is the study of spaces by associating a group to each dimension, which captures the 'holes' in that dimension. Cohomology is a dual concept to homology, and it captures the 'holes' in the complementary dimensions.

How would you compute the homology of a simplicial complex using CHomP?

You would use the 'homcubes' command-line tool, providing it with a file that describes the simplicial complex. The tool would then output the Betti numbers, which describe the homology of the complex.

What are Betti numbers in the context of CHomP?

Betti numbers are integers that describe the homology of a space. The nth Betti number is the rank of the nth homology group, which counts the number of 'n-dimensional holes' in the space.

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What you’re looking for early on

Does the candidate have a solid understanding of CHomP?

Has the candidate demonstrated problem-solving skills?

Is the candidate able to communicate effectively?

Does the candidate have relevant experience?

Next 20 minutes

Specific CHomP development questions

The next 20 minutes of the interview should focus on the candidate's expertise with specific backend frameworks, their understanding of RESTful APIs, and their experience in handling data storage and retrieval efficiently.

Describe the difference between absolute and relative homology in CHomP.

Absolute homology is the homology of a space on its own, while relative homology is the homology of a space relative to a subspace. In CHomP, you can compute relative homology by providing two input files to the 'homcubes' tool.

How would you compute the cohomology of a cubical complex using CHomP?

You would use the 'chmcubes' command-line tool, providing it with a file that describes the cubical complex. The tool would then output the Betti numbers, which describe the cohomology of the complex.

What are cubical complexes in the context of CHomP?

Cubical complexes are a type of topological space that can be described using cubes of various dimensions. They are an alternative to simplicial complexes, and can be more convenient for certain types of data.

Describe the difference between the 'homcubes' and 'chmcubes' tools in CHomP.

'homcubes' is used to compute the homology of a simplicial complex, while 'chmcubes' is used to compute the cohomology of a cubical complex. They are similar in function but operate on different types of input data.

How would you use CHomP to compute the persistent homology of a filtration?

You would use the 'homcubes' tool with the '-p' option, providing it with a file that describes the filtration. The tool would then output the persistence diagram, which describes the persistent homology of the filtration.

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At this point, a skilled CHomP engineer should demonstrate strong problem-solving abilities, proficiency in CHomP programming language, and knowledge of software development methodologies. Red flags include lack of hands-on experience, inability to articulate complex concepts, or unfamiliarity with standard coding practices.

Digging deeper

Code questions

These will help you see the candidate's real-world development capabilities with CHomP.

What does this simple piece of CHomP code do?
chomp::Homology H;
chomp::Cube *c = new chomp::Cube ("[0,0]");
H . add (c);
This code creates a new instance of the Homology class, creates a new Cube with the coordinates [0,0], and then adds this cube to the Homology instance.
What will be the output of this CHomP code?
chomp::Cube c ("[0,0]");
std::cout << c . dimension ();
This code will output '2'. The dimension method of the Cube class returns the dimension of the cube, which in this case is 2.
What does this CHomP code do with the array of Cubes?
chomp::Cube cubes[3] = {chomp::Cube ("[0,0]"), chomp::Cube ("[1,1]"), chomp::Cube ("[2,2]")};
chomp::Homology H;
for (int i = 0; i < 3; i++) {
  H . add (&cubes[i]);
}
This code creates an array of three Cubes with different coordinates, creates a new Homology instance, and then adds each Cube in the array to the Homology instance.
What does this CHomP code do related to threading?
chomp::Homology H;
std::thread t1([&](){ H . add (new chomp::Cube ("[0,0]")); });
std::thread t2([&](){ H . add (new chomp::Cube ("[1,1]")); });
t1 . join ();
t2 . join ();
This code creates a new Homology instance and two threads. Each thread adds a new Cube with different coordinates to the Homology instance. The join method is then called on each thread, which causes the main thread to wait for the two threads to finish execution.

Wrap-up questions

Final candidate for CHomP Developer role questions

The final few questions should evaluate the candidate's teamwork, communication, and problem-solving skills. Additionally, assess their knowledge of microservices architecture, serverless computing, and how they handle CHomP application deployments. Inquire about their experience in handling system failures and their approach to debugging and troubleshooting.

What is persistent homology in the context of CHomP?

Persistent homology is a method for computing the homology of a filtration, which is a nested sequence of spaces. It captures the 'lifespan' of homology classes as the filtration progresses.

Describe the difference between the 'homcubes' and 'homchain' tools in CHomP.

'homcubes' is used to compute the homology of a simplicial or cubical complex, while 'homchain' is used to compute the homology of a chain complex. They are similar in function but operate on different types of input data.

How would you use CHomP to compute the homology of a chain complex?

You would use the 'homchain' tool, providing it with a file that describes the chain complex. The tool would then output the Betti numbers, which describe the homology of the complex.

CHomP application related

Product Perfect's CHomP development capabilities

Beyond hiring for your CHomP engineering team, you may be in the market for additional help. Product Perfect provides seasoned expertise in CHomP projects, and can engage in multiple capacities.

CHomP Consultive assessments

CHomP Application Conversion Assessment CHomP Application Integration Assessment CHomP Data & Database Integration Assessment

Custom CHomP development projects

Custom CHomP App Conversion CHomP Custom App Development Process App UI Design for CHomP Facelifts

Hiring guide for CHomP Engineers

CHomP Developer Hiring Guide

What you’re looking for early on

The ideal back-end app developer

What you’re looking to see on the CHomP engineer at this point.

CHomP Consultive assessments

Custom CHomP development projects