Cracking the Code

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Central Intelligence Agency

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    Cracking The Coding Interview PDF 6th Edition by Laakmann

    Cracking the Coding Interview 189 Programming questions and Solutions Sixth Edition by Gayle Laakmann Mcdowell | PDF Free Download.

    Author of Cracking the Coding Interview PDF

    Gayle Laakmann McDowell is the editor of 6th Edition of Cracking the Coding Interview 189 Programming questions and Solutions eBook.

    Cracking the Coding Interview Contents

    • The Interview Process.
    • Behind the Scenes.
    • Special Situations.
    • Before the Interview.
    • Behavioral Questions.
    • BigO.
    • Technical Questions.
    • The Offer and Beyond.
    • Interview Questions.
    • Chapter 1. Arrays and Strings.
    • Chapter 2. Linked Lists.
    • Chapter 3. Stacks and Queues.
    • Chapter 4. Trees and Graphs.

    Concepts and Algorithms

    • Chapter 5. Bit Manipulation.
    • Chapter 6. Math and Logic Puzzles.
    • Chapter 7. Object-Oriented Design.
    • Chapter 8. Recursion and Dynamic Programming.
    • Chapter 9. System Design and Scalability.
    • Chapter 10. Sorting and Searching.
    • Chapter 11. Testing.

    • Chapter 12. CandC++
    • Chapter 13. Java
    • Chapter 14. Databases.
    • Chapter 15. Threads and Locks.

    Additional Review Problems

    • Chapter 16. Moderate.
    • Chapter 17. Hard.
    • Solutions.
    • Advanced Topics.
    • Code Library.
    • Hints.

    Introduction to 6th edition of Cracking the Coding Interview

    We walked out of the hiring meeting frustrated-again. Of the ten candidates we reviewed that day, none would receive offers. Were we being too harsh, we wondered?

    I, in particular, was disappointed. We had rejected one of my candidates. A former student. One I had referred.

    He had a 3.73 GPA from the University of Washington, one of the best computer science schools in the world, and had done extensive work on open-source projects.

    He was energetic. He was creative. He was sharp. He worked hard. He was a true geek in all the best ways. But I had to agree with the rest of the committee: the data wasn’t there.

    Get Also
    Cracking the Coding Interview PDF 4th Edition
    Cracking the Coding Interview PDF 5th Edition

    Even if my emphatic recommendation could sway them to reconsider, he would surely get rejected in the later stages of the hiring process.

    There were just too many red flags. Although he was quite intelligent, he struggled to solve the interview problems.

    Most successful candidates could fly through the first question, which was a twist on a well-known problem, but he had trouble developing an algorithm. When he came up with one, he failed to consider solutions that optimized for other scenarios.

    Finally, when he began coding, he flew through the code with an initial solution, but it was riddled with mistakes that he failed to catch.

    Though he wasn’t the worst candidate we’d seen by any measure, he was far from meeting the “bar:’ Rejected. When he asked for feedback over the phone a couple of weeks later, I struggled with what to tell him. Be smarter?

    No, I knew he was brilliant. Be a better coder? No, his skills were on par with some of the best I’d seen. Like many motivated candidates, he had prepared extensively.

    He had read K&R’s classic C book, and he’d reviewed CLRS’ famous algorithms textbook. He could describe in detail the myriad of ways of balancing a tree, and he could do things in C that no sane programmer should ever want to do.

    I had to tell him the unfortunate truth: those books aren’t enough. Academic books prepare you for fancy research, and they will probably make you a better software engineer, but they’re not sufficient for interviews. Why?

    I’ll give you a hint: Your interviewers haven’t seen red-black trees since they were in school either. To crack the coding interview, you need to prepare with real interview questions.

    You must practice on real problems and learn their patterns. It’s about developing a fresh algorithm, not memorizing existing problems.

    Cracking the Coding Interview is the result of my first-hand experience interviewing at top companies and later coaching candidates through these interviews.

    It is the result of hundreds of conversations with candidates. It is the result of the thousands of questions contributed by candidates and interviewers.

    And it’s the result of seeing so many interview questions from so many firms. Enclosed in this Cracking the Coding Interview book are 189 of the best interview questions, selected from thousands of potential problems.

    #Note: If you’d like to buy the Paperback of this eBook, click here

    Download 6th Edition of Cracking the Coding Interview in PDF Format for free.

    Cracking the Code

    How augmented reality and predictive maintenance are helping unlock digital transformation in the chemical industry.

    Posted: May 24, 2020

    Cracking the Code

    Digital technology is revolutionising industries around the world, with even traditionally conservative sectors such as plant and chemicals embracing its potential.

    Chemicals companies are turning to digital technology to gain a competitive advantage as competition increases and margins continue to be squeezed. A recent survey of key decision makers within the chemicals industry found that 92% of those interviewed believe they will be left behind competition if they don’t adopt digital technologies.

    Some chemical organisations such as Eastman Chemicals Company, a global organization, headquartered in the US and Russian firm NIIK are already reaping the rewards of more efficient operating processes. Eastman, for example is confident of achieving industry benchmarks savings of 30% reduction in engineering and design costs and 3% Total Installed Cost after implementing intelligent data management software while Russia’s NIIK has introduced laser scanning and 3D modeling to execute projects faster and save significant costs. Some of the improvements have been dramatic: pipeline specifications have been reduced from three days’ work to one hour. Changes are managed 15% faster, and corrections for collisions and clashes specifically are 50% faster.

    Many chemical companies recognise that the future is digital and are actively implementing intelligent information management systems to create a single source of accurate and trust-worthy data throughout an asset’s lifecycle. However, there remains a lack of understanding of both the full benefits of digital transformation and how best to implement successful programmes. This confusion is preventing the sector from fully cracking the digital transformation code.

    Industrial Internet of Things (IIoT) and Industry 4.0 technologies promise to tackle these problems by promoting operational efficiency and sharing of consistent data. But while it is easy to be dazzled by a piece of “cool” technology, for industry the primary driver must always be business value. In this context, there are two particular technologies that have huge potential to deliver value on investment in the chemical industry.

    The first is augmented reality (AR). This has the potential to transform plant engineering, operations and maintenance – enabling engineers and operators to be able to point a tablet at plant equipment and have it overlaid with the relevant logs, diagrams and performance curves.

    Second is predictive maintenance. An ARC Advisory Group reliability study showed that 82% of all assets have a random failure pattern. That means that even with the best preventive maintenance practices, the very best people can’t account for the random failures that no one sees coming. But what if they could?

    Predictive maintenance solutions use advanced analytical methods to capture problems before they occur, helping to improve reliability and performance. Leading chemical companies have already incorporated this approach into their operating practices; indeed, a predictive maintenance solution enabled a large industrial gas company to detect a slight vibration anomaly, leading to the discovery of a cracked impeller inside an air compressor. This single catch saved the company over $500,000 in maintenance costs avoidance and unplanned downtime. By adopting AR and predictive maintenance, chemical companies can empower their operators with digital insights to improve their operations and their bottom line.

    Augmented Reality (AR)

    While every trade show contains highly produced demos of so-called digital twin and AR technology, a lot of what’s being demonstrated fails to deliver on the tangible value that customers need to see to justify investment in new technology. But AR technology does merit investment because it can empower users in the field with actionable insights. One of the leading chemical companies in the world, BASF implemented AR as part of the company’s Industry 4.0 initiative. The company is using it to achieve operator-driven reliability, improving asset performance, reliability and utilisation while increasing production efficiency.

    Talking to our clients, their overarching concern is how to operate their enterprise more efficiently and profitably. Yet much AR technology was developed without understanding the specifics of an industrial use case. With that in mind, let’s turn to how AR technology is starting to change industrial operating practices.

    Building Augmented Reality on Mobile Technology

    Asking users to completely modify the operational tasks they’re familiar with overnight is a recipe for failure. Yet, drawing on workers’ increasing use of technology in their private lives, companies are learning the benefits of tech that can draw together useful information from diverse sources into a single interface that teams can work with easily. As the industrial workforce shifts, this kind of intelligent data management is increasingly important.

    As companies adopt digital technologies, they are learning to build on existing foundations which can help them to drive efficiency. One key area is mobile technology that teams already use in their daily routine. Rather than trying to transition users and operators to an entirely new way of performing their job duties through AR goggles, start small. Begin by enabling operators in chemical manufacturing plants to perform maintenance rounds using mobile technology, like smartphones and tablets.

    Applications in Chemical Manufacturing

    This immediately delivers value in traditional chemical manufacturing applications. For instance, mobile operator rounds enable operators to capture key operational data in digital form to immediately give the entire organisation real-time operations visibility. This also drives enterprise wide KPI management by providing all functional units with complete manufacturing situational awareness.

    These solutions accelerate the speed and accuracy of operator rounds by delivering important KPIs directly to their device and automating task management. Operators can also capture pictures and videos of the assets directly for further documentation purposes and can collaborate in real-time with reliability engineers and operators performing maintenance rounds. When Ascend Performance Materials implemented mobile operator rounds, the company saved over $1 million in maintenance costs and $2 million in avoided plant shutdowns.

    Predictive Maintenance

    Typical maintenance strategies start with preventive maintenance based on calendar time or usage. But how does one account for things like manufacturing defects in equipment or simple human error in maintenance procedures? Shadow sensing technology, machine learning, big data and predictive maintenance are allowing companies to shift to a new, IIoT-enabled proactive maintenance solution. These solutions replace conventional reactive maintenance strategies with the ability to see what’s going to fail weeks or months before it does. That means you can perform maintenance at the optimal time, instead of replacing equipment that still has a useful operational life ahead of it or scrambling after a failure occurs.

    More accurate and efficient automated data collection enabled through IIoT sensor and information technology drastically expands the number and variety of environmental and operational parameters that maintenance and operations technicians can use to keep their systems running. The downside is that this big data can be cumbersome to actually sort through and manage. Predictive maintenance solutions use analytic techniques such as advanced pattern recognition (APR) and machine learning to tackle this big data, parsing actionable insights from the volume of data to detect the smallest operational anomalies. This provides users with the insights they need to take corrective action.

    User-Friendly Predictive Maintenance

    When implementing predictive maintenance, one common hidden cost is the complexity of the solutions. Many solutions require in-depth coding knowledge to use and configure properly. Some of the more complex solutions may require a dedicated data scientist to properly deploy and adjust the solution. In the long run, this level of staff investment can add up to a substantial cost. Therefore, selecting a user-friendly predictive maintenance solution can make all the difference for chemical companies. These solutions are easy to use and can be configured without any programming experience. This makes it possible for chemical companies to adjust these solutions without substantial (and expensive) support from the original vendor or outside consultants.

    By implementing predictive maintenance, chemical companies can benefit from higher uptime, better reliability and reduced maintenance costs. Being able to predict and schedule maintenance properly also leads to reduced inventory costs. In addition, preventing unexpected equipment failures in a chemical manufacturing plant with many volatile agents has a huge positive impact on employee safety and regulatory compliance.

    It is clear that the chemicals sector is now fully aware of the advantages of digital transformation and they are actively investing in technologies. While AR and predictive maintenance can immediately deliver value, the results they deliver will only ever be as good as the data they are working with. Therefore, in their quest to cracking the digital code, the first step must be an intelligent data management. The success of these solutions can them help justify the business case for further technology adoption, enabling your company to take the next step on its digital transformation.

    To learn more about AR, predictive maintenance and the benefits of digital transformation, visit our stand at the ACHEMA Conference in Frankfurt, Germany from 11-15 June 2020.

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