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98-381 - Introduction to Programming Using Python - Dump Information

Vendor : Microsoft
Exam Code : 98-381
Exam Name : Introduction to Programming Using Python
Questions and Answers : 40 Q & A
Updated On : January 22, 2019
PDF Download Mirror : 98-381 Brain Dump
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98-381 Questions and Answers

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98-381 Introduction to Programming Using Python

Study Guide Prepared by Killexams.com Microsoft Dumps Experts


Killexams.com 98-381 Dumps and Real Questions

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98-381 exam Dumps Source : Introduction to Programming Using Python

Test Code : 98-381
Test Name : Introduction to Programming Using Python
Vendor Name : Microsoft
Q&A : 40 Real Questions

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Microsoft Microsoft Introduction to Programming

Microsoft (98-381): Introduction to Programming the usage of Python | killexams.com Real Questions and Pass4sure dumps

The MeasureUp 98-381: Introduction to Programming using Python observe check is designed to support candidates put together for and move the MTA 98-381 exam.

Candidates should be in a position to respect and write relevant Python code, use supported statistics varieties and operators, and control circulate with selections and loops. Candidates should still take into account how to write, debug, doc, and preserve neatly-formed Python code.

Certification: This exam counts as credit towards the following certifications:MTA: Introduction to Programming using Python


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Microsoft Philanthropies expands program that sends tech company volunteers into high schools to train computing device science | killexams.com Real Questions and Pass4sure dumps

With seven new states, together with Ohio and Wisconsin, TEALS offered in more than 31 states

REDMOND, Wash. — may also 15, 2017 — Microsoft Philanthropies on Monday introduced the expansion of TEALS (technology training and Literacy in colleges), which pairs know-how authorities with lecturers to “co-train” laptop science. The TEALS application will add almost a hundred schools within the united states, and will recruit 1,one hundred volunteers, for the arrival school year. Volunteers come from corporations starting from small startups to significant brands comparable to Amazon, Google and Microsoft Corp., among others. Volunteers are invited to follow in the course of the TEALS site.

“As industries evolve, we should prepare younger americans with the expertise they want,” noted Mary Snapp, corporate vp and head of Microsoft Philanthropies. “Half a million jobs that require computing skills are unfilled within the u.s., with openings in every business and in every state. Yet most high colleges don't train computing device science. Volunteers from Microsoft and more than 300 different organizations are working to fill that gap, in partnership with academics, via TEALS.”

probably the most excellent manufacturing areas within the country will advantage from the TEALS expansion. among the many new states for the software are Wisconsin, Ohio and North Carolina. A majority of the five excessive schools introduced in Michigan are in the Detroit metropolitan area. The country wide affiliation of manufacturers (NAM) estimates the gap between the technology competencies employers need, and the capabilities many worker's possess, is maintaining 350,000 manufacturing jobs unfilled.

TEALS volunteers co-train Introduction to computer Science and AP laptop Science, depending on a college’s selection, and some schools offer each. The TEALS curriculum became developed along side the UC Berkeley and the institution of Washington computing device science departments. A key a part of the application is helping to prepare classroom teachers to train the discipline in the future. Over two years, the lecture room lecturers gradually take over instructing the route on their personal.

This expansion is the influence of latest partnerships with colleges, companies, govt leaders and nonprofits. In Ohio, spurred partly through a 2014 Cleveland foundation examine that showed the need for 10,000 local advice technology (IT) people, the Cuyahoga County government and the Cleveland basis cast a partnership with Microsoft to convey TEALS to the state. The Cleveland foundation provided a $185,000 grant and the Cleveland Metropolitan faculty District, Cleveland State college, and the Envision Excellence in STEM training additionally played key roles in bringing the application to Ohio. collectively, they'll extend access to rigorous computing device science courses throughout Cuyahoga County, and especially in the Cleveland Metropolitan faculty District.

“one in every of Cuyahoga County’s key priorities is to ensure the staff’s competitive knowledge by using offering classes that include the total spectrum of our residents’ lives from early childhood through submit-secondary education and into the workplace,” referred to Armond Budish, Cuyahoga County govt. “The TEALS application is a essential part to our team of workers building initiative. we're focused on setting up profession pathways for youth and younger adults and assist county schoolchildren enrolled within the TEALS courses, feeding students without delay into our native agencies.”

Gener8tor, a startup accelerator, and the Milwaukee Institute, a nonprofit, joined with Thrivent fiscal, a financial capabilities enterprise, Schneider countrywide, a transportation business, and others to deliver the software to Wisconsin. TEALS could be offered in excessive faculties in Appleton, eco-friendly Bay and Milwaukee, in addition to different cities.

“we are extremely joyful that Microsoft is investing in our vicinity with the aid of setting up the TEALS application in aid of laptop science training in a few Wisconsin high schools,” talked about Paul Mueller, chief assistance officer, Thrivent monetary. “Given the present and becoming shortage of IT talent needed to drive our economic system, TEALS is a welcome strategy. it will accelerate the construction of the skill we want, while investing in the futures of our college students.”

The deepest sector performs an mainly critical function in TEALS, which depends on volunteers from local corporations to associate with school room academics to bring computing device science into high colleges. any individual with a computer programming historical past and a need to make contributions to their community is encouraged to apply. TEALS arranges with colleges for courses to learn during first period, allowing volunteers to teach earlier than the workday begins. these drawn to volunteering can be trained greater at https://www.tealsk12.org/volunteers/.

Microsoft (Nasdaq “MSFT” @microsoft) is the main platform and productiveness business for the mobile-first, cloud-first world, and its mission is to empower every grownup and every corporation on earth to achieve more.

For more suggestions, press simplest:

Microsoft Media family members, WE Communications, (425) 638-7777, rrt@we-global.com

word to editors: For extra guidance, news and views from Microsoft, please seek advice from the Microsoft news core at http://information.microsoft.com. web hyperlinks, phone numbers and titles had been suitable at time of book, however may additionally have modified. For additional tips, journalists and analysts may additionally contact Microsoft’s swift Response team or other applicable contacts listed at https://news.microsoft.com/microsoft-public-members of the family-contacts.


98-381 Introduction to Programming Using Python

Study Guide Prepared by Killexams.com Microsoft Dumps Experts


Killexams.com 98-381 Dumps and Real Questions

100% Real Questions - Exam Pass Guarantee with High Marks - Just Memorize the Answers



98-381 exam Dumps Source : Introduction to Programming Using Python

Test Code : 98-381
Test Name : Introduction to Programming Using Python
Vendor Name : Microsoft
Q&A : 40 Real Questions

i found the entirety had to bypass 98-381 exam here.
I distinctly advocate this package deal to anyone planning to get 98-381 q and a. Exams for this certification are difficult, and it takes a variety of work to pass them. killexams.com does most of it for you. 98-381 exam I got from this internet site had most of the questions provided throughout the exam. Without these dumps, I suppose I could fail, and this is why such a lot of people dont skip 98-381 examination from the first strive.


No worries whilst getting geared up for the 98-381 examination.
In recent times i bought your certification package deal and studied it very well. Final week I handed the 98-381 and obtained my certification. Killexams.Com on line checking out engine became a notable device to put together the examination. That better my self guarantee and i with out troubles exceeded the certification exam! Notably recommended!!!


it's miles amazing to have 98-381 present day dumps.
I desired to drop you a line to thanks on your look at materials. This is the number one time i have used your cram. I just took the 98-381 today and passed with an 80 percentage rating. I ought to admit that i was skeptical at the start however me passing my certification examination virtually proves it. Thank you lots! Thomas from Calgary, Canada


Is there any manner to clean 98-381 examination earlier than the whole lot strive?
Have just passed my 98-381 exam. Questions are valid and accurate, which is the good news. I was ensured 99% pass rate and money back guarantee, but obviously I have got excellent scores. Which is the good news.


It is great ideal to prepare 98-381 exam with dumps.
Have exceeded 98-381 examination with killexams.com questions solutions. killexams.com is a hundred% reliable, most of the questions had been similar to what I were given on the exam. I neglected some questions just because I went blankand didnt consider the solution given within the set, but in view that I got the rest proper, I passed with top rankings. So my recommendation is to research everything you get on your training p.c. from killexams.com, this is all you want to pass 98-381.


proper region to discover 98-381 real test questions paper.
Every time I want to pass my certification take a look at to keep my project, I right away go to killexams.Com and are seeking the desired certification take a look at, purchase and prepare the test. It surely is really worth admiring due to the fact, I constantly pass the take a look at with precise rankings.


precisely equal questions, WTF!
I moreover had a splendid revel in with this training set, which led me to passing the 98-381 exam with over ninety eight%. The questions are real and valid, and the checking out engine is a tremendous/guidance device, despite the fact that youre no longer planning on taking the examination and sincerely want to expand your horizons and enlarge your knowledge. Ive given mine to a pal, who also works on this place but clearly received her CCNA. What I imply is its a splendid reading device for absolutely everyone. And if you plan to take the 98-381 exam, this is a stairway to achievement :)


can i find actual Q&A of 98-381 examination?
certainly one of maximum complicated venture is to pick splendid examine material for 98-381 certification examination. I never had sufficient faith in myself and consequently idea I wouldnt get into my favored university thinking about that I didnt have enough subjects to have a observe from. This killexams.Com got here into the photo and my mind-set changed. I was able to get 98-381 fully prepared and i nailed my check with their help. Thank you.


examination questions are modified, wherein am i able to discover new questions and solutions?
To get organized for 98-381 practice exam requires plenty of difficult work and time. Time management is such a complicated problem, that can be rarely resolved. however killexams.com certification has in reality resolved this difficulty from its root level, via imparting number of time schedules, in order that you possibly can without problems entire his syllabus for 98-381 practice examination. killexams.com certification presents all of the tutorial guides which are essential for 98-381 practice examination. So I need to say with out losing a while, start your practise underneath killexams.com certifications to get a excessive rating in 98-381 practice examination, and make your self sense at the top of this global of understanding.


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Introduction to Programming Using Python

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An introduction to probabilistic programming, now available in TensorFlow Probability | killexams.com real questions and Pass4sure dumps

Posted by: Mike Shwe, Product Manager for TensorFlow Probability at Google; Josh Dillon, Software Engineer for TensorFlow Probability at Google; Bryan Seybold, Software Engineer at Google; Matthew McAteer; and Cam Davidson-Pilon.

New to probabilistic programming? New to TensorFlow Probability (TFP)? Then we’ve got something for you. Bayesian Methods for Hackers, an introductory, hands-on tutorial, is now available with examples in TFP. Available as an open-source resource for all, the TFP version complements the previous one written in PyMC3.

There’s a number of neat things about Bayesian Methods for Hackers: not only is it approachable for the probabilistic novice, but it also demonstrates how to apply probabilistic programming to real-world problems.

Probabilistic programming for everyone

Though not required for probabilistic programming, the Bayesian approach offers an intuitive framework for representing beliefs and updating those beliefs based on new data. Bayesian Methods for Hackers teaches these techniques in a hands-on way, using TFP as a substrate. Since the book is written in Google Colab, you’re invited to run and modify the Python examples.

The TensorFlow team built TFP for data scientists, statisticians, and ML researchers and practitioners who want to encode domain knowledge to understand data and make predictions. TFP is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware. TFP allows you to:

  • Explore your data interactively
  • Evaluate different models rapidly
  • Leverage modern, vectorized hardware accelerators automatically
  • Launch easily and with confidence. TFP is professionally built and tested, Google-Cloud ready, and supported by a vibrant open source community.
  • As we discuss in related blog posts, probabilistic programming has diverse applications, including finance and the oil and gas industry. Why? Uncertainty is ubiquitous. Real-world phenomena — even those we completely understand — are subject to externalities beyond our control or even awareness. Dismissing these factors means conclusions might be wrong or at best misleading. We’ve built TFP to be accessible to all, to model the uncertainty all around us.

    Addressing real-world problems

    Many Bayesian tutorials focus on working through easy problems which have analytical solutions: think coin flips and dice rolls. While Bayesian Methods for Hackers starts with these, it quickly moves onto more realistic problems. Examples range from understanding the cosmos to detecting a change in an online user’s behavior.

    In the remainder of this blog, we’ll provide an overview of a famous real-world problem that gets more detailed treatment in the Bayesian Hackers book in Chapter 2: the 1986 Space Shuttle Challenger disaster.

    On January 28, 1986 on the 25th flight of the U.S. Space Shuttle, one of the two solid rocket boosters on the Shuttle Challenger exploded, due to an O-ring failure. Although mission engineers had numerous communications with the manufacturer of the O-ring about damage on previous flights, the manufacturer deemed the risk to be acceptable [1].

    Below, we depict the observations of seven O-ring damage incidents on prior shuttle missions, as a function of ambient temperature. (At 70 degrees, there are two incidents.)

    You’ll note that as the temperature decreases, there’s clearly an increase in proportion of damaged O-rings, yet there’s not an obvious temperature threshold below which the O-rings would likely fail. As with most real-world phenomena, there is uncertainty involved. We wish to determine at a given temperature t, what is the probability of an O-ring failure?

    We can model the probability p of O-ring damage at temperature t using a logistic function, in particular:

    where β determines the shape of the logistic function and 𝛼 is the bias term, shifting the function from left to right. Since both of these parameters can be positive or negative with no particular bounds or biases in size, we can model them as gaussian random variables:

    In TFP, we can intuitively represent 𝛼 and β with tfp.distributions.Normal, as in this code snippet:

    (To run this code snippet, head on over to the Google Colab version of Chapter 2, so you can run the entire Space Shuttle example).

    Note that we get our actual value of 0 or 1 for p(t) in line 8, wherein we’re sampling from the logistic function using the previously sampled values of 𝛼 and β in lines 6 and 7. Also, note that the evaluate() helper function allows us to transition between graph and eager modes seamlessly, while converting tensor values to numpy. We describe eager and graph modes, as well as this helper function in more detail in the beginning of Chapter 2.

    To connect the probability of failure at temperature t, p(t), to our observed data we can use a Bernoulli random variable with parameter p(t). Note that in general, Ber(p) is a random variable that takes value 1 with probability p, and 0 otherwise. So, the last part of our generative model is that we have a defect incident D𝑖 at temperature value 𝑖 that can be modeled as:

    Given this generative model, we want to find model parameters so that the model can explain our observed data — that’s the goal of probabilistic inference!

    TFP performs probabilistic inference by evaluating the model using an unnormalized joint log probability function. The arguments to this joint_log_prob are data and model state. The function returns the log of the joint probability that the parameterized model generated the observed data. To learn more about the joint_log_prob, please see this vignette.

    For the sake of the Challenger example, here’s how we define the joint_log_prob:

    Notice how lines 15–18 succinctly encode our generative model, one line per random variable. Also, note that rv_alpha and rv_beta represent the random variables for our prior distributions for 𝛼 and β. By contrast, rv_observedrepresents the conditional distribution for the likelihood of observations in temperature and O-ring outcome, given a logistic distribution parameterized by 𝛼 and β.

    Next, we take our joint_log_prob function, and send it to the tfp.mcmc module. Markov chain Monte Carlo (MCMC) algorithms make educated guesses about the unknown input values, computing the likelihood of the set of arguments in the joint_log_prob function. By repeating this process many times, MCMC builds a distribution of likely parameters. Constructing this distribution is the goal of probabilistic inference.

    Accordingly, we’ll set up a particular type of MCMC, called Hamiltonian Monte Carlo, over our challenge_joint_log_prob function:

    Finally, we’ll actually perform the inference, through our evaluate() helper function:

    Plotting the distributions for 𝛼 and β, we note that the distributions are fairly wide, as one would expect with so few data points and overlap in temperature between failure and non-failure observations. Yet, even with the wide distributions, we can be fairly confident that temperature does indeed have an effect on the probability of O-ring damage, since all of the samples of β are greater than 0. We can likewise be confident that α is significantly less than 0, since all samples are well into the negative.

    As we mentioned above, what we really want to know is: What is the expected probability of O-ring damage at a given temperature? To compute this probability, we can average over all samples from the posterior to get a likely value for p(t𝑖).

    We can then compute a 95% credible interval across the range of temperatures. Note that this is a credible interval, not a confidence interval typically found in frequentist approaches to statistical analysis. The 95% credible intervals tells us that we can be 95% sure that the true value will lie within the interval. For example, as we depict below with the purple region, at 50 degrees, we can be 95% sure that the probability of failure lies between 1.0 and 0.80. Ironically, many people erroneously interpret a confidence interval to have this property.

    On the day of the Challenger disaster, at 31 degrees, it turns out that the posterior distribution for an O-ring failure would lead us to conclude with a high degree of confidence that a problem would occur.

    This rather straightforward probabilistic analysis demonstrates the power of TFP and Bayesian methods: that they can be used to provide valuable insight and prediction into real-world problems of significant consequence.

    Read on!

    You’ll find a cornucopia of real-world examples in the Bayesian Hackers book. An analysis of text-messaging volume over time that you can apply to a wide variety of fault detection problems in manufacturing and production systems. Software engineers at Google applied the methods for the text-messaging analysis to understanding text flakiness for production software — within a few weeks after we first drafted the TFP version of the chapter.

    You’ll also find an analysis to find dark matter in the universe. Also, predicting returns of shares in public companies, i.e., stock returns.

    We hope that you’ll dig into the conceptual walkthroughs in the book, applying the techniques to problems in your field. Please help us to keep this book in top shape by making comments and pull requests in the github!

    Acknowledgements

    We thank the TensorFlow Probability team for their assistance in writing the TFP version of the Bayesian Hackers book.

    References

    [1] https://en.wikipedia.org/wiki/Space_Shuttle_Challenger_disaster


    Thread Carefully: An Introduction To Concurrent Python | killexams.com real questions and Pass4sure dumps

    The ability to execute code in parallel is crucial in a wide variety of scenarios. Concurrent programming is a key asset for web servers, producer/consumer models, batch number-crunching and pretty much any time an application is bottlenecked by a resource.

    It’s sadly the case that writing quality concurrent code can be a real headache, but this article aims to demonstrate how easy it is to get started writing threaded programs in Python. Due to the large number of modules available in the standard library which are there to help out with this kind of thing, it’s often the case that simple concurrent tasks are surprisingly quick to implement.

    We’ll walk through the difference between threads and processes in a Python context, before reviewing some of the different approaches you can take and what they’re best suited for.

    (Python 3 is used for the duration of the article.)

    The Global Interpreter Lock

    It’s impossible to talk about concurrent programming in Python without mentioning the Global Interpreter Lock, or GIL. This is because of the large impact it has on which approach you select when writing asynchronous Python. The most important thing to note is that it is only a feature of CPython (the widely used “reference” Python implementation), it’s not a feature of the language. Jython and IronPython, among other implementations, have no GIL.

    The GIL is controversial because it only allows one thread at a time to access the Python interpreter. This means that it’s often not possible for threads to take advantage of multi-core systems. Note that if there are blocking operations which happen outside Python, long-wait tasks like I/O for instance, then the GIL is not a bottleneck and writing a threaded program will still be a benefit. However, if the blocking operations are largely crunching through CPython bytecode, then the GIL becomes a bottleneck.

    Why was the GIL introduced at all? It makes memory management much simpler with no possibility of simultaneous access or race conditions, and it makes C extensions easier to write and easier to wrap.

    The upshot of all this is that if you need true parallelism and need to leverage multi-core CPUs, threads won’t cut it and you need to use processes. A separate process means a separate interpreter with separate memory, its own GIL, and true parallelism. This guide will give examples of both thread and process architectures.

    The concurrent.futures module

    The concurrent.futures module is a well-kept secret in Python, but provides a uniquely simple way to implement threads and processes. For many basic applications, the easy to use Pool interface offered here is sufficient.

    Here’s an example where we want to download some webpages, which will be much quicker if done in parallel.

    """Download webpages in threads.""" import requests from concurrent.futures import ThreadPoolExecutor download_list = [ {'name': 'google', 'url': "http://google.com"}, {'name': 'reddit', 'url': "http://reddit.com"}, {'name': 'ebay', 'url': "http://ebay.com"}, {'name': 'bbc', 'url': "http://bbc.co.uk"} ] def download_page(page_info): """Download and save webpage.""" r = requests.get(page_info['url']) with open(page_info['name'] + '.html', 'w') as save_file: save_file.write(r.text) if __name__ == '__main__': pool = ThreadPoolExecutor(max_workers=10) for download in download_list: pool.submit(download_page, download)

    Most of the code is just setting up our downloader example; it’s only the last block which contains the threading-specific code. Note how easy it is to create a dynamic pool of workers using ThreadPoolExecutor and submit a task. We could even simplify the last two lines to one using map:

    pool.map(download_page, download_list)

    Using threads works well in this case since the blocking operation that benefits from concurrency is the act of fetching the webpage. This means that the GIL is not an issue and threading is an ideal solution. However, if the operation in question was something which was CPU intensive within Python, processes would likely be more appropriate because of the restrictions of the GIL. In that case, we could have simply switched out ThreadPoolExecutor with  ProcessPoolExecutor.

    The threading module

    Whilst the concurrent.futures module offers a great way to get off the ground quickly, sometimes more control is needed over different threads, which is where the ubiquitous threading module comes in.

    Let’s re-implement the website downloader we made above, this time using the threading module.

    """Download webpages in threads, using `threading`.""" import requests import time import threading download_list = [ {'name': 'google', 'url': "http://google.com"}, {'name': 'reddit', 'url': "http://reddit.com"}, {'name': 'ebay', 'url': "http://ebay.com"}, {'name': 'bbc', 'url': "http://bbc.co.uk"} ] def status_update(): """Print 'Still downloading' at regular intervals.""" while True: print("Still downloading") time.sleep(0.1) def download_page(page_info): """Download and save webpage.""" r = requests.get(page_info['url']) with open(page_info['name'] + '.html', 'w') as save_file: save_file.write(r.text) if __name__ == '__main__': for download in download_list: downloader = threading.Thread(target=download_page, args=(download,)) downloader.start() status = threading.Thread(target=status_update) status.start()

    For each thread we want to create, we make an instance of the threading.Thread class, specifying what we would like our worker function to be, and the arguments required.

    Note that we’ve also added a status update thread. The purpose of this is to repeatedly print “Still downloading” until we’ve finished fetching all the web pages. Unfortunately, since Python waits for all threads to finish executing before it exits, the program will never exit and the status updater thread will never stop printing.

    This is an example of when the threading module’s multitude of options could be useful: we can mark the updater thread as a daemon thread, which means that Python will exit when only daemon threads are left running.

    status = threading.Thread(target=status_update) status.daemon = True status.start()

    The program now successfully stops printing and exits when all downloader threads are finished.

    Daemon threads are generally most useful for background tasks and repetitive functions which are only required when the main program is running, since a daemon can be killed at any moment, causing data loss.

    The combination of threading and queues

    So far we’ve only looked at cases where we know exactly what we want the threads to be working on when we start them. However, it’s often the case that we need to start a group of worker threads, then feed them tasks as they arrive.

    The best data structure for dealing with these tasks is, of course, a queue, and Python provides a queue module which is especially geared towards threading applications. FIFO, LIFO and priority queues are available.

    Using a queue.Queue object to add, get, and mark tasks as done is as simple as this:

    from queue import Queue # maxsize=0 means infinite size limit tasks = Queue(maxsize=0) tasks.put("a task") tasks.put("another task") while not tasks.empty(): print(tasks.get()) # execute task tasks.task_done()

    Ok, that’s pretty basic so far. Now let’s use it to create a tasks queue for our website downloader. We’ll create a group of worker threads which can all access the queue and wait for tasks to come in.

    """Download webpages in threads, using `threading` and `queue`.""" import requests import threading from queue import Queue NUM_WORKER_THREADS = 3 def download_page(page_info): """Download and save webpage.""" r = requests.get(page_info['url']) with open(page_info['name'] + '.html', 'w') as save_file: save_file.write(r.text) def handle_tasks(tasks_queue): """Monitor tasks queue and execute tasks as appropriate.""" while True: download_page(tasks_queue.get()) tasks_queue.task_done() if __name__ == '__main__': tasks = Queue(maxsize=0) # Create and start worker threads for i in range(NUM_WORKER_THREADS): worker = threading.Thread(target=handle_tasks, args=(tasks,)) worker.daemon = True worker.start() # Add some tasks to the queue tasks.put({'name': 'google', 'url': "http://google.com"}) tasks.put({'name': 'reddit', 'url': "http://reddit.com"}) tasks.put({'name': 'ebay', 'url': "http://ebay.com"}) tasks.put({'name': 'bbc', 'url': "http://bbc.co.uk"}) tasks.join()

    Note that in this example all the tasks were added in one go for the sake of brevity, but in a real application the tasks could trickle in at any rate. Here we exit the program when the tasks queue has been fully completed, using the .join() method.

    The multiprocessing module

    The threading module is great for detailed control of threads, but what if we want this finer level of control for processes? You might think that this would be more challenging since once a process is launched, it’s completely separate and independent – harder to control than a new thread which remains within the current interpreter and memory space.

    Fortunately for us, the Python developers worked hard to create a multiprocessing module which has an interface that is almost identical to the threading module. This means that launching processes follows the exact same syntax as our examples above. Our simple downloader would become this:

    """Download webpages in threads, using `multiprocessing`.""" import requests import time import multiprocessing download_list = [ {'name': 'google', 'url': "http://google.com"}, {'name': 'reddit', 'url': "http://reddit.com"}, {'name': 'ebay', 'url': "http://ebay.com"}, {'name': 'bbc', 'url': "http://bbc.co.uk"} ] def status_update(): """Print 'Still downloading' at regular intervals.""" while True: print("Still downloading") time.sleep(0.1) def download_page(page_info): """Download and save webpage.""" r = requests.get(page_info['url']) with open(page_info['name'] + '.html', 'w') as save_file: save_file.write(r.text) if __name__ == '__main__': for download in download_list: downloader = multiprocessing.Process(target=download_page, args=(download,)) downloader.start() status = multiprocessing.Process(target=status_update) status.daemon = True status.start()

    We think it’s awesome that Python manages to keep the same syntax between the threading and multiprocessing modules, when the action taking place under the hood is so different.

    When it comes to distributing data between processes, the queue.Queue that we used for threading will not work between processes. This is because a queue.Queue is fundamentally just a data structure within the current process – albeit one which is cleverly locked and mutexed. Thankfully there exists a multiprocessing.Queue, which is specifically designed for inter-process communication. Behind the scenes, this will serialize your data and send it through a pipe between processes – a very convenient abstraction.

    Summary

    Writing concurrent code in Python can be a lot of fun due to the inbuilt language features that abstract away a lot of problems. This doesn’t mean that a detailed level of control cannot be achieved either, but rather that the barrier to getting started with simple tasks is lowered. So when you’re stuck waiting for one process to finish before starting the next, give one of these techniques a try.


    Microsoft (98-381): Introduction to Programming Using Python | killexams.com real questions and Pass4sure dumps

    The MeasureUp 98-381: Introduction to Programming Using Python practice test is designed to help candidates prepare for and pass the MTA 98-381 exam.

    Candidates should be able to recognize and write correct Python code, use supported data types and operators, and control flow with decisions and loops. Candidates should understand how to write, debug, document, and maintain well-formed Python code.

    Certification: This exam counts as credit toward the following certifications:MTA: Introduction to Programming Using Python



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    Intel [2 Certification Exam(s) ]
    IQN [1 Certification Exam(s) ]
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    Magento [3 Certification Exam(s) ]
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    Medical [69 Certification Exam(s) ]
    Microsoft [368 Certification Exam(s) ]
    Mile2 [2 Certification Exam(s) ]
    Military [1 Certification Exam(s) ]
    Misc [1 Certification Exam(s) ]
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    PostgreSQL-CE [1 Certification Exam(s) ]
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    SASInstitute [15 Certification Exam(s) ]
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    Teacher-Certification [4 Certification Exam(s) ]
    The-Open-Group [8 Certification Exam(s) ]
    TIA [3 Certification Exam(s) ]
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    Trainers [3 Certification Exam(s) ]
    Trend [1 Certification Exam(s) ]
    TruSecure [1 Certification Exam(s) ]
    USMLE [1 Certification Exam(s) ]
    VCE [6 Certification Exam(s) ]
    Veeam [2 Certification Exam(s) ]
    Veritas [33 Certification Exam(s) ]
    Vmware [58 Certification Exam(s) ]
    Wonderlic [2 Certification Exam(s) ]
    Worldatwork [2 Certification Exam(s) ]
    XML-Master [3 Certification Exam(s) ]
    Zend [6 Certification Exam(s) ]





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    References:


    Pass4sure Certification Exam Questions and Answers - barico.ba
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    Study notes to cover complete exam syllabus - Killexams.com
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