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98-381 Introduction to Programming Using Python

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

Test Code : 98-381
Test cognomen : Introduction to Programming Using Python
Vendor cognomen : Microsoft
: 40 existent Questions

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Microsoft Microsoft Introduction to Programming

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

The MeasureUp 98-381: Introduction to Programming using Python commemorate check is designed to support candidates do together for and saunter the MTA 98-381 exam.

Candidates should be in a position to respect and write apposite Python code, exhaust supported statistics varieties and operators, and control circulate with selections and loops. Candidates should still prefer 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


Microsoft adds recent AI Capabilities to observe, PowerPoint | killexams.com existent Questions and Pass4sure dumps

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

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

REDMOND, Wash. — may too 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 compass from corporations starting from small startups to significant brands comparable to Amazon, Google and Microsoft Corp., among others. Volunteers are invited to ensue in the course of the TEALS site.

“As industries evolve, they should prepare younger americans with the expertise they want,” preeminent 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 trade and in every state. Yet most lofty 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 odds from the TEALS expansion. among the many recent states for the software are Wisconsin, Ohio and North Carolina. A majority of the five extreme 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 present 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 play lecturers gradually prefer 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 requisite 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 vouchsafe 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 learning by using offering classes that involve the total spectrum of their 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 their 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 their autochthonous agencies.”

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

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

The deepest sector performs an mainly critical office in TEALS, which depends on volunteers from local corporations to associate with school play academics to bring computing device science into lofty colleges. any individual with a computer programming historical past and a requisite to execute 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 trade 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, THEY Communications, (425) 638-7777, rrt@we-global.com

word to editors: For extra guidance, news and views from Microsoft, gladden search 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 pilot Prepared by Killexams.com Microsoft Dumps Experts


Killexams.com 98-381 Dumps and existent Questions

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

Test Code : 98-381
Test cognomen : Introduction to Programming Using Python
Vendor cognomen : Microsoft
: 40 existent Questions

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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 existent 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? recent 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 shapely things about Bayesian Methods for Hackers: not only is it approachable for the probabilistic novice, but it too 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 recent data. Bayesian Methods for Hackers teaches these techniques in a hands-on way, using TFP as a substrate. Since the reserve is written in Google Colab, you’re invited to shun and modify the Python examples.

The TensorFlow team built TFP for data scientists, statisticians, and ML researchers and practitioners who want to encode domain learning to understand data and execute predictions. TFP is a Python library built on TensorFlow that makes it effortless 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 they contend 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 they completely understand — are subject to externalities beyond their 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 everyone around us.

    Addressing real-world problems

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

    In the balance of this blog, we’ll provide an overview of a eminent real-world problem that gets more minute treatment in the Bayesian Hackers reserve 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, they depict the observations of seven O-ring damage incidents on prior shuttle missions, as a office of ambient temperature. (At 70 degrees, there are two incidents.)

    You’ll note that as the temperature decreases, there’s clearly an extend in harmony 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. They 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 office and 𝛼 is the prejudice term, shifting the office from left to right. Since both of these parameters can be positive or negative with no particular bounds or biases in size, they can model them as gaussian random variables:

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

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

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

    To connect the probability of failure at temperature t, p(t), to their observed data they can exhaust 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 eventual Part of their generative model is that they Have a defect incident D𝑖 at temperature value 𝑖 that can be modeled as:

    Given this generative model, they want to find model parameters so that the model can interpret their 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 office returns the log of the joint probability that the parameterized model generated the observed data. To learn more about the joint_log_prob, gladden espy this vignette.

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

    Notice how lines 15–18 succinctly encode their generative model, one line per random variable. Also, note that rv_alpha and rv_beta picture the random variables for their 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, they prefer their joint_log_prob function, and dispatch it to the tfp.mcmc module. Markov chain Monte Carlo (MCMC) algorithms execute 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 their challenge_joint_log_prob function:

    Finally, we’ll actually discharge the inference, through their evaluate() helper function:

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

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

    We can then compute a 95% credible interval across the orbit of temperatures. Note that this is a credible interval, not a self-confidence interval typically institute in frequentist approaches to statistical analysis. The 95% credible intervals tells us that they can be 95% sure that the actual value will palter within the interval. For example, as they depict below with the purple region, at 50 degrees, they can be 95% sure that the probability of failure lies between 1.0 and 0.80. Ironically, many people erroneously interpret a self-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 lofty degree of self-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 they first drafted the TFP version of the chapter.

    You’ll too find an analysis to find shadowy 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. gladden back us to sustain this reserve in top shape by making comments and drag 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 existent 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 attribute concurrent code can be a existent headache, but this article aims to demonstrate how effortless it is to collect started writing threaded programs in Python. Due to the great number of modules available in the standard library which are there to back out with this kindhearted of thing, it’s often the case that simple concurrent tasks are surprisingly quick to implement.

    We’ll walk through the unlikeness between threads and processes in a Python context, before reviewing some of the different approaches you can prefer 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 great repercussion it has on which approach you select when writing asynchronous Python. The most famous 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 feasible for threads to prefer odds of multi-core systems. Note that if there are blocking operations which betide outside Python, long-wait tasks dote 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 everyone this is that if you requisite actual parallelism and requisite to leverage multi-core CPUs, threads won’t crop it and you requisite to exhaust processes. A separate process means a separate interpreter with separate memory, its own GIL, and actual parallelism. This pilot 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 effortless to exhaust Pool interface offered here is sufficient.

    Here’s an instance where they 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 reclaim 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 their downloader example; it’s only the eventual shroud which contains the threading-specific code. Note how effortless it is to create a dynamic pool of workers using ThreadPoolExecutor and submit a task. They could even simplify the eventual 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 pattern solution. However, if the operation in question was something which was CPU intensive within Python, processes would likely be more usurp because of the restrictions of the GIL. In that case, they could Have simply switched out ThreadPoolExecutor with  ProcessPoolExecutor.

    The threading module

    Whilst the concurrent.futures module offers a powerful way to collect 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 they 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 reclaim 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 they want to create, they execute an instance of the threading.Thread class, specifying what they would dote their worker office to be, and the arguments required.

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

    This is an instance of when the threading module’s legion of options could be useful: they can cost 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 everyone 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 they know exactly what they want the threads to be working on when they start them. However, it’s often the case that they requisite 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 cost 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 exhaust it to create a tasks queue for their website downloader. We’ll create a group of worker threads which can everyone access the queue and wait for tasks to compass 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 reclaim 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 instance everyone the tasks were added in one proceed for the sake of brevity, but in a existent application the tasks could trickle in at any rate. Here they exit the program when the tasks queue has been fully completed, using the .join() method.

    The multiprocessing module

    The threading module is powerful for minute control of threads, but what if they want this finer flush of control for processes? You might mediate that this would be more challenging since once a process is launched, it’s completely separate and independent – harder to control than a recent 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 selfsame syntax as their examples above. Their 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 reclaim 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 mediate it’s awesome that Python manages to sustain the same syntax between the threading and multiprocessing modules, when the action taking spot under the hood is so different.

    When it comes to distributing data between processes, the queue.Queue that they 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 dispatch 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 carry weight that a minute flush 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 existent questions and Pass4sure dumps

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

    Candidates should be able to recognize and write rectify Python code, exhaust supported data types and operators, and control rush 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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