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Test Number : 70-357
Test Name : Developing Mobile Apps
Vendor Name : Microsoft
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70-357 exam Format | 70-357 Course Contents | 70-357 Course Outline | 70-357 exam Syllabus | 70-357 exam Objectives

Exam Title : Microsoft Certified Solutions Developer (MCSD) - App Builder
Exam ID : 70-357
Exam Duration : 150 mins
Questions in exam : 40-60
Passing Score : 700 / 1000
Exam Center : Pearson VUE
Real Questions : Microsoft Developing Mobile Apps Real Questions
VCE VCE exam : Microsoft 70-357 Certification VCE Practice Test

Develop a XAML page layout for an adaptive UI (10–15%)
Construct a page layout
Configure a RelativePanel layout; select the appropriate XAML layout panel based on the UI requirement; configure a grid with appropriate column and row properties; configure alignment, margins, and padding
Implement responsive and adaptive UI behaviors
Differentiate between responsive and adaptive UI behaviors, create responsive and adaptive UIs by using VisualStateManager and AdaptiveTriggers, implement settings syntax for element properties and attached properties
Create and use custom controls within an adaptive UI
Evaluate when to create a custom control; create a custom control; implement styles, themes, and resource dictionaries; apply styles to custom controls by using Generic.xaml
Optimize a page layout
Reduce complexity for performance gains, reduce unnecessary nesting

Implement page navigation and lifecycle events (10–15%)
Choose the appropriate navigation structure for an app
Evaluate when to implement the Hub, Master/Details, Tabs and Pivot, and Nav Pane navigation patterns; evaluate when to implement a custom navigation pattern
Implement Nav Pane navigation
Load page content by using Frame.Navigate, implement page navigation by using the Nav Pane pattern; implement a SplitView control for use as a navigation pane; support accessibility requirements within navigation by implementing key based navigation, UI automation, and narrator; handle Back button behavior for different Windows 10 device families
Manage app activation
Launch an app, activate an app on Startup, implement activation from a deep link, implement activation based on Search integration, implement activation from a secondary tile
Manage app suspension and resuming
Prepare an app for suspension, resume from suspension or termination, extend execution and monitor suspension errors

Implement data access and data binding (20–25%)
Access data by using Entity Framework (EF)
Access data by using EFCore with SQLite, implement a local SQLite database
Implement the {Binding} extension
Implement the {x:Bind} extension
Implement MVVM classes and class interactions
Implement event binding by applying command patterns, implement a Dispatcher to update the UI thread with async return data Implement app-to-app communications
Integrate a Share contract to share content with another app, integrate drag-and-drop, launch an app for results, implement app extensions, implement App Services
Implement REST Web Services
Implement JSON and data serialization, access cloud data and Web APIs by using HttpClient
Implement file system access
Manage storage by using StorageFile, StorageFolder, and StorageItem; access a file location by using FilePickers; implement data roaming and roaming folders

Implement feature detection for adaptive coding (10–15%)
Implement API detection within adaptive code
Implement Type detection within adaptive code
Implement supported capabilities
Implement support for a microphone, implement support for a webcam, implement support for location, implement support for enterprise authentication

Manage user input and custom user interactions (10–15%)
Implement command bars, flyouts, and dialogs
Implement command bars and AppBarButton buttons, implement context menus and menu flyouts, implement content dialogs, display a tooltip by using ToolTipService, display a pop-up menu, implement control over app settings
Implement support for traditional and touch input devices
Support touch input, support mouse input, support keyboard and virtual keyboard input
Implement speech and voice commands
Support speech synthesis, support speech recognition, support Cortana integration, support Personal Assistant Launch capability, support voice commands
Implement alternative forms of input
Implement inking, implement camera input, implement location services and GPS input

Manage authentication and identity management (10–15%)
Implement authentication using Web Authentication Broker
Implement web service authentication, implement OAuth, implement Azure Active Directory authentication
Manage credentials securely with Credential Locker
Implement two-factor authentication
Implement two-factor authentication using Microsoft Passport, implement two-factor authentication using Windows Hello

Implement notifications, background tasks, and reusable components (15–20%)
Create and consume class libraries and Windows Runtime components
Develop Windows Runtime components, develop class libraries, integrate class libraries and Windows Runtime components
Implement tile and toast notifications
Implement adaptive and interactive toast notifications, implement local tile notifications
Create and register a background task
Create a background task project and reference the background task within a project, implement background task event triggers and conditions
Implement and manage a background task
Monitor background task progress and completion, manage task lifecycle, share data and events between an app and its background tasks, call a background task directly
Create and consume a Universal Windows Platform (UWP) app service
Specify the AppService extension, implement app service as a background task, deploy the app service provider, call app services

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Microsoft Developing learning

Microsoft researchers advance assistive eye-monitoring AI that works on any equipment | 70-357 Practice Questions and boot camp

The audio difficulty access right here

Gaze tracking has the competencies to assist people dwelling with motor neuron ailments and problems exert handle over their atmosphere and communicate with others. however estimating a person’s gaze isn’t a trivial project because of variables including head pose, head position, eye rotation, distance, illumination, history noise, and the presence of glasses, face coverings, and assistive clinical equipment. Commercially attainable gaze trackers exist — they use specialized sensor assemblies — however they tend to be costly, costing as much as hundreds of dollars. cost effective software-based mostly trackers, having said that, are often prone to lighting fixtures interference.

This problem impressed a group of researchers at Microsoft to increase an extremely-exact, hardware-agnostic gaze tracker that works with any off-the-shelf webcam. In a preprint paper posted previous this month, they aspect their work on a gadget that achieves an error of 1.8073 centimeters on GazeCapture, an MIT corpus containing eye-monitoring facts from over 1,450 americans, without calibration or high-quality-tuning.

Microsoft has a exact background of gaze-tracking research. In a previous analyze, researchers on the company experimented with distinctive infrared lights round a display for eye-tracking, in addition to with a camera and depth sensors. And home windows 10 turned into the first edition of the working equipment to present Eye control, a expertise that allows clients to use their eyes to handle an on-display mouse and keyboard adventure, and the eye power Library, which emulates a joystick by way of eye-tracking.

This latest effort from Microsoft engineers isn’t the first try to build a more accurate software-based mostly tracker. The MIT group at the back of GazeCapture designed iTracker, an AI model that performs gaze tracking on Apple gadgets the usage of constructed-in cameras. Their system carried out a prediction error of 1.86 centimeters and a pair of.81 centimeters on smartphones and tablets, respectively, with out calibration.

Microsoft gaze tracking AI

however the researchers aimed to take this a step extra to permit mouse-like gestures on laptops and computers apart from smartphones and tablets.

Like iTracker, the coauthors’ model takes the left eye, appropriate eye, and face areas from digicam photos as enter along with a 25-by-25 face-grid indicating positions of the face pixels in the captured pictures. These input pictures are passed via eye and face sub-models (which might be according to the pretrained ResNet18 laptop imaginative and prescient algorithm), and the outputs of those sub-fashions are then processed into gaze element coordinates.

The researchers knowledgeable their mannequin on parts of the GazeTracker dataset, but they also carried out data augmentation to ensure the model would greater deal with variations it could encounter in the true world. They randomly modified the brightness, contrast, saturation, and hue of demo photos and then resized them before randomly cropping them so as to add noise that averted the mannequin from overfitting. (Overfitting refers to a mannequin that learns the element in the training records to the extent that it negatively influences the performance of the mannequin on new information.)

The coauthors combined the tracking model with a face detection library, Dlib, they observed resulted in additional helpful, consistent, and higher-high-quality detection. whereas GazeCapture doesn’t encompass facts above the eyebrow and under the lip areas, Dlib handles more than a few head rotation assistance in captured photographs. all over detection, the library (and an OpenCV formula referred to as minAreaRect) matches a rectangle to identified facial landmarks and estimates the head perspective before performing rotation correction and extracting face and eye plants. The rotation perspective is encoded into the face-grid.

Microsoft gaze tracking AI

The researchers attempted to root out knowledge biases and other considerations through analyzing the areas of faces to which the system paid attention. They found that it basically cared concerning the eye location but additionally the eyebrow and the lower fringe of the eyelid — in other words, the muscles activated when people flow their eyes in certain instructions. “Trigonometric models that center of attention best on the student and the iris would not always choose these elements and hence, this is the place deep discovering could take advantage of past the obvious with the intention to enrich the accuracy,” the researchers wrote.

In future work, the coauthors plan to strengthen custom neural community architectures that enhance efficiency even further. “Gaze-tracking as accessibility know-how has many roadblocks together with lack of interoperability and non-existence of a diverse and big-scale dataset protecting issues of facial occlusion, head poses and a number of eye situations,” they continued. “This research indicates promise that one day soon any desktop, pill, or cell could be controllable the use of just your eyes because of the prediction capabilities of deep neutral networks.”

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