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Android development refers to the process of creating mobile applications for devices that run on the Android operating system. It is one of the most popular mobile operating systems globally, and developing applications for it can be a rewarding endeavour. Android development offers a wide range of possibilities, from creating games to productivity apps and more.

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Android development refers to the process of creating applications (apps) for devices that run on the Android operating system. Android is the most widely used mobile operating system globally, and it powers a variety of devices, including smartphones, tablets, smartwatches, and more. Android development is the practice of designing, coding, and testing software applications specifically for Android-powered devices.

Here are some key aspects and components of Android development:
Java or Kotlin Programming: Android apps can be developed using programming languages like Java and Kotlin. Kotlin has gained popularity as the preferred language for Android development due to its conciseness and safety features.

Android Studio: Android developers typically use Android Studio, which is the official integrated development environment (IDE) for Android app development. Android Studio provides tools for coding, debugging, testing, and designing user interfaces.

User Interface (UI) Design: Creating an attractive and user-friendly UI is a crucial aspect of Android development. Android provides a range of UI components and layout tools to design visually appealing interfaces.

XML Layouts: Android apps often use XML (Extensible Markup Language) for defining the layout and appearance of the user interface elements.

Activities and Fragments: Android apps are typically composed of activities, which represent individual screens or windows, and fragments, which are reusable UI components within activities.

Intents: Intents are used to facilitate communication between different parts of an Android app and between different apps on the device.

Data Storage: Android apps can store data locally using various mechanisms, such as SharedPreferences, SQLite databases, or external storage. They can also interact with remote data sources and APIs.

Permissions: Android apps must request and handle permissions to access certain device features or user data, such as the camera, location, or contacts.

Testing and Debugging: Android developers use various testing methods and tools to ensure the functionality and reliability of their apps. Debugging is an essential part of the development process.

Publishing: Once an Android app is complete, it can be published on the Google Play Store, which is the official marketplace for Android apps. Developers need to follow specific guidelines and requirements for app submission.

Updates and Maintenance: Android app development is an ongoing process that includes maintaining and updating apps to add new features, fix bugs, and ensure compatibility with newer Android versions.

Android development can be both a rewarding and challenging field, given the diversity of Android devices and the continuous evolution of the platform. It offers opportunities for creating a wide range of apps, from games and productivity tools to social networking and e-commerce applications. Developers need to stay updated with the latest Android technologies and best practices to create successful and user-friendly Android apps.

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Databricks Certified Machine Learning ProfessionalThe Databricks Certified Machine Learning Professional certification exam assesses an individual’s ability to use Databricks Machine Learning and its capabilities to perform advanced machine learning in production tasks. This includes the ability to track, version, and manage machine learning experiments and manage the machine learning model lifecycle. In addition, the certification exam assesses the ability to implement strategies for deploying machine learning models. Finally, test-takers will also be assessed on their ability to build monitoring solutions to detect data drift. Individuals who pass this certification exam can be expected to perform advanced machine learning engineering tasks using Databricks Machine Learning.

Exam DetailsType: Proctored certificationNumber of items: 60 multiple-choice questionsTime limit: 120 minutesRegistration fee: $200Languages: EnglishDelivery method: Online proctoredPrerequisites: None, but related training highly recommendedRecommended experience: 1+ years of hands-on experience performing the machine learning tasks outlined in the exam guide Validity period: 2 yearsRecertification: Recertification is required to maintain your certification status. Databricks Certifications are valid for two years from issue date.

Exam Topics Section 1: Experimentation – 30%Data Management● Read and write a Delta table● View Delta table history and load a previous version of a Delta table● Create, overwrite, merge, and read Feature Store tables in machine learning workflowsExperiment Tracking● Manually log parameters, models, and evaluation metrics using MLflow● Programmatically access and use data, metadata, and models from MLflow experimentsAdvanced Experiment Tracking● Perform MLflow experiment tracking workflows using model signatures and input examples● Identify the requirements for tracking nested runs● Describe the process of enabling autologging, including with the use of Hyperopt● Log and view artifacts like SHAP plots, custom visualizations, feature data, images, and metadata

Section 2: Model Lifecycle Management – 30%Preprocessing Logic● Describe an MLflow flavor and the benefits of using MLflow flavors● Describe the advantages of using the pyfunc MLflow flavor● Describe the process and benefits of including preprocessing logic and context in custom model classes and objectsModel Management● Describe the basic purpose and user interactions with Model Registry● Programmatically register a new model or new model version.● Add metadata to a registered model and a registered model version● Identify, compare, and contrast the available model stages● Transition, archive, and delete model versionsModel Lifecycle Automation● Identify the role of automated testing in ML CI/CD pipelines● Describe how to automate the model lifecycle using Model Registry Webhooks and Databricks Jobs● Identify advantages of using Job clusters over all-purpose clusters● Describe how to create a Job that triggers when a model transitions between stages, given a scenario● Describe how to connect a Webhook with a Job● Identify which code block will trigger a shown webhook● Identify a use case for HTTP webhooks and where the Webhook URL needs to come.● Describe how to list all webhooks and how to delete a webhook

Section 3: Model Deployment – 25%Batch● Describe batch deployment as the appropriate use case for the vast majority of deployment use cases● Identify how batch deployment computes predictions and saves them somewhere for later use● Identify live serving benefits of querying precomputed batch predictions● Identify less performant data storage as a solution for other use cases● Load registered models with load_model● Deploy a single-node model in parallel using spark_udf● Identify z-ordering as a solution for reducing the amount of time to read predictions from a table● Identify partitioning on a common column to speed up querying● Describe the practical benefits of using the score_batch operationStreaming● Describe Structured Streaming as a common processing tool for ETL pipelines● Identify structured streaming as a continuous inference solution on incoming data● Describe why complex business logic must be handled in streaming deployments● Identify that data can arrive out-of-order with structured streaming● Identify continuous predictions in time-based prediction store as a scenario for streaming deployments● Convert a batch deployment pipeline inference to a streaming deployment pipeline● Convert a batch deployment pipeline writing to a streaming deployment pipelineReal-time● Describe the benefits of using real-time inference for a small number of records or when fast prediction computations are needed● Identify JIT feature values as a need for real-time deployment● Describe model serving deploys and endpoint for every stage● Identify how model serving uses one all-purpose cluster for a model deployment● Query a Model Serving enabled model in the Production stage and Staging stage● Identify how cloud-provided RESTful services in containers is the best solution for production-grade real-time deployments

Section 4: Solution and Data Monitoring – 15%Drift Types● Compare and contrast label drift and feature drift● Identify scenarios in which feature drift and/or label drift are likely to occur● Describe concept drift and its impact on model efficacyDrift Tests and Monitoring● Describe summary statistic monitoring as a simple solution for numeric feature drift● Describe mode, unique values, and missing values as simple solutions for categorical feature drift● Describe tests as more robust monitoring solutions for numeric feature drift than simple summary statistics● Describe tests as more robust monitoring solutions for categorical feature drift than simple summary statistics● Compare and contrast Jenson-Shannon divergence and Kolmogorov-Smirnov tests for numerical drift detection● Identify a scenario in which a chi-square test would be usefulComprehensive Drift Solutions● Describe a common workflow for measuring concept drift and feature drift● Identify when retraining and deploying an updated model is a probable solution to drift● Test whether the updated model performs better on the more recent data

Share Databricks Machine Learning Professional Free Dumps1. Which of the following Databricks-managed MLflow capabilities is a centralized model store?A.ModelsB.Model RegistryC.Model ServingD.Feature StoreE.ExperimentsAnswer: C

A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or computing predictions using that model. They decide to wrap this preprocessing in a custom model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as a pyfunc model.Which of the following is a benefit of this approach when loading the logged pyfunc model for downstream deployment?A.The pvfunc model can be used to deploy models in a parallelizable fashionB.The same preprocessing logic will automatically be applied when calling fitC.The same preprocessing logic will automatically be applied when calling predictD.This approach has no impact when loading the logged Pvfunc model for downstream deploymentE.There is no longer a need for pipeline-like machine learning objectsAnswer: E
Which of the following MLflow Model Registry use cases requires the use of an HTTP Webhook?A.Starting a testing job when a new model is registeredB.Updating data in a source table for a Databricks SQL dashboard when a model version transitions to the Production stageC.Sending an email alert when an automated testing Job failsD.None of these use cases require the use of an HTTP WebhookE.Sending a message to a Slack channel when a model version transitions stagesAnswer: B
Which of the following lists all of the model stages are available in the MLflow Model Registry?A.Development. Staging. ProductionB.None. Staging. ProductionC.Staging. Production. ArchivedD.None. Staging. Production. ArchivedE.Development. Staging. Production. ArchivedAnswer: A
A machine learning engineer needs to deliver predictions of a machine learning model in real-time. However, the feature values needed for computing the predictions are available one week before the query time.Which of the following is a benefit of using a batch serving deployment in this scenario rather than a real-time serving deployment where predictions are computed at query time?A.Batch serving has built-in capabilities in Databricks Machine LearningB.There is no advantage to using batch serving deployments over real-time serving deploymentsC.Computing predictions in real-time provides more up-to-date resultsD.Testing is not possible in real-time serving deploymentsE.Querying stored predictions can be faster than computing predictions in real-timeAnswer: A
Which of the following describes the purpose of the context parameter in the predict method of Python models for MLflow?A.The context parameter allows the user to specify which version of the registered MLflow Model should be used based on the given application’s current scenarioB.The context parameter allows the user to document the performance of a model after it has been deployedC.The context parameter allows the user to include relevant details of the business case to allow downstream users to understand the purpose of the modelD.The context parameter allows the user to provide the model with completely custom if-else logic for the given application’s current scenarioE.The context parameter allows the user to provide the model access to objects like preprocessing models or custom configuration filesAnswer: A
A machine learning engineering team has written predictions computed in a batch job to a Delta table for querying. However, the team has noticed that the querying is running slowly. The team has already tuned the size of the data files. Upon investigating, the team has concluded that the rows meeting the query condition are sparsely located throughout each of the data files.Based on the scenario, which of the following optimization techniques could speed up the query by colocating similar records while considering values in multiple columns?A.Z-OrderingB.Bin-packingC.Write as a Parquet fileD.Data skippingE.Tuning the file sizeAnswer: E

NIFT Entrance Exam Coaching in Janakpuri, Uttam Nagar, Dwarka, Delhi
Established in 1986, NIFT is the pioneer of fashion education in the country and has been in the vanguard of providing professional human resources for textile and apparel manufacturing. In 2006, it was made a statutory institute by an Act of the Indian Parliament with the President of India as a ‘Visitor’.

The NIFT has also worked on developing and positioning handlooms and handicrafts for the Union and State governments over the years.

History
Under the Ministry of Textiles of the Government of India, NIFT was established in 1986. The NIFT Act of the Indian Parliament established the institute as a statutory institute in 2006 and empowered it to grant its own degrees.

Together with the Ministry of Textiles, NIFT is developing an India-specific size chart. In 2021, the project was fully underway. NIFT 2022 results were announced online on March 9, 2022 by the National Institute of Fashion Technology.

Campus
At present, NIFT has 17 campuses across the country. The first campus was established in Hauz Khas, New Delhi, in 1986. Chennai, Kolkata, Gandhinagar, Hyderabad, and Mumbai campuses were established in 1995, followed by Bengaluru in 1997. The Bhopal campus was established in June 2008, Jodhpur in 2010, Kangra in 2009, Kannur temporarily in 2008, but permanently in 2012, and Patna in 2008. In the last decade, Raebareli, Shillong, Srinagar, and Panchkula were established.

NIFT Advantage
A number one academic institution for design, management, and technology, NIFT aspires to set the standard for the style industry.

Both practical and theoretical training is administered by the Institute’s comprehensive and cutting-edge infrastructure and equipment. NIFT’s reputation as a prestigious design college allows UG students to choose from the following courses:

Fashion Design
Textile Design
Accessory Design
Knitwear Design
Design in Leather
Fashion Communication
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A very competitive environment will improve your performance and help you prepare for the NIFT exam. Best Coaching Institute for Design Entrance Exam, Here are some ways in which the best NIFT exam coaching in Delhi can improve your chances:

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