Preparation is Essential in Making a Positive Impression at a Psw Career Fair

First impressions are everything. For graduating college students, making a positive impression to potential employers is key to landing a full-time career upon graduation. When attending a career fair, it’s normal to be nervous when so many recruiters are in one room. But a little preparation can go a long way in ensuring students are ready with the right attitude, attire and documents to make a lasting impression with employers.
On Oct. 18, approximately 30 students who are studying to be Personal Support Workers (PSW) attended a career fair at Evergreen College in Toronto. The students were all in various stages of their academic program, with some being in the classroom stage, while others were in a job placement.
In what was the first-ever career fair at the college, six industry employers attended the fair. Five of these organizations were hiring agencies and one represented a local long-term care facility. Margaret McLeish, the college’s PSW Program Manager, said this fair was organized based on the large number of employers wanting to hire the college’s students and meet with them.
Typically, the goal for employers and recruiters at a job fair is to meet the students and-hopefully-position them at the top of their hiring list.
Preparation will propel many PSW students to the top of this list. From reviewing their resume to practicing interview skills, preparing a portfolio and dressing for success; below are some suggestions to help students prepare for their next career fair.
Research the organizations attending the fair
Prior to attending a career fair, take some time to look up information about the attending organizations. Learning about their location, work culture and job postings can help students get an idea of the type of position(s) they would be hiring for. Also, identify any gaps in information to help fuel any questions that the students would want to ask at the fair.
Typically, this type of information can be found on each company’s website under the “careers” or “about us” section. Knowing the types of organizations that are attending can go a long way in helping students to complete the following preparation tasks, such as creating a targeted resume and developing a list of questions for each employer.
Refresh the resume
The first step to attending any job fair involves polishing up an existing resume. If a student has not been active in the job market, their resume may require some updates to reflect new credentials, educational achievements or even part-time employment. Students may find it beneficial to ask an instructor to review their resume prior to the fair to ensure it’s well targeted to the industry and position they’re seeking.
On the day of the fair, students should print out several copies of their resume to leave with each employer. Some companies may ask for more than one copy of a student’s resume, so it’s important to bring many photocopies. Carrying these in a folder, along with other important documents, a notebook and a pen will enable students to present themselves in an organized fashion.
Practice an introductory pitch
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Students will often have a minute or two to introduce themselves to employers at a fair. They can make the most of this short time by rehearsing what they want to say. An essential pitch includes a brief, personal introduction, an overview of skills, along with a sentence or two about a student’s career goals.
Next, students will often be able to display the documents they have brought with them, and take a recruiter through their resume verbally. As part of their preparation, students should plan to give an example of each attribute listed on their resume. Whether it’s facility experience as a PSW or customer-service traits from a previous career it’s important to be able to back up each item with specific examples.
Prepare a list of questions for employers
Career fairs present a unique opportunity versus traditional job applications. Students will be able to speak with recruiters directly and ask any questions they have prior to an official interview. Preparing these questions in advance is important to getting the most out of each interaction.
Students will want to avoid asking simple questions that they can find the answers to on a company’s website. For example, asking a recruiter where the company is located or what types of positions they are hiring for could demonstrate that a student has not done their research. It would also be wise to avoid any discussion about salaries or benefits unless it’s something that the recruiter brings up.
Instead, aspiring PSWs could ask employers where they might be situated, what kind of support they offer to PSWs working in the community, and how and when PSWs can they get a hold of someone if they have a question or need backup support in the field.

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React Native is an open-source framework for building mobile applications using JavaScript and React, a popular JavaScript library for building user interfaces. Developed by Facebook, React Native allows developers to create mobile apps for multiple platforms, such as iOS and Android, using a single codebase. This approach is often referred to as “write once, run anywhere” or “cross-platform” development.

Here are the key features and concepts of React Native:
JavaScript and React: React Native leverages JavaScript, one of the most widely used programming languages, and React, a JavaScript library for building user interfaces. Developers can use their existing knowledge of JavaScript and React to build mobile apps.

Native Components: React Native provides a set of pre-built, platform-specific components that are mapped to native user interface elements. These components ensure that the resulting mobile app looks and feels like a native app, offering a seamless user experience.

Single Codebase: With React Native, developers write a single codebase that can be used to build apps for both iOS and Android. This reduces development time and effort compared to building separate native apps for each platform.

Hot Reloading: React Native offers a feature called “hot reloading,” which allows developers to see the immediate effects of code changes in the running app, making the development process more efficient.

Third-Party Libraries: Developers can easily integrate third-party libraries and modules, both JavaScript and native, to extend the functionality of their React Native apps.

Native Module Access: React Native provides a bridge that allows developers to access native platform features and libraries when necessary. This ensures that developers can leverage platform-specific capabilities.

Community and Ecosystem: React Native has a vibrant community and a large ecosystem of open-source libraries and tools. This makes it easy to find resources, solutions to common problems, and community-contributed packages.

Performance: React Native apps often exhibit good performance because they are not running in a webview but are compiled to native code. However, there might still be some performance differences compared to fully native apps for certain use cases.

Developer Tooling: React Native is supported by various development tools, including debugging tools and integrated development environments (IDEs) such as Visual Studio Code.

Expo: Expo is a set of tools and services built on top of React Native that simplifies the development process even further. It provides a range of pre-configured features and a development client for quick testing on physical devices.

React Native is a popular choice for mobile app development, especially for projects where time-to-market and code reusability across platforms are essential. It has been used to build many successful apps, including Facebook, Instagram, Airbnb, and many more. However, developers should be aware that while React Native offers cross-platform advantages, some platform-specific features may still require native development expertise.

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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