ITEC 610 Midterm Exam Questions: Answer The Following Five Q ✓ Solved

ITEC 610 Midterm Exam Questions: Answer the following five q

1. You have just been hired to work in a brand new store that sells health and nutrition related products including vitamins, supplements, diet and energy products. You know the value of information systems and want to convince the owner (who knows only how to manage a vitamin store) that technology can help the business. List at least four business-related competitive strategies (not technologies) that will address the potential risks an organization may encounter as it strives to maintain or improve its position in the marketplace. Also, for each competitive strategy you identify, give an IT technology example that can apply to support that strategy in the business.

2. Identify and describe four technologies developed and/or advanced by Amazon or Google and explain how each impacts society positively and negatively. At least one technology must be related to networks and at least one should be related to mobile technology. Indicate which technology is related to networks and which relates to mobile technology.

3. When making decisions about how to acquire hardware, software, or any other components of the IT Infrastructure, consideration is given to the following four characteristics of an IT infrastructure: Dependable; Manageable; Adaptable; Affordable. Evaluate cloud computing in relation to each of these four criteria.

4. Explain artificial intelligence, describe how it could be used in the battle against the Covid-19 virus (no generic answers accepted) and discuss potential risks with its use (no generic answers accepted).

5. Describe how applying big data technology to social media can be useful for: (1) a chain of fitness centers, (2) a large government agency, (3) an outpatient clinic and (4) a global online university. Finally, summarize the negative aspects of applying social media data in this way and identify ways to mitigate these concerns.

Paper For Above Instructions

Introduction

This paper answers five midterm questions for the ITEC 610 exam. Each section addresses a specific prompt: competitive business strategies for a health and nutrition store (with IT examples), four Amazon/Google technologies and societal impacts, evaluation of cloud computing vs four infrastructure criteria, concrete AI uses for COVID-19 and associated risks, and big-data/social-media applications for four types of organizations plus mitigation of harms. Citations are provided throughout.

1. Competitive strategies for a health & nutrition store (with IT examples)

Four business-related competitive strategies that mitigate marketplace risks are:

  • Differentiation through product/service expertise: Focus on curated product lines, certified staff advice, and customer education. IT support: an expert knowledge base and customer relationship management (CRM) with integrated product recommendation engines to record customer preferences and training modules for staff (CRM systems enable personalized service and retention) (Gandomi & Haider, 2015).
  • Operational excellence and cost leadership: Streamline inventory and reduce spoilage to lower costs. IT support: inventory management integrated with point-of-sale (POS) and supplier portals (cloud-based inventory with automated reordering improves turnover and reduces stockouts) (Armbrust et al., 2010).
  • Convenience and omnichannel presence: Provide in-store, online ordering, curbside pickup, and delivery to capture different customer segments. IT support: an e-commerce platform with mobile ordering and location-based services (mobile app and web storefront) to unify channels and improve customer lifetime value (Kaplan & Haenlein, 2010).
  • Community and loyalty ecosystem: Build a local wellness community through loyalty programs, classes, and social engagement. IT support: loyalty-management integrated with social media analytics and targeted email campaigns to increase repeat visits and gather feedback (Gandomi & Haider, 2015).

Each strategy addresses risks: differentiation reduces price competition; operational excellence mitigates margin erosion; omnichannel reduces loss of customers to online rivals; community/loyalty counters churn. Technology examples are tools to implement these strategies, not strategies themselves (Mell & Grance, 2011).

2. Four Amazon/Google technologies and societal impacts

1) TensorFlow (Google) — AI framework: Positive: accelerates ML research and deployment in healthcare, retail personalization, and diagnostics. Negative: lowers barrier to misuse (biased models, surveillance), and fuels automation that displaces some jobs (Abadi et al., 2016).

2) Android (Google) — mobile operating system (mobile technology): Positive: broad mobile access, app ecosystem enabling health apps, telehealth, and mobile commerce. Negative: fragmentation and security/privacy challenges on unpatched devices; data-collection practices raise privacy concerns (Kaplan & Haenlein, 2010).

3) AWS global backbone & networking services (Amazon) — networks technology: Positive: resilient global infrastructure for startups, healthcare analytics, and government services; enables scalable, on-demand computing. Negative: concentration of infrastructure risk and vendor lock-in; outages can cascade across many dependent services (Armbrust et al., 2010).

4) Google Maps & Location Services (Google) — location/mobile-enabled service: Positive: powers logistics, local business discovery, contact tracing support and delivery optimization. Negative: location tracking can be repurposed for intrusive surveillance and re-identification risks if data are mishandled (Ferretti et al., 2020).

3. Cloud computing vs Dependable, Manageable, Adaptable, Affordable

Dependable: Major cloud providers offer high availability, geographic redundancy, and SLAs that improve dependability versus single-site on-prem deployments; however dependency on provider availability and potential regional outages present systemic risk (Mell & Grance, 2011).

Manageable: Cloud platforms centralize management, provide dashboards, orchestration and automated patching which reduce administrative burden. They also require new skills (cloud ops, IaC) and governance to avoid sprawl (Armbrust et al., 2010).

Adaptable: Cloud elasticity (auto-scaling, microservices) allows rapid adaptation to demand spikes and evolving business models; portability challenges and architecture design influence real adaptability (use of containers and serverless patterns increases adaptability) (Mell & Grance, 2011).

Affordable: Cloud shifts capital expenditure to operational expenditure and can reduce total cost for many workloads through pay-as-you-go pricing. Yet poorly optimized usage, egress fees, and lack of cost controls can make cloud expensive; cost governance is essential (Armbrust et al., 2010).

4. Artificial intelligence for COVID-19: concrete uses and risks

AI defined: systems that perform tasks that normally require human intelligence, using statistical learning, planning, and perception (Abadi et al., 2016).

Concrete COVID-19 applications:

  • Protein structure prediction to accelerate therapeutics: Deep learning models such as AlphaFold predict protein folding to identify viral protein structures and potential drug targets; this speeds in silico screening and rational drug design (Jumper et al., 2021).
  • Automated CT/Chest X‑ray triage: Specialized convolutional neural networks can flag probable COVID-19 pneumonia patterns in CT scans to prioritize radiologist review in overwhelmed hospitals (Li et al., 2020).
  • Contact-tracing risk scoring: ML models that combine proximity logs, time, mask-wearing and local prevalence to compute exposure risk scores powering targeted quarantine recommendations (Ferretti et al., 2020).
  • Supply-demand forecasting for PPE and oxygen: Time-series ML models on hospital usage and mobility data optimize supply chain allocation to prevent local shortages.

Risks (non-generic): model bias from non-representative training data (e.g., radiology models trained on hospitalized patients may underperform on community cases), false reassurance from low-specificity triage models leading to missed diagnoses, and privacy harms when proximity/location models are deanonymized—especially if combined with commercial datasets (Li et al., 2020; Jumper et al., 2021). Mitigation requires diverse datasets, clinical validation, calibration, transparent model cards, and strict legal/privacy safeguards (GDPR-style policy) (Abadi et al., 2016).

5. Big data + social media: use cases and mitigations

Use cases:

  • Chain of fitness centers: Social listening and engagement analytics identify trending classes, local influencers, and member sentiment; targeted promotions and churn prediction models increase retention and tailor class schedules (Gandomi & Haider, 2015).
  • Large government agency: Social media trend analysis supports crisis detection, vaccine misinformation monitoring, and sentiment mapping to target public messaging and resource allocation (Ferretti et al., 2020).
  • Outpatient clinic: Patient sentiment analysis and appointment feedback mining improve service quality, enable automated appointment reminders, and detect emerging local symptom clusters to prioritize testing.
  • Global online university: Learner engagement analytics from social platforms and forums enable personalized course recommendations, early-warning signals for at-risk students, and community-building strategies to boost retention (Kaplan & Haenlein, 2010).

Negative aspects and mitigations:

Privacy invasion and re-identification risk: mitigate via anonymization, aggregation, differential privacy, and strict data minimization policies (GDPR principles). Bias and representativeness: social media over/under-represents groups; combine multiple data sources, stratify analyses, and report limitations. Misinformation amplification and ethical misuse: adopt transparency, human-in-the-loop review, and governance boards. Legal/compliance risks: implement contract controls, data processing agreements, and regular audits (Mell & Grance, 2011).

Conclusion

Technology supports business strategies rather than replaces them. For the health and nutrition store, strategic choices (differentiation, operational excellence, omnichannel, loyalty) paired with specific technologies will mitigate market risks. Technologies from Google and Amazon bring societal benefits but also concentration, privacy, and bias risks. Cloud computing typically improves dependability, manageability, adaptability, and can be affordable if governed. AI offers concrete COVID-19 benefits—protein prediction, imaging triage, contact-risk scoring—but requires careful validation and privacy safeguards. Big data from social media yields operational and strategic insights across sectors, but practitioners must mitigate privacy, bias, and ethical risks through technical and governance controls.

References

  • Abadi, M., et al. (2016). TensorFlow: Large-scale machine learning on heterogeneous systems. arXiv. Retrieved from https://arxiv.org/abs/1603.04467
  • Armbrust, M., et al. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58.
  • Ferretti, L., et al. (2020). Quantifying SARS-CoV-2 transmission suggests epidemic control with digital contact tracing. Science, 368(6491), eabb6936.
  • Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.
  • Jumper, J., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596, 583–589.
  • Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of social media. Business Horizons, 53(1), 59–68.
  • Li, L., et al. (2020). Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology: Artificial Intelligence, 2(2), e200095.
  • Mell, P., & Grance, T. (2011). The NIST definition of cloud computing. NIST Special Publication 800-145.
  • European Union. (2016). General Data Protection Regulation (GDPR). Official Journal of the European Union.
  • AWS. (2019). Overview of Amazon Web Services. Amazon Web Services whitepaper. Retrieved from https://aws.amazon.com/whitepapers/

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