F200 TA3: Accessing Data Across Platforms

1 State what is meant by an API. 2

One mark for each of the following, to a maximum of 2.

• A defined interface between two systems
• Without either needing to know the other's internal workings, or for data or functionality

Model answer. An API is a defined interface that lets one system request data or functionality from another, without either system needing to know how the other works internally.

2 Explain the difference between authentication and authorisation in a data system. 4

One mark for each of the following, to a maximum of 4.

• Authentication establishes identity
• An example of authentication
• Authorisation determines what is permitted
• An example, or why the distinction matters

Model answer. Authentication establishes who a user or system is, using something they know, have or are: a password with a second factor, a passkey, a certificate or an API token. Authorisation then determines what that identity is permitted to do, which is usually expressed through roles, so an analyst can read the sales table but not delete it and cannot see the payroll table at all. The distinction matters because the two fail differently: an HTTP 401 means the system does not recognise you, while a 403 means it recognises you perfectly well and the answer is still no, and fixing one would not fix the other.

3 A hospital wants to give its research team access to patient records for analysis. Explain three access control measures that should be applied, and why each is necessary. 6

One mark for each of the following, to a maximum of 6.

• Least privilege or role-based access control
• Why it is necessary here
• Pseudonymisation or masking of identifiers
• Why it is necessary here
• Audit logging, or row and column level security
• Why it is necessary here

Model answer. The team's access should be role based and set to least privilege: a research role granted read-only access to the specific dataset the approved study needs, rather than individual grants on the live clinical system. That matters because health records are special category data under the UK GDPR, the research purpose is narrower than clinical care, and a role can be audited and withdrawn in one change when the study ends. The data should be pseudonymised before it reaches them, with names, addresses and NHS numbers replaced by a study identifier and the linking key held separately by the clinical team, because researchers almost never need to know which individual a record belongs to and removing that need removes most of the risk. Finally every access should be logged and reviewed, with row level security limiting each researcher to their approved cohort, because logging is what makes misuse detectable and investigable rather than merely prohibited, and a hospital has to be able to evidence who looked at a record and why.

4 A growing company is deciding whether to keep its data warehouse on its own servers or move it to a cloud provider. Discuss the factors it should consider and recommend an approach. 8

One mark for each of the following, to a maximum of 8.

• Cost shape is compared
• Scalability is compared
• Maintenance and staffing are addressed
• Resilience and backup are addressed
• Security and configuration risk are addressed
• Legal or data location issues are addressed
• A clear recommendation is made
• The recommendation is justified against the drawbacks raised

Model answer. The decision turns on five factors. Cost shape comes first: on premise is a capital purchase that has to be sized for the busiest hour of the year and then sits idle, while cloud is an operating cost that tracks use, which suits a company whose volumes are growing and unpredictable but which becomes expensive if a steady, heavy workload is left running continuously. Scalability follows from that: cloud capacity changes in minutes and can be reduced again, whereas on premise growth means specifying, buying, racking and configuring hardware weeks in advance. Maintenance matters more than it first appears, because a growing company rarely has staff to spare: a managed cloud warehouse removes patching, backup and hardware replacement from a small team, and that reclaimed time is a real saving rather than an accounting one. Resilience favours cloud too, since multi-region redundancy and tested restores are available as configuration rather than as a second data centre. Against all that sit two genuine risks. Security does not improve automatically; the dominant cause of cloud data exposure is misconfiguration, so the company would need enforced encryption, role-based access, least privilege and scheduled access reviews. Legally, it must know and control where data physically sits, put a processor contract with appropriate safeguards in place, and check whether any dataset is subject to restrictions on international transfer. My recommendation is a cloud warehouse with a hybrid exception: move the analytical warehouse to the cloud, where the elasticity and reduced maintenance burden genuinely fit a growing company, and keep any regulated or special category data on premise or in a specifically contracted region. That answers the two drawbacks directly, because the configuration risk is addressed by governance the company needs in any case, and the legal risk is addressed by not moving the data that creates it.