Free CDL practice questions

Cloud computing is the on-demand delivery of computing resources, such as servers, storage and software, over a network from a provider's shared pool, billed by use.

The defining features are on-demand self-service, network access, shared provider-run resources and pay-for-what-you-use billing. A backup site the company owns, a fixed hardware lease and a laptop sync tool each miss at least one of these: either the company still owns and runs the capacity, or the capacity and price are fixed in advance.

1. Recall the defining traits of cloud: on demand, over a network, shared provider resources, pay as you go.
2. Check who owns and runs the resources in each option.
3. Check whether capacity and cost follow use or are fixed up front.
4. Pick the option that has every trait.

Exam tip: Cloud = provider-run, shared resources you reach over a network, take on demand and pay for as you use them.

One core value of data is real-time business insight: acting on what is happening now instead of on a report that arrives after the moment has passed.

The deciding requirement is acting 'while customer demand is still changing'. That needs insight produced as data arrives. Archives and monthly reports are tempting because the chain already has them, but they are backward-looking. Third-party trend data is general rather than specific to the chain, and governance rules protect data without speeding up insight.

1. Find the business need: act during the day, not after the month ends.
2. Ask which kind of insight closes that delay.
3. Recognise that real-time data shows events as they happen.
4. Reject options that are historical, external or about control rather than speed.

Exam tip: When the business must react while events unfold, the value of data is real-time insight.

Artificial intelligence is the broad field of systems that perform tasks needing human-like intelligence; machine learning is the subset of AI where systems learn from data.

Machine learning sits inside AI. Its defining trait is learning patterns from data rather than following instructions written by a programmer. The tempting distractors reverse the nesting (ML wider than AI, or ML inside generative AI) or confuse ML with reporting, which is business intelligence.

1. Recall the nesting: AI contains ML, and ML contains generative AI.
2. Identify ML's defining trait: it learns from data.
3. Reject options that reverse the nesting.
4. Reject the option that describes reporting rather than learning.

Exam tip: AI is the field, ML is how many AI systems learn from data, and gen AI is a kind of ML that creates content.

A workload is an application or service, plus the compute, storage and data it depends on, treated as one unit in a migration.

Migration programs are organized around workloads: each application and its supporting resources is assessed, given a migration approach and scheduled into a wave. Capacity bought, a server list and the wave plan are all related, but none of them is the workload.

1. Ask what is actually being moved in each migration wave.
2. Recognize that it is an application or service together with its resources.
3. Separate that from billing capacity, hardware lists and plans.

Exam tip: A workload is the application plus what it needs to run; migrations move workloads, not servers.

Ransomware is malware that encrypts or locks data and demands payment to restore access; its business impact is downtime, lost data and pressure to pay.

Encrypting the victim's files and demanding money for the key is the signature of ransomware. Phishing is often the delivery route, and a virus is a broad malware type, but neither names the attack the hospital is experiencing. Cryptomining abuses compute rather than holding data hostage.

1. Spot the two clues: files encrypted, and payment demanded for the key.
2. Recall that holding data hostage for payment defines ransomware.
3. Separate the delivery method (phishing) from the attack itself.
4. Choose ransomware.

Exam tip: Data locked and a ransom demanded = ransomware.