An educational crypto, finance, and AI vocabulary puzzle about transaction processing, business cash movements, model inputs, and dataset checks.
Transaction Lifecycle
These terms describe preparing, signing, distributing, and including a blockchain transaction.
Transaction ConstructionDigital SignatureTransaction BroadcastBlock Inclusion
- Transaction Construction: Transaction construction assembles fields such as the recipient, amount, and fee settings before signing.
- Digital Signature: A digital signature lets others verify that a message was signed using the private key corresponding to a public key.
- Transaction Broadcast: Transaction broadcast distributes a signed transaction to network peers for propagation and processing.
- Block Inclusion: Block inclusion occurs when a transaction becomes part of a block; inclusion alone does not guarantee finality.
Business Cash Flows
Cash flow categories separate core operations, long-term investments, and financing; net cash flow measures their combined effect.
Operating Cash FlowInvesting Cash FlowFinancing Cash FlowNet Cash Flow
- Operating Cash Flow: Operating cash flow is cash generated or used by the principal revenue-producing activities of a business.
- Investing Cash Flow: Investing cash flow records cash used to acquire or received from disposing of long-term assets and investments.
- Financing Cash Flow: Financing cash flow records cash movements involving borrowings and owner capital, such as debt issuance or dividend payments.
- Net Cash Flow: Net cash flow is the difference between cash received and cash paid during a period.
Language Model Inputs
These terms connect text segmentation, numeric token references, positional information, and input capacity.
TokenizerToken IDPositional EncodingContext Window
- Tokenizer: A tokenizer splits or maps text into units that a language model can represent as tokens.
- Token ID: A token ID is an integer that identifies a token in a tokenizer vocabulary.
- Positional Encoding: Positional encoding supplies information about token positions so a model can account for sequence order.
- Context Window: A context window is the maximum token span a model can process in a request, subject to its input and output limits.
Data Quality Checks
These checks test missing values, repeated records, expected data types, and allowed numeric boundaries.
Null CheckDuplicate CheckType CheckRange Check
- Null Check: A null check identifies fields whose values are missing or explicitly null.
- Duplicate Check: A duplicate check finds records that repeat according to selected fields or a defined identity rule.
- Type Check: A type check tests whether a value matches its expected data type, such as a number or a string.
- Range Check: A range check tests whether a value falls within specified lower and upper bounds.