Are flashcards effective for the DataSys+ exam?

Yes. Flashcards use active recall — retrieving an answer from memory — which research shows is one of the most effective study methods, especially in short, spaced sessions. They're ideal for the DataSys+'s heavy memorization load: SQL commands, join types, normal forms, RAID levels, and security terms.

What topics do the DataSys+ flashcards cover?

All five DS0-001 domains: Database Fundamentals (SQL, keys, joins, normalization, ACID), Database Deployment (models, environments, OLTP/OLAP), Database Management and Maintenance (indexing, performance, concurrency), Data and Database Security (access control, encryption, threats), and Business Continuity (backups, RAID, disaster recovery).

How should I study with these DataSys+ flashcards?

Lead with the heaviest domains — Database Management & Maintenance (25%) and Database Fundamentals (24%). Drill the SQL command categories and join types with Type and Quiz modes until automatic, then mix in the other domains. Pair the cards with our full practice test before exam day.

Are the DataSys+ flashcards free?

Yes — 100% free, all four study modes, no paywall and no sign-up.

Do these flashcards match the current DataSys+ exam blueprint?

Yes. The cards are organized to the current CompTIA DataSys+ DS0-001 exam objectives, covering all five scored domains in their official proportions. DataSys+ is the database-administration certification, distinct from CompTIA Data+ (analytics).

Atomicity, Consistency, Isolation, Durability — the four guarantees of a reliable database transaction.

A transaction completes fully or not at all — partial changes are rolled back.

A transaction moves the database from one valid state to another, honoring all constraints.

Concurrent transactions don't interfere; each behaves as if it ran alone.

Once committed, changes survive crashes and power loss (persisted to non-volatile storage).

Stores data in tables (relations) of rows and columns with a defined schema; queried with SQL.

A column (or set) that uniquely identifies each row; cannot be null or duplicated.

A column that references another table's primary key, enforcing referential integrity.

A primary key made up of two or more columns combined.

Any column or set of columns that could serve as the primary key (uniquely identifies a row).

An artificial, system-generated key (e.g., auto-increment id) used instead of natural data.

A key made from real, meaningful data that already exists (e.g., email or SSN).

Referential integrity

The rule that every foreign-key value must match an existing primary-key value (no orphans).

The rule that every table has a primary key and no primary-key value is null.

The structure/blueprint of a database: tables, columns, data types, keys, and relationships.

A single record in a table — one set of related values across all columns.

A named field in a table holding one type of value for every row.

The nature of a relationship between tables: one-to-one, one-to-many, or many-to-many.

Organizing tables to reduce redundancy and avoid update/insert/delete anomalies.

First Normal Form (1NF)

Each cell holds a single atomic value; no repeating groups; each row is unique.

Second Normal Form (2NF)

1NF and every non-key column depends on the whole primary key (no partial dependencies).

Third Normal Form (3NF)

2NF and no non-key column depends on another non-key column (no transitive dependencies).

Boyce-Codd Normal Form — a stricter 3NF where every determinant is a candidate key.

Deliberately adding redundancy to speed up read-heavy queries, trading off integrity.

Data Definition Language — CREATE, ALTER, DROP, TRUNCATE; defines and changes structure.

Data Manipulation Language — SELECT, INSERT, UPDATE, DELETE; reads and changes data.

Data Control Language — GRANT and REVOKE; controls permissions.

Transaction Control Language — COMMIT, ROLLBACK, SAVEPOINT; manages transactions.

DML statement that retrieves rows from one or more tables.

DML statement that adds a new row to a table.

DML statement that modifies existing rows that match a condition.

DML statement that removes rows matching a WHERE clause; can be rolled back.

DDL statement that removes all rows from a table quickly and resets it.

DDL statement that removes an entire table (structure and data) from the database.

Filters rows in a query to those that meet a condition.

Groups rows that share values so aggregate functions apply per group.

Filters groups after GROUP BY (WHERE filters rows before grouping).

Sorts the result set by one or more columns, ascending or descending.

Removes duplicate rows from a SELECT result set.

Returns only rows with matching values in both joined tables.

Returns all rows from the left table plus matches from the right (NULLs where none).

Returns all rows from the right table plus matches from the left (NULLs where none).

Returns all rows from both tables, with NULLs where either side has no match.

Returns the Cartesian product — every row of one table paired with every row of the other.

A table joined to itself, often to compare rows within the same table.

Your FREE CompTIA DataSys+ Flashcards 2026 – 250+ Cards

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DataSys+ Flashcards

ACID: Atomicity, Consistency, Isolation, Durability — the four guarantees of a reliable database transaction.
Atomicity (ACID): A transaction completes fully or not at all — partial changes are rolled back.
Consistency (ACID): A transaction moves the database from one valid state to another, honoring all constraints.
Isolation (ACID): Concurrent transactions don't interfere; each behaves as if it ran alone.
Durability (ACID): Once committed, changes survive crashes and power loss (persisted to non-volatile storage).
Relational database: Stores data in tables (relations) of rows and columns with a defined schema; queried with SQL.
Primary key: A column (or set) that uniquely identifies each row; cannot be null or duplicated.
Foreign key: A column that references another table's primary key, enforcing referential integrity.
Composite key: A primary key made up of two or more columns combined.
Candidate key: Any column or set of columns that could serve as the primary key (uniquely identifies a row).
Surrogate key: An artificial, system-generated key (e.g., auto-increment id) used instead of natural data.
Natural key: A key made from real, meaningful data that already exists (e.g., email or SSN).
Referential integrity: The rule that every foreign-key value must match an existing primary-key value (no orphans).
Entity integrity: The rule that every table has a primary key and no primary-key value is null.
Schema: The structure/blueprint of a database: tables, columns, data types, keys, and relationships.
Tuple / row: A single record in a table — one set of related values across all columns.
Attribute / column: A named field in a table holding one type of value for every row.
Cardinality: The nature of a relationship between tables: one-to-one, one-to-many, or many-to-many.
Normalization: Organizing tables to reduce redundancy and avoid update/insert/delete anomalies.
First Normal Form (1NF): Each cell holds a single atomic value; no repeating groups; each row is unique.
Second Normal Form (2NF): 1NF and every non-key column depends on the whole primary key (no partial dependencies).
Third Normal Form (3NF): 2NF and no non-key column depends on another non-key column (no transitive dependencies).
BCNF: Boyce-Codd Normal Form — a stricter 3NF where every determinant is a candidate key.
Denormalization: Deliberately adding redundancy to speed up read-heavy queries, trading off integrity.
DDL: Data Definition Language — CREATE, ALTER, DROP, TRUNCATE; defines and changes structure.
DML: Data Manipulation Language — SELECT, INSERT, UPDATE, DELETE; reads and changes data.
DCL: Data Control Language — GRANT and REVOKE; controls permissions.
TCL: Transaction Control Language — COMMIT, ROLLBACK, SAVEPOINT; manages transactions.
SELECT: DML statement that retrieves rows from one or more tables.
INSERT: DML statement that adds a new row to a table.
UPDATE: DML statement that modifies existing rows that match a condition.
DELETE: DML statement that removes rows matching a WHERE clause; can be rolled back.
TRUNCATE: DDL statement that removes all rows from a table quickly and resets it.
DROP: DDL statement that removes an entire table (structure and data) from the database.
WHERE clause: Filters rows in a query to those that meet a condition.
GROUP BY: Groups rows that share values so aggregate functions apply per group.
HAVING clause: Filters groups after GROUP BY (WHERE filters rows before grouping).
ORDER BY: Sorts the result set by one or more columns, ascending or descending.
DISTINCT: Removes duplicate rows from a SELECT result set.
INNER JOIN: Returns only rows with matching values in both joined tables.
LEFT JOIN: Returns all rows from the left table plus matches from the right (NULLs where none).
RIGHT JOIN: Returns all rows from the right table plus matches from the left (NULLs where none).
FULL OUTER JOIN: Returns all rows from both tables, with NULLs where either side has no match.
CROSS JOIN: Returns the Cartesian product — every row of one table paired with every row of the other.
SELF JOIN: A table joined to itself, often to compare rows within the same table.
Aggregate function: Computes a single value over a set of rows: COUNT, SUM, AVG, MIN, MAX.
COUNT(): Aggregate function returning the number of rows (or non-null values).
View: A saved, named query that acts like a virtual table; simplifies access and can restrict columns.
Index: A structure that speeds row lookups by a column's value without scanning the whole table.
Data redundancy: The same data stored in more than one place — a source of inconsistency normalization reduces.
NULL: A marker for a missing or unknown value — not zero and not an empty string.
Constraint: A rule enforced on a column: PRIMARY KEY, FOREIGN KEY, UNIQUE, NOT NULL, CHECK, DEFAULT.
UNIQUE constraint: Ensures all values in a column (or set) are different across rows.
CHECK constraint: Restricts column values to those that satisfy a boolean condition.
Data type: Defines the kind of value a column stores: INT, VARCHAR, DATE, DECIMAL, BOOLEAN, etc.
VARCHAR vs CHAR: VARCHAR stores variable-length text; CHAR is fixed-length and padded.
SQL vs NoSQL: SQL = structured, fixed-schema relational tables; NoSQL = flexible schema, horizontal scale.
Document database: NoSQL store of self-describing documents (e.g., JSON) — example: MongoDB.
Key-value store: NoSQL store mapping unique keys to values — example: Redis.
Column-family store: NoSQL store organizing data by column families for wide rows — example: Cassandra.
Graph database: NoSQL store of nodes and relationships — example: Neo4j.
ERD: Entity-Relationship Diagram — a model of entities, attributes, and relationships in a design.
DBMS: Database Management System — software that stores, retrieves, and manages databases.
Stored procedure: A named, precompiled set of SQL statements executed by name, often with parameters.
Trigger: Code that runs automatically in response to an INSERT, UPDATE, or DELETE event.
Function (SQL): A routine that returns a value and can be used inside a query.
OLTP: Online Transaction Processing — many short real-time transactions; normalized for fast writes.
OLAP: Online Analytical Processing — complex analytical queries over large historical data; often denormalized.
ETL: Extract, Transform, Load — moves and reshapes data from sources into a warehouse.
ELT: Extract, Load, Transform — loads raw data first, then transforms it in the target system.
Data warehouse: A central repository of integrated historical data optimized for analysis and reporting.
Data lake: A store of raw data in native format at scale, for varied later processing.
Data mart: A subject-focused subset of a data warehouse for a specific team or use.
Star schema: A warehouse design: one central fact table linked to denormalized dimension tables.
Snowflake schema: A star schema whose dimension tables are normalized into related sub-tables.
Fact table: The central table in a warehouse holding measurable, numeric business events.
Dimension table: A table of descriptive attributes (who, what, where, when) joined to facts.
Database deployment: Installing, configuring, and making a database available in an environment.
Configuration baseline: A documented standard config used to deploy databases consistently and securely.
Infrastructure as code (IaC): Managing infrastructure and config as version-controlled files (e.g., Terraform, Ansible).
Database migration: Moving a database or its data/schema to a new version, server, or platform.
Schema migration: Applying versioned, repeatable changes to a database structure over time.
On-premises database: A database running on hardware the organization owns and manages itself.
Cloud database: A database hosted/managed in a cloud provider's environment (IaaS, PaaS, or DBaaS).
DBaaS: Database as a Service — a fully managed cloud database (provider handles patching, backups, HA).
IaaS: Infrastructure as a Service — you manage the OS and DBMS on provider-supplied VMs.
PaaS: Platform as a Service — the provider manages the platform; you deploy databases/apps on it.
Scripting fundamentals: Using shell/Python/PowerShell + SQL scripts to automate deployment and admin tasks.
Idempotent script: A script that produces the same result no matter how many times it runs.
Connection string: Parameters an app uses to connect: server, database, credentials, and options.
ODBC / JDBC: Standard APIs/drivers that let applications connect to and query databases.
Containerized database: A database packaged in a container (e.g., Docker) for portable, repeatable deployment.
Staging environment: A production-like environment for testing deployments before go-live.
Sandbox / dev environment: An isolated environment for development and experimentation, separate from production.
Version control (DB): Tracking schema and script changes in a repository (e.g., Git) for review and rollback.
Data import/export: Bulk loading or extracting data via tools like BCP, COPY, or import/export utilities.
CSV: Comma-Separated Values — a plain-text format commonly used to import/export tabular data.
Database instance: A running copy of the DBMS engine managing one or more databases in memory and on disk.
Clustered index: Defines the physical sort order of a table's rows; only one per table.
Non-clustered index: A separate structure pointing back to rows; many allowed per table.
Composite index: An index on two or more columns, useful for multi-column lookups and sorting.
B-tree index: A balanced-tree structure that keeps lookups fast as data grows.
Covering index: An index that contains all columns a query needs, so the table isn't read.
Index fragmentation: Disordered index pages that slow reads; fixed by rebuilding or reorganizing.
Index rebuild: Recreates an index to remove fragmentation (heavier than a reorganize).
Index reorganize: Defragments index pages in place with lower overhead than a rebuild.
Statistics (DB): Metadata about data distribution the optimizer uses to choose a good plan.
Query optimizer: The DBMS component that chooses the execution strategy for a SQL statement.
Execution plan: The optimizer's step-by-step plan for running a query (scans, seeks, joins, sorts).
Table scan: Reading every row of a table — slow on large tables; a sign a useful index is missing.
Index seek: Using an index to jump directly to matching rows — efficient lookup.
Performance tuning: Improving speed via indexing, query rewriting, statistics, and configuration.
Query profiling: Measuring a query's resource use and timing to find bottlenecks.
Locking: Preventing conflicting concurrent access to data to preserve isolation.
Deadlock: Two transactions each hold a lock the other needs; the DBMS kills one as the victim.
Lock escalation: The DBMS converting many fine-grained locks into a coarser one to save resources.
Concurrency: Multiple transactions executing at the same time without corrupting data.
Isolation level: Sets how visible one transaction's changes are to others (read committed, serializable, etc.).
Dirty read: Reading uncommitted changes that may later be rolled back.
Phantom read: Rows appearing/disappearing between reads in a transaction due to other inserts/deletes.
Database job / scheduler: Automated, scheduled task: backups, index maintenance, statistics updates, cleanup.
Patching: Applying vendor updates to the DBMS to fix bugs and close security holes.
Maintenance plan: A scheduled set of upkeep tasks: backups, integrity checks, index/statistics maintenance.
Integrity check (DBCC): A consistency check (e.g., DBCC CHECKDB) that detects corruption in a database.
Capacity planning: Forecasting storage, memory, and CPU needs to keep performance acceptable as data grows.
Monitoring: Continuously tracking health metrics (CPU, memory, I/O, waits, blocking, errors).
Baseline (performance): A record of normal performance used to spot abnormal drift.
Alerting: Automated notifications when a metric crosses a threshold (e.g., low disk, high latency).
Log file (transaction log): Records all changes so transactions can be committed, rolled back, and recovered.
Log truncation: Reclaiming space in the transaction log after backups, preventing it from filling.
Partitioning: Splitting a large table across segments to improve manageability and performance.
Sharding: Horizontally splitting data across multiple servers to scale out.
Archiving: Moving old, rarely accessed data to cheaper storage to keep active tables lean.
Vacuum / cleanup: Reclaiming space from dead rows and updating statistics (e.g., PostgreSQL VACUUM).
Connection pooling: Reusing a set of database connections to reduce overhead and improve throughput.
Resource governor: A feature that caps CPU/memory per workload to prevent one query starving others.
CIA triad: Confidentiality, Integrity, Availability — the three core security goals.
Confidentiality: Keeping data accessible only to authorized parties (encryption, access control).
Integrity: Ensuring data is accurate and unaltered except by authorized changes.
Availability: Ensuring data and the database are accessible when needed.
Authentication: Verifying who a user or service is (passwords, keys, MFA, certificates).
Authorization: Determining what an authenticated user is allowed to do.
RBAC: Role-Based Access Control — assign permissions to roles, then users to roles.
Least privilege: Granting only the minimum access a role needs to do its job.
Principle of separation of duties: Splitting critical tasks among people so no one has unchecked control.
GRANT: SQL (DCL) command that gives a user or role a permission.
REVOKE: SQL (DCL) command that removes a previously granted permission.
SQL injection: Inserting malicious SQL via unsanitized input to read or alter data.
Parameterized query: A prepared statement that separates SQL code from input — the main SQL-injection defense.
Input validation: Checking and sanitizing user input to block malicious or malformed data.
Encryption at rest: Encrypting stored data on disk so files/backups are unreadable if stolen.
Encryption in transit: Encrypting data moving over the network (TLS) so it can't be intercepted.
TDE: Transparent Data Encryption — encrypts database files at rest automatically.
TLS: Transport Layer Security — the protocol that encrypts data in transit between client and server.
AES: Advanced Encryption Standard — a strong symmetric cipher widely used for data encryption.
Hashing: A one-way function producing a fixed-length digest; used to store passwords (with salt).
Salt: Random data added to a password before hashing to defeat precomputed (rainbow) attacks.
Key management: Securely generating, storing, rotating, and revoking encryption keys.
Data masking: Hiding sensitive values (e.g., showing only last 4 digits) for non-privileged users.
Tokenization: Replacing sensitive data with a non-sensitive token mapped in a secure vault.
Auditing: Recording who accessed or changed what and when, for accountability and compliance.
Audit log: An immutable record of security-relevant database events.
Data classification: Labeling data by sensitivity (public, internal, confidential, restricted) to drive controls.
PII: Personally Identifiable Information — data that can identify a person; requires protection.
GDPR: EU regulation governing personal-data privacy, consent, and breach notification.
HIPAA: U.S. law protecting the privacy and security of health information (PHI).
PCI DSS: Security standard for organizations that handle payment-card data.
Privilege escalation: An attacker gaining higher permissions than they were granted.
Defense in depth: Layering multiple security controls so no single failure exposes the data.
Database firewall: A control that monitors and filters SQL traffic to block malicious queries.
Backup encryption: Encrypting backup files so stolen backups don't expose data.
Account lockout: Disabling an account after repeated failed logins to slow brute-force attacks.
Service account: A non-human account an application uses; should run with least privilege.
RPO: Recovery Point Objective — the maximum tolerable data loss; drives backup frequency.
RTO: Recovery Time Objective — the maximum tolerable downtime; drives recovery design.
Full backup: A complete copy of all data; fastest restore, most storage and time.
Differential backup: Copies all changes since the last FULL backup; restore = full + latest differential.
Incremental backup: Copies changes since the last backup of ANY type; smallest, but slowest restore.
3-2-1 rule: Keep 3 copies of data, on 2 media types, with 1 copy off-site.
Backup verification: Testing that backups actually restore — an untested backup is unproven.
Cold backup: A backup taken while the database is shut down (offline).
Hot backup: A backup taken while the database is running and online.
Point-in-time recovery: Restoring a database to a specific moment using backups plus transaction logs.
Snapshot: A read-only, point-in-time image of data used for fast recovery or cloning.
RAID 0: Striping for speed; no redundancy — one disk failure loses all data.
RAID 1: Mirroring — an identical copy on a second disk; survives one disk failure.
RAID 5: Striping with distributed parity; survives one disk failure; needs 3+ disks.
RAID 6: Double parity; survives two simultaneous disk failures; needs 4+ disks.
RAID 10: Mirrored pairs that are striped; high performance and redundancy.
High availability (HA): Designing systems to minimize downtime through redundancy and failover.
Failover: Automatically switching to a standby system when the primary fails.
Failover cluster: Multiple nodes sharing storage so a standby can take over the database service.
Replication: Keeping synchronized copies of a database on multiple servers.
Synchronous replication: Commits on the primary and replica together — zero data loss, higher latency.
Asynchronous replication: Replica lags slightly behind — lower latency, small risk of data loss.
Log shipping: Periodically copying and restoring transaction logs to a standby server.
Mirroring (DB): Maintaining a hot standby copy of a database for failover.
Disaster recovery (DR): The plan and process to restore service after a major outage or disaster.
DR site: A secondary location (hot, warm, or cold) used to resume operations after a disaster.
Hot site: A fully equipped, running DR site that can take over almost immediately.
Warm site: A partially ready DR site needing some setup/data load before takeover.
Cold site: A facility with infrastructure but no live systems/data — slowest to bring up.
Business continuity plan (BCP): An organization-wide plan to keep critical functions running during disruption.
MTTR: Mean Time To Repair — average time to fix a failed component.
MTBF: Mean Time Between Failures — average operating time between failures (reliability).
Geo-redundancy: Storing copies in geographically separate regions to survive a regional outage.
Retention policy: Rules for how long backups/data are kept before deletion or archive.
LIKE operator: Pattern-matches text in a WHERE clause using wildcards % (any string) and _ (one char).
IN operator: Tests whether a value matches any value in a list or subquery.
BETWEEN operator: Tests whether a value falls within an inclusive range.
Subquery: A query nested inside another query, used in SELECT, FROM, or WHERE.
UNION: Combines result sets of two queries and removes duplicates (UNION ALL keeps them).
Alias (AS): A temporary name given to a column or table in a query for readability.
COALESCE: Returns the first non-null value from a list of expressions.
CASE expression: Adds if/then/else conditional logic inside a SQL query.
Transaction: A unit of work treated as a single, all-or-nothing operation.
COMMIT: Permanently saves the changes made in the current transaction.
ROLLBACK: Undoes the changes made in the current transaction.
SAVEPOINT: A marker within a transaction you can roll back to without undoing everything.
One-to-many relationship: One row in a table relates to many rows in another (the most common relationship).
Many-to-many relationship: Rows on both sides relate to many — implemented with a junction (bridge) table.
Junction table: A bridge table that resolves a many-to-many relationship using two foreign keys.
Domain (column): The set of valid values a column may hold, enforced by data type and constraints.
Heap: A table with no clustered index — rows stored in no particular order.
Auto-increment / identity: A column that automatically assigns the next sequential number to new rows.
Temporal data: Time-based data; some databases support system-versioned (temporal) tables for history.
JSON column: A column storing JSON documents, blending relational and document storage.
Blue-green deployment: Two identical environments; switch traffic to the new one for zero-downtime releases.
Rollback (deployment): Reverting a deployment to the previous known-good version after a failure.
Smoke test: A quick post-deployment check that core functionality works.
Bulk insert: Loading many rows at once efficiently, bypassing row-by-row overhead.
Data transformation: Cleaning, reshaping, and converting data during ETL/ELT.
Data validation (load): Verifying imported data meets type, range, and integrity rules.
Connection encryption: Requiring TLS for client-server connections during deployment hardening.
Environment variable: A configuration value (e.g., credentials, host) supplied to a deployment at runtime.
Secrets management: Storing credentials/keys securely (vaults) rather than hard-coded in scripts.
Database link / linked server: A configured connection that lets one database query another remote database.
Replication setup: Configuring primary and replica roles during deployment for HA or read scaling.
Capacity sizing: Estimating CPU, memory, and storage before deploying a database.
Wait statistics: Metrics showing what the engine is waiting on (I/O, locks, CPU) to find bottlenecks.
Blocking: One transaction holding a lock that stalls another (not yet a deadlock).
Query hint: An instruction that overrides the optimizer's default choice for a query.
Parameter sniffing: The optimizer caching a plan based on the first parameter, hurting other values.
Plan cache: Stored execution plans reused to avoid recompiling frequent queries.
Recompilation: Generating a fresh execution plan when data or statistics change significantly.
Database integrity: The overall accuracy and consistency of data, protected by constraints and checks.
Orphaned user: A database user whose matching server login is missing — a common post-restore fix.
Tempdb / temp space: Workspace the engine uses for sorts, joins, and temporary objects.
Autogrowth: A setting that lets a data/log file expand automatically when it fills.
Compression (DB): Reducing storage and I/O by compressing data or backups.
Health check: A routine review of performance, errors, security, and backups.
Refresh statistics: Updating data-distribution stats so the optimizer makes good choices.
Maintenance window: A scheduled period for disruptive upkeep (patching, rebuilds) with low user impact.
MFA: Multi-Factor Authentication — requiring two or more proofs of identity.
Certificate authentication: Using digital certificates to verify identity instead of passwords.
Row-level security: Restricting which rows a user can see based on their identity or role.
Column-level security: Restricting access to specific sensitive columns within a table.
Dynamic data masking: Masking column values at query time for unauthorized users without changing stored data.
Vulnerability assessment: Scanning a database for misconfigurations and known weaknesses.
Penetration test: An authorized simulated attack to find exploitable security gaps.
Patch (security): A fix that closes a known vulnerability in the DBMS.
Brute-force attack: Trying many passwords/keys until one works; mitigated by lockout and strong policies.
Privilege creep: Users accumulating permissions over time beyond what they currently need.
Data breach: Unauthorized access to or disclosure of protected data.
Incident response: The plan to detect, contain, eradicate, and recover from a security incident.
Compliance audit: A formal review confirming controls meet a regulation or standard.
Data sovereignty: The principle that data is subject to the laws of the country where it is stored.
Secure baseline: A hardened default configuration applied to every database.
Standby database: A secondary copy kept ready to take over (warm/hot) if the primary fails.
Active-active: Both nodes serve traffic simultaneously, sharing load and providing redundancy.
Active-passive: One node serves traffic while a standby waits to take over on failure.
Quorum: The minimum number of cluster nodes that must agree for the cluster to stay online.
Split-brain: A cluster fault where two nodes both think they're primary — prevented by quorum/witness.
Witness / arbiter: An extra vote that breaks ties and prevents split-brain in a cluster.
Backup window: The time period available to run backups without harming performance.
Backup retention: How long backup copies are kept before deletion (driven by policy/compliance).
Offsite backup: A backup stored in a separate location to survive a site-level disaster.
Tabletop exercise: A walkthrough drill that tests a DR/BCP plan without real failover.
Recovery testing: Periodically restoring backups to prove they work and meet the RTO.
Bare-metal recovery: Restoring a full system, including OS, from backup onto new hardware.
Continuous data protection: Capturing every change so you can restore to nearly any point in time.
SLA: Service Level Agreement — the committed uptime/recovery targets for a service.

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By Career EmployerUpdated Jun 19, 2026

250+ free DataSys+ flashcards — 4 ways to study

DataSys+ Flashcard of the Day

The classic card. Do you know this one?

Click Study Flashcards above to open the flashcard hub — hundreds of DataSys+ cards you can flip, match, type, or quiz yourself on. Every card is drawn from the five official DS0-001 domains, so you study exactly what the exam tests.^[1] Pair them with our free practice test and study guide.

CompTIA DataSys+ is one of the 14 CompTIA certifications — explore our CompTIA flashcards to compare and prep across the whole family.

DataSys+ Flashcard Study Modes

Most flashcard sites give you one thing: a card to flip. Ours has four modes so you can both learn the material and prove you know it — the difference between recognizing an answer and recalling it under exam pressure.

Flip (Study) — the classic card. Flip term ↔ definition, shuffle the deck, and mark each card “Got it” or “Still learning.”
Match (Game) — a timed game: pair each term to its definition as fast as you can. Great for cementing SQL commands, join types, and acronyms.
Type (Recall) — read the definition and type the term. Typing forces true active recall instead of passive recognition.
Quiz (Test) — multiple-choice questions generated from the cards, so you self-test exactly like exam day.

Free CompTIA DataSys+ flashcards from Career Employer — active recall for the DS0-001 exam

Why Flashcards Work for the DataSys+

Flashcards aren’t busywork — they’re built on active recall: pulling an answer out of memory strengthens it far more than re-reading notes. Pair that with spacing — short sessions across several days rather than one cram — and you retain more in less time.

The DataSys+ is dense with things you simply have to know cold — SQL command categories, join types, the normal forms, ACID, RAID levels, and security acronyms.^[1] Spaced flashcards are the most efficient way to keep it all fresh. Used alongside our practice test and study guide, they turn review time into measurable progress.

DataSys+ Flashcards by Domain

The cards are organized by the five official DS0-001 domains. Drill the highest-weighted ones first — Database Management & Maintenance and Database Fundamentals make up nearly half the exam:^[1]

DataSys+ flashcards by domain and weight
Domain	Exam weight
Database Management and Maintenance	25%
Database Fundamentals	24%
Data and Database Security	23%
Database Deployment	16%
Business Continuity	12%

How to Get the Most Out of These Flashcards

Lead with the heavy domains. Management & Maintenance (25%) and Fundamentals (24%) are nearly half the exam — start there.
Memorize the SQL categories. Use Type and Quiz on DDL/DML/DCL/TCL and the join types until you can recall every one without hesitation.
Drill your weakest domain. Pick a single domain deck and grind it until the Match time drops and the Quiz score climbs.
Then prove it. When the cards feel easy, confirm with a full practice test — aim comfortably above passing before exam day.

DataSys+ Flashcards FAQ

Hundreds of free DataSys+ flashcards, organized across all five DS0-001 domains — Database Fundamentals, Database Deployment, Database Management and Maintenance, Data and Database Security, and Business Continuity. They're free with no account required.

References

1.CompTIA. “DataSys+ (DS0-001) Certification Exam Objectives.” comptia.org. ↑
2.International Organization for Standardization. “ISO/IEC 9075 — Database language SQL.” iso.org. ↑
3.National Institute of Standards and Technology. “SP 800-53 — Security and Privacy Controls.” csrc.nist.gov. ↑

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