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FDST-BDS practice questions

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Question 1Data Governance and Stewardship

In a data governance operating model, what distinguishes a data owner from a data steward?

  1. The owner holds accountability for a data domain and approves policy, while the steward executes day-to-day quality and definition work. Correct answer.
  2. The owner is always an external auditor, while the steward is always a system administrator.
  3. The owner performs daily data cleansing, while the steward sets enterprise-wide retention policy.
  4. The owner maintains technical metadata repositories, while the steward approves access requests for the domain.

Explanation

Accountability and execution are separated in mature operating models: the owner is the accountable decision-maker for a domain, funding and approving standards, while stewards handle definitions, quality monitoring, and issue triage within that mandate. Confusing the two roles leads to decisions made without authority or accountability without operational follow-through.

Question 2Data Governance and Stewardship

Which artifact most directly establishes who may access a specific data domain and under what conditions?

  1. A data quality scorecard
  2. A data catalog glossary entry
  3. A conceptual data model
  4. An access control policy with defined roles and approval rules Correct answer.

Explanation

Access control policy states the rules, roles, and approval paths that determine who can reach a dataset and under what constraints. Glossaries, scorecards, and conceptual models describe meaning and quality but do not authorize or restrict access.

Question 3Data Governance and Stewardship

A retail bank wants a single authoritative list of approved terms with definitions used across reporting. Which governance asset should it establish?

  1. A disaster recovery runbook
  2. A capacity plan
  3. A data lineage diagram
  4. A business glossary Correct answer.

Explanation

A business glossary is the controlled vocabulary of approved business terms and their definitions, giving reporting a single agreed meaning. Lineage tracks movement, runbooks cover recovery, and capacity plans address infrastructure sizing.

Question 4Data Governance and Stewardship

What is the primary purpose of a data governance council or steering committee?

  1. To perform nightly batch loads
  2. To negotiate software license discounts
  3. To resolve cross-domain policy conflicts and prioritize governance initiatives Correct answer.
  4. To write application code for data pipelines

Explanation

A governance council provides decision-making authority across domains, arbitrating conflicts on standards, ownership, and priorities that individual teams cannot resolve alone. Engineering, operations, and procurement tasks belong to other functions.

Question 5Data Governance and Stewardship

Which statement best describes data stewardship as a function rather than a job title?

  1. It is a legal role reserved for privacy regulators.
  2. It is a software module that automatically fixes data errors.
  3. It is a set of responsibilities for defining, monitoring, and improving data within an assigned domain. Correct answer.
  4. It is a certification that must be renewed annually by a vendor.

Explanation

Stewardship is defined by the responsibilities attached to a domain, such as maintaining definitions, monitoring quality, and resolving issues, regardless of the individual's title. It is neither a vendor certification, an automated tool, nor a regulator's position.

Question 6Data Governance and Stewardship

A governance team needs to record the business meaning, permitted uses, and quality thresholds for a customer dataset. Which artifact is most appropriate?

  1. A vendor invoice
  2. A firewall rule set
  3. A network topology diagram
  4. A data domain charter or data policy document Correct answer.

Explanation

A domain charter or policy document captures purpose, permitted uses, ownership, and quality expectations for a dataset in a durable, auditable form. Firewall rules, topology diagrams, and invoices do not express governance intent.

Question 7Data Governance and Stewardship

Which metric best indicates whether data governance is actually changing behavior rather than just producing documentation?

  1. Percentage of new projects that passed a governance review gate before build Correct answer.
  2. Number of policy PDFs published
  3. Number of governance meetings held
  4. Number of glossary terms defined

Explanation

Outcome-oriented measures show governance embedded in how work is done, such as review gates applied before projects proceed. Counting terms, documents, or meetings measures activity volume, which can rise without any change in behavior.

Question 8Data Governance and Stewardship

What does a data lineage record primarily document in a governance context?

  1. The origin, transformation, and flow of data across systems Correct answer.
  2. The seniority level of each data steward
  3. The marketing campaign that generated a lead
  4. The physical storage cost of each table

Explanation

Lineage traces where data originates, how it is transformed, and where it moves, which supports impact analysis, audit, and root-cause investigation. Cost, staffing, and campaign attribution are separate concerns.

Question 9Data Governance and Stewardship

A regulator asks a firm to demonstrate that personal data is deleted when consent is withdrawn across all downstream copies. Which governance capability is most directly required?

  1. End-to-end lineage combined with retention and deletion controls Correct answer.
  2. A business glossary of marketing terms
  3. A conceptual entity-relationship model
  4. A data quality dashboard for address accuracy

Explanation

Proving deletion across downstream copies requires knowing where copies exist (lineage) and having enforceable retention and deletion controls applied to them. Glossaries, quality dashboards, and conceptual models do not track propagation or enforce deletion.

Question 10Data Governance and Stewardship

Under a federated governance model, which arrangement is typical?

  1. A central team makes every data decision for all business units
  2. No central standards exist and each team acts independently
  3. Business units own domain decisions within centrally defined standards Correct answer.
  4. An external regulator approves each data model change

Explanation

Federated models balance central standards with local execution: a central function sets principles and frameworks while domains apply them and own domain-level decisions. Centralized control and full autonomy are the two extremes federation deliberately avoids.

Question 11Data Governance and Stewardship

A steward discovers that the term 'active customer' is defined three different ways across finance, marketing, and risk. What is the most appropriate first governance action?

  1. Delete two of the three definitions without consultation
  2. Publish all three definitions side by side with no decision
  3. Convene the owning stakeholders to agree a single approved definition and record it in the glossary Correct answer.
  4. Escalate directly to the regulator

Explanation

Conflicting definitions are resolved through the accountable owners reaching agreement, after which the approved definition is recorded and versioned in the glossary. Unilateral deletion, undecided publication, or external escalation bypasses the governance process and leaves the conflict unresolved.

Question 12Data Governance and Stewardship

Which control most directly supports the principle of least privilege in a data platform?

  1. Granting broad read access to speed up onboarding
  2. Storing all data in a single unrestricted schema
  3. Relying on employees to self-report excessive access
  4. Role-based entitlements scoped to required datasets with periodic recertification Correct answer.

Explanation

Least privilege is enforced by scoping entitlements to what a role actually needs and reviewing them periodically so access does not accumulate. Broad grants, unrestricted schemas, and self-reporting all allow unnecessary access to persist.

Question 13Data Governance and Stewardship

What is the governance purpose of a data classification scheme such as public, internal, confidential, and restricted?

  1. To drive proportionate handling, access, and protection requirements based on sensitivity Correct answer.
  2. To rank employees by performance
  3. To determine which storage vendor to purchase
  4. To replace the need for access controls

Explanation

Classification assigns sensitivity levels so that handling rules, access restrictions, and protection measures can be applied proportionately. It informs controls rather than replacing them, and it has nothing to do with procurement or personnel ranking.

Question 14Data Governance and Stewardship

A data quality issue causes incorrect regulatory reports. Which governance response best reflects root-cause stewardship?

  1. Ignore the issue because the report was accepted
  2. Add a disclaimer to the report
  3. Trace the defect to its source system, assign an owner, and fix the upstream control Correct answer.
  4. Correct the reported figures manually each cycle

Explanation

Sustainable remediation traces defects upstream, assigns accountability, and strengthens the control that should have prevented the error. Manual corrections, disclaimers, and acceptance of a flawed report treat symptoms and allow recurrence.

Question 15Data Governance and Stewardship

Which pair correctly matches a governance role to its typical decision right?

  1. Data owner approves access to the domain; data steward maintains definitions and quality rules Correct answer.
  2. Governance council performs data entry; data steward sets corporate strategy
  3. Data steward approves enterprise-wide retention policy; data owner cleans records daily
  4. Data custodian sets business definitions; data owner operates backup jobs

Explanation

Decision rights follow accountability: owners authorize access and set direction for their domain, while stewards maintain definitions and quality rules. Custodians handle technical operations, and the council arbitrates policy rather than performing operational tasks.

Question 16Data Governance and Stewardship

An organization wants metadata about who changed a record, when, and why to be available for audit. Which governance requirement does this describe?

  1. Data virtualization
  2. Audit trail and change accountability Correct answer.
  3. Schema denormalization
  4. Data minimization

Explanation

Recording who changed data, when, and for what reason creates an audit trail that supports accountability and regulatory review. Minimization limits collection, virtualization abstracts access, and denormalization is a physical design choice.

Question 17Data Governance and Stewardship

A team proposes storing production customer data in a personal cloud account to speed analysis. Which governance principle is most directly violated?

  1. Approved environment and sanctioned access channels Correct answer.
  2. Metadata versioning
  3. Data reuse
  4. Master data consolidation

Explanation

Governance requires data to remain within approved environments and access channels so that controls, monitoring, and lineage apply. Moving data to an unsanctioned personal account bypasses those controls regardless of the analytical benefit.

Question 18Data Governance and Stewardship

Which artifact should define the escalation path when a data quality incident crosses two business domains?

  1. The glossary term list
  2. The data model diagram
  3. The governance operating model with defined escalation and dispute resolution steps Correct answer.
  4. The vendor contract

Explanation

Cross-domain incidents require a documented escalation and dispute-resolution path, which is part of the governance operating model. Glossaries, models, and contracts do not assign escalation authority between domains.

Question 19Data Governance and Stewardship

A firm defines a policy that all critical data elements must have a named owner and documented quality threshold. Which governance lever is being used?

  1. Organizational restructuring
  2. Technology substitution
  3. Policy and standards setting Correct answer.
  4. Incentive design

Explanation

Mandating owners and thresholds for critical data elements is a standards-setting lever that establishes required practice. Incentives, tooling changes, and restructuring are different levers that may support but do not define the requirement.

Question 20Data Governance and Stewardship

Why should critical data elements be identified before designing data quality controls?

  1. Because critical elements never change over time
  2. Because effort and controls should be proportionate to business and regulatory impact Correct answer.
  3. Because controls are cheaper when applied to all fields equally
  4. Because regulators require controls on every field

Explanation

Identifying critical data elements focuses investment on the fields whose failure would most affect reporting, customers, or compliance, so controls are proportionate to impact. Uniform control across all fields spreads effort thinly, and criticality is reassessed as the business changes.

Question 21Data Governance and Stewardship

A global firm must reconcile conflicting local retention rules with a group-wide minimum standard. Which approach best reflects sound governance judgment?

  1. Let each region ignore the group standard entirely
  2. Apply the longest retention to every dataset regardless of purpose
  3. Apply the shortest local retention everywhere to minimize storage
  4. Adopt the strictest applicable requirement where rules conflict and document the rationale Correct answer.

Explanation

When legal or regulatory retention rules conflict, applying the strictest applicable requirement satisfies all obligations, and documenting the rationale preserves an auditable decision record. Uniform shortest or longest retention ignores purpose and jurisdictional differences.

Question 22Data Governance and Stewardship

A steward wants to measure whether a newly enforced definition of 'delinquent account' is actually used in reporting. Which evidence is most probative?

  1. The seniority of the steward who authored it
  2. The number of meetings where the definition was mentioned
  3. The glossary page view count
  4. Reconciliation showing reported figures now derive from the approved definition across reports Correct answer.

Explanation

Adoption is demonstrated when reported outputs demonstrably derive from the approved definition, which reconciliation can show. Page views, meeting mentions, and author seniority are indirect signals that do not prove the definition governs actual reporting logic.

Question 23Data Governance and Stewardship

During a merger, two entities use incompatible customer identifiers and both claim ownership of the customer domain. What is the most defensible governance sequence?

  1. Assign ownership to the vendor supplying both systems
  2. Immediately merge all databases and resolve ownership later
  3. Let each entity keep its own identifier indefinitely with no crosswalk
  4. Establish interim domain accountability, define a master data standard, then remediate and reconcile Correct answer.

Explanation

Sound sequencing secures accountability first so decisions have an owner, then defines the target standard, then remediates and reconciles data against it. Merging first or leaving identifiers unlinked creates compounding integrity and accountability problems.

Question 24Data Governance and Stewardship

A governance council must decide whether to fund remediation of a known data defect affecting a regulatory report. Which reasoning best reflects risk-based prioritization?

  1. Prioritize based on likelihood and impact of misstatement, including regulatory and customer harm Correct answer.
  2. Prioritize the defect that is cheapest to fix regardless of impact
  3. Defer until the next annual audit cycle
  4. Defer because the report was previously accepted without challenge

Explanation

Risk-based prioritization weighs the probability and severity of harm, including regulatory exposure and customer impact, rather than past acceptance or mere cost. Cheapest-first or deferral logic ignores the magnitude of potential harm.

Question 25Data Governance and Stewardship

An organization wants stewards to challenge business requests that conflict with data policy without fearing retaliation. Which governance element is most essential?

  1. Additional reporting dashboards
  2. Explicit escalation protection and executive sponsorship for stewards Correct answer.
  3. A larger metadata repository
  4. More frequent glossary updates

Explanation

Stewards can only enforce policy if escalation is protected and visibly backed by executive sponsors, which gives their challenge legitimacy. Tooling and documentation improvements do not create the authority or safety needed to say no.

Question 26Data Governance and Stewardship

Which statement best describes the distinction between data governance and day-to-day data management?

  1. Governance applies only to personal data; management applies to all other data.
  2. Governance sets decision rights and policy for data; management executes those decisions operationally. Correct answer.
  3. Governance is a one-time project; management is a recurring audit activity.
  4. Governance is performed only by IT operations staff; management is performed by business analysts.

Explanation

Data governance defines who has authority to make decisions about data and what rules apply, while data management is the operational execution of those rules. Governance is therefore a decision-and-accountability layer, not a synonym for technical administration.

Question 27Data Governance and Stewardship

A data owner is accountable for a domain dataset. Which responsibility is most characteristic of that role rather than of a data steward?

  1. Running the nightly reconciliation job that compares source and target counts.
  2. Approving access requests and accepting residual risk for the dataset on behalf of the business. Correct answer.
  3. Documenting field-level definitions and resolving day-to-day data quality exceptions.
  4. Configuring ETL pipelines that load the dataset into the warehouse.

Explanation

The data owner holds accountability and decision rights, including approving access and accepting risk for the domain. Stewards support the owner with definitions, quality monitoring, and issue triage, while engineers build and operate pipelines.

Question 28Data Governance and Stewardship

Which capability of a business glossary most directly reduces ambiguity when two reports show different totals for the same measure?

  1. Recording the software version of the reporting tool used to build each dashboard.
  2. Linking each approved term to a single certified definition and its associated critical data elements. Correct answer.
  3. Archiving screenshots of legacy reports for historical reference.
  4. Storing the contact email of every employee who has ever queried the term.

Explanation

A glossary resolves ambiguity when it binds an approved business term to one certified definition and the critical data elements that implement it, so reports can be traced to the same meaning. Contact lists, tool versions, and screenshots do not establish semantic agreement.

Question 29Data Governance and Stewardship

A firm wants a data-sharing agreement with a partner to be enforceable. Which element is most essential for the agreement to function as a governance control?

  1. A general statement that both parties will comply with applicable law.
  2. An appendix describing the partner's marketing strategy.
  3. Specified permitted purposes, retention limits, and consequences for breach, tied to defined data categories. Correct answer.
  4. A list of the partner's office locations and headcount.

Explanation

An enforceable sharing control must state what data categories are shared, for which purposes, for how long, and what happens on breach, so obligations can be tested. A generic compliance clause gives no measurable obligations to monitor.

Question 30Data Governance and Stewardship

A governance council is defining policy for a new analytics platform. Which approach best embeds privacy-by-design into the platform's governance rather than bolting it on afterward?

  1. Reviewing privacy only after the first production incident involving personal data.
  2. Requiring a data protection impact assessment and documented lawful basis before any dataset is onboarded. Correct answer.
  3. Delegating all privacy decisions to the vendor that supplies the platform.
  4. Restricting privacy review to the legal team without involving platform engineers.

Explanation

Privacy-by-design means privacy requirements are assessed and documented before data is onboarded, so lawful basis and minimization shape the design. Retrospective review or full delegation removes the control from the accountable organization.

Question 31Data Governance and Stewardship

An organization wants to demonstrate that its data governance program has executive backing and can resolve cross-domain disputes. Which structural element is most appropriate?

  1. A monthly newsletter summarizing governance achievements.
  2. A wiki page listing every dataset in the estate.
  3. A shared inbox where any employee can raise data questions without a decision forum.
  4. A steering committee of senior business leaders that meets on a defined cadence with authority to arbitrate domain conflicts. Correct answer.

Explanation

Escalation and arbitration require a decision-making body with seniority and a regular cadence, otherwise disputes stall. Communication channels and inventories support the program but cannot resolve conflicting priorities.

Question 32Data Governance and Stewardship

A steward must decide whether a proposed new attribute should be classified as confidential. Which input is most relevant to that classification decision?

  1. The programming language used to generate the attribute.
  2. The number of columns currently in the source table.
  3. The storage cost per gigabyte of retaining the attribute.
  4. The sensitivity of the information the attribute reveals and the harm from unauthorized disclosure. Correct answer.

Explanation

Classification is driven by the sensitivity of the information and the potential harm from disclosure, which determines the controls required. Cost, table size, and implementation language are technical or financial factors that do not measure sensitivity.

Question 33Data Governance and Stewardship

A policy requires that changes to critical data element definitions pass review before taking effect. Which mechanism best enforces this requirement consistently?

  1. A workflow in the metadata repository that blocks publication of a changed definition until the designated approver signs off. Correct answer.
  2. A quarterly presentation reminding staff that definitions should be stable.
  3. An email asking analysts to notify the governance team when they change a definition.
  4. A spreadsheet where stewards may optionally log definition changes.

Explanation

Consistent enforcement requires a control embedded in the tooling that prevents an unapproved change from becoming effective. Voluntary notification, reminders, and optional logs rely on discretion and will be bypassed under time pressure.

Question 34Data Governance and Stewardship

A firm operates in several jurisdictions with different rules on cross-border transfer of personal data. Which governance design best allows the firm to satisfy all of them simultaneously?

  1. Prohibit all cross-border transfer of personal data across the firm.
  2. Let each local team set its own rules with no group-level baseline.
  3. Establish a baseline standard meeting the strictest applicable requirement, then document justified local variations where law permits. Correct answer.
  4. Apply the least restrictive local rule everywhere to minimize operational effort.

Explanation

A strictest-common baseline with documented, lawful local variations satisfies each jurisdiction while avoiding fragmented practice. Applying the weakest rule or letting local teams act independently creates breaches in stricter jurisdictions.

Question 35Data Governance and Stewardship

Two business units propose different retention periods for the same customer record class, and both cite legitimate operational needs. Which governance action best resolves the conflict while preserving defensibility?

  1. Adopt the shorter period by default to reduce storage cost.
  2. Adopt the longer period by default without analysis to avoid deleting anything.
  3. Allow each unit to apply its own period indefinitely without escalation.
  4. Have the accountable owner decide against documented legal, operational, and risk criteria, and record the rationale and any exception. Correct answer.

Explanation

Conflict resolution requires a documented decision by the accountable owner weighed against legal, operational, and risk criteria, with the rationale retained for audit. Defaulting to either extreme or leaving the conflict unresolved produces inconsistent retention and weak defensibility.

Question 36Data Governance and Stewardship

A regulator asks a firm to prove that a specific critical data element has been governed consistently for the past two years. Which combination of evidence most directly supports that assertion?

  1. A current org chart and the platform's architecture diagram.
  2. The vendor contract for the reporting tool and its renewal date.
  3. A list of upcoming governance training sessions.
  4. Versioned definitions with approval dates, owner assignments, and linked quality results over the period. Correct answer.

Explanation

Consistency over time is demonstrated by time-stamped evidence showing who approved which definition and how quality performed across the period. Current-state artifacts such as org charts or contracts cannot show historical governance continuity.

Question 37Data Architecture and Modeling

In a star schema, which characteristic distinguishes a fact table from a dimension table?

  1. The fact table holds descriptive text attributes that filter the connected dimension tables.
  2. The fact table is always normalized to third normal form while dimensions remain denormalized.
  3. The fact table stores numeric measures and foreign keys to dimensions, while dimension tables hold descriptive attributes. Correct answer.
  4. The fact table enforces referential integrity while dimension tables store aggregated measures.

Explanation

A fact table records business events as numeric measures plus foreign keys pointing to dimension tables. Dimension tables provide the descriptive context used for filtering and grouping those measures. This division is the defining structural property of a star schema.

Question 38Data Architecture and Modeling

Which schema pattern places conformed dimension tables around multiple fact tables?

  1. Flat single-table schema
  2. Snowflake schema
  3. Third normal form enterprise model
  4. Galaxy schema Correct answer.

Explanation

A galaxy schema, also called a fact constellation, shares conformed dimensions across several fact tables. This enables cross-process analysis using consistent dimension keys. A single fact table with dimensions describes only a star.

Question 39Data Architecture and Modeling

What is the primary purpose of a surrogate key in a dimension table?

  1. To compress dimension rows by removing redundant columns
  2. To replace the natural business key with a stable, warehouse-generated identifier Correct answer.
  3. To encrypt sensitive attributes stored in the dimension
  4. To enforce uniqueness across every fact table in the warehouse

Explanation

Surrogate keys are system-generated integers that insulate the warehouse from changes in source business keys. They also support tracking multiple versions of a dimension member. Natural keys remain as ordinary attributes for lineage.

Question 40Data Architecture and Modeling

A dimension row keeps the prior attribute values plus effective and expiration dates when a customer changes address. Which technique is this?

  1. Type 0 slowly changing dimension
  2. Type 2 slowly changing dimension Correct answer.
  3. Type 3 slowly changing dimension
  4. Type 1 slowly changing dimension

Explanation

Type 2 preserves history by inserting a new row with validity dates and often a current-record flag. The old row is expired rather than overwritten. This allows fact rows to join to the attribute values in effect at event time.

Question 41Data Architecture and Modeling

Which modeling approach stores data as normalized entities with relationships resolved at query time rather than pre-joined structures?

  1. Star schema
  2. Dimensional model
  3. Data vault model Correct answer.
  4. Wide denormalized table

Explanation

A data vault organizes data into hubs, links, and satellites using third normal form principles for auditability and load flexibility. Relationships are captured as link structures rather than pre-joined dimension tables. Query-time joins reconstruct the business view.

Question 42Data Architecture and Modeling

In dimensional modeling, what does grain of a fact table describe?

  1. The physical partitioning strategy applied to the fact table
  2. The number of dimension tables joined to the fact table
  3. The cardinality of the surrogate key sequence
  4. The level of detail represented by a single fact row Correct answer.

Explanation

Grain states exactly what one fact row represents, such as one order line or one daily account balance. Declaring grain first prevents mixing incompatible levels of detail in one table. All measures must be consistent with that declared grain.

Question 43Data Architecture and Modeling

Which key type is used in a fact table to reference a dimension table?

  1. Composite primary key of all dimension attributes
  2. Hashed payload key
  3. Foreign key to the dimension surrogate key Correct answer.
  4. Natural business key

Explanation

Fact tables reference dimensions through foreign keys that point to dimension surrogate keys. This keeps joins fast and stable when source business keys change. Natural keys are retained inside the dimension for traceability.

Question 44Data Architecture and Modeling

What does a conformed dimension provide across separate data marts?

  1. A normalized entity model replacing all star schemas
  2. A duplicate copy of each fact table in every mart
  3. A guarantee that all marts use the same database engine
  4. A shared set of dimension attributes and keys with identical meaning in each mart Correct answer.

Explanation

Conformed dimensions use the same keys and attribute definitions wherever they appear, enabling drill-across between fact tables. They are the backbone of consistent cross-process reporting. Without conformance, metrics from different marts cannot be reliably combined.

Question 45Data Architecture and Modeling

A retail warehouse has an order-line fact table and needs to report revenue by the product category that was valid when each line was sold. Product attributes change over time. Which design satisfies this requirement?

  1. Type 2 product dimension with validity dates joined on order date Correct answer.
  2. Type 1 product dimension overwritten in place
  3. A junk dimension combining product and order flags
  4. Type 3 product dimension with one prior-category column

Explanation

Type 2 dimensions create a new surrogate key per version, so each fact row can join to the version in effect at event time. Validity dates let the join be constrained to the correct period. This preserves historically accurate category reporting.

Question 46Data Architecture and Modeling

A team loads daily snapshots where each row is a full copy of a customer record and only the latest version is needed for reporting. Which load pattern fits best?

  1. Type 1 overwrite of the existing dimension row Correct answer.
  2. Type 2 history tracking with effective dates
  3. Accumulating snapshot fact table
  4. Bridge table resolving a many-to-many hierarchy

Explanation

When only current state is required, a Type 1 overwrite keeps one row per member and avoids unnecessary history rows. Type 2 would add complexity and storage with no analytical benefit here. The requirement for only the latest version is the deciding factor.

Question 47Data Architecture and Modeling

A fact table contains both order-line amounts and monthly sales-quota amounts for the same salesperson. Why is this design problematic?

  1. The two measures have different grains, so aggregating them together produces incorrect totals Correct answer.
  2. The fact table lacks a surrogate key for the salesperson dimension
  3. Quota amounts are always non-additive and must be stored as text
  4. Order-line amounts should be stored in a dimension rather than a fact

Explanation

Mixing grains in one fact table causes double counting because each row no longer represents a single consistent event. Order lines and monthly quotas must live in separate fact tables at their own declared grains. They can then be compared through conformed dimensions.

Question 48Data Architecture and Modeling

Which design best supports a product hierarchy where each product belongs to exactly one category and one department?

  1. A factless fact table recording category assignments
  2. A bridge table between product and category
  3. A degenerate dimension on the fact table
  4. A snowflaked dimension with separate category and department tables Correct answer.

Explanation

A fixed one-to-many hierarchy can be normalized into separate dimension tables, producing a snowflake. Each level references its parent, avoiding redundant storage of category and department attributes. The hierarchy is strict, so no bridge table is needed.

Question 49Data Architecture and Modeling

A fact table has no numeric measures and records which products were promoted in which stores. What is this table called?

  1. Junk dimension
  2. Factless fact table Correct answer.
  3. Accumulating snapshot fact table
  4. Periodic snapshot fact table

Explanation

A factless fact table captures the occurrence of a relationship or event using only dimension keys. It answers questions about coverage, such as which products were on promotion. Counting its rows yields meaningful metrics without stored measures.

Question 50Data Architecture and Modeling

Which structure resolves a many-to-many relationship between two dimensions, such as patients and diagnoses, while keeping weighting factors available?

  1. Degenerate dimension
  2. Outrigger dimension
  3. Bridge table with weighting factor Correct answer.
  4. Role-playing dimension

Explanation

A bridge table holds one row per dimension-pair combination and can carry a weighting factor for allocations. It lets queries count or sum correctly across the many-to-many relationship. Without weighting, multi-valued attributes cause double counting.