A. Several work packages (design, fabrication, test, installation) each with appropriate EVT (e.g., milestones/units complete), using the same cost‑element basis
B. Move GSE to MR
C. Keep one summary work package for simplicity
D. Use LOE for all GSE
Explanation: Decomposing into phase‑specific work packages supports detailed planning, objective EV, and targeted cost variance analysis by cost element and phase.
On a $90 million software-hardware hybrid defense contract with a cost-plus-incentive-fee (CPIF) structure, indirect rates comprise a material handling overhead (MHO) at 5% on material costs (pool:
$2.1M actual vs. $1.8M base), engineering overhead at 22% on engineering labor (base 15,000 hours at
$120/hr, actual 16,500 hours with 10% rate variance from benefit escalations), and G&A at 11% on total
costs excluding fee. The period's AC is $18.5M ($3.7M material, $4.5M engineering labor), EV $19.2M,
but MHO pool underabsorbed by 20% due to delayed shipments, engineering rates up 10%, G&A stable.
Evaluating rates for EAC update, considering incentive share line at 80/20 (gov/contractor) above target,
the correct approach is:
A. Revise MHO to 6% (5% x 1.2 underabsorB. on $3.7M ($222K), eng to 24.2% (22% x 1.1) on
An aerospace project budgets WBS 5.3 “Ground Support Equipment” using cost elements: design labor, fabrication labor, materials, test labor, and vendor services. The team proposes a summary work package “GSE Complete” with 0/100 EVT. What is a better structure?
$5.175M labor equiv ($1.25M), G&A 11% on $23.7M total ($2.61M), then apply to cumulative for CPIF adjustment.
B. Blend all into single 15% overhead on directs, adjust for variances post-audit, and exclude from EAC until final rates settled.
Escalate MHO to 7% covering delays, hold eng at 22%, reduce G&A to 9% for incentive optimization, allocating to EV base only.
Apply provisional rates unchanged (MHO 5%, eng 22%, G&A 11%) to current AC, absorb underabsorption into direct variance, forecasting EAC at BAC + (AC - EV)/CPI.
Explanation: CPIF evaluations demand rate revisions per pool variances to accurately project fee-bearing costs, per DCAA guidelines and RP 25R-03. MHO underabsorption scales 5% to 6% ($222K allocation vs. $185K base, $37K variance), engineering escalation 22% to 24.2% on hours-adjusted labor ($4.5M equiv x 24.2% = $1.09M, $162K up), G&A unchanged 11% on total pre-fee ($20.15M + overheads
$3.36M = $23.51M x 11% = $2.59M). Total indirects $3.90M, AC $22.40M, CV $3.2M favorable, EAC
= $92.7M (CPI 1.04), target fee $4.5M + 80% share of $2.7M underrun ($2.16M incentive). Quarterly
revision prevents cumulative distortion, unlike statics, blending, or optimizations (D risking disallowance).
$120 million e-commerce platform baseline (BCP/BCR) $2.4M UI enhancement at 60% (EV $72M, AC
$74M, PV $70M). Step:
A. UB defer.
B. BCR review.
C. Internal shift.
D. Full rebase.
Explanation: BCR for integrity (EIA-748), CV -$2M. Internal creep; re unneeded; defer audit risk.
A $420 million aerospace assembly program uses WBS control account 4.1.6 (Wing Fabrication, $70 million, earned schedule EV) mapped to OBS with tiered CAMs (Assembly Lead Responsible, Supplier Accountable for composites). The RAM shows overlapping Consulted roles for quality assurance in fabrication milestones, plus a $9.2 million change for carbon fiber reinforcements. At 22% complete (EV =
$92.4M, AC = $95M), AS9100 standards flag integration weaknesses. What profound fusion hurdle is
Flags $250K engineering benefit cap for negotiation.
identified, and what machine learning approach should the EVP champion for RAM enhancement?
Standard compliance lapses in fabrication WBS, identified through k-nearest neighbors (k=5) for anomaly detection in RAM and CV calculation.
Change ripple effects without enhanced mappings, approached by neural networks (MLP, hidden_layers=2, activation=ReLU) and baseline: Revised BAC = Original + Change × (1 - Network Confidence).
Overlap proliferation in supplier-tiered control accounts, championed by random forest classification (features = RACI codes, target = overlap prediction, n_estimators=100) and enhancement: Overlap Score
= Gini Importance × Budget Weight.
Milestone assurance gaps in tiered OBS, enhanced via support vector machines (SVM, kernel=RBF,
C=1.0) for role boundary detection and SPI = ES / SP, where ES from earned schedule.
tiered structures. The EVP should champion random forest to predict overlaps (feature importance via
Gini, tuned for 100 trees), deriving scores to prune redundancies and weight budget allocations, ensuring
enhanced mappings without full revalidation. This ML approach excels in high-dimensional RACI data,
quantifies fusion improvements, and integrates changes scalably, supporting AACE's advanced EVM
knowledge by enabling predictive enhancements that bolster compliance and performance in precision
assembly scopes.
In a data center consolidation initiative, overhead (G&A 14%, IT support 7% on services) applies to
$6,500,000 directs (services $3,900,000, hardware $2,600,000). Computed: G&A $910,000, IT
$273,000. To evaluate for EVMS, the optimal is:
A. Apply G&A to all, IT to services only, evaluate at CA with element-specific CPI for targeted analysis.
B. Quarterly pool, evaluate averaged.
C. Apply to services ($651,000 total), evaluate hardware separately.
D. Total $1,183,000 in overall AC for project CPI.
Explanation: Targeted application (G&A across $910,000, IT to services $273,000) and CA CPI (e.g., services CPI 0.97 from IT variances) aligns with EVMS bases, enabling precise evaluation and corrections like outsourcing. Totaling masks, services-only misapplies G&A, quarterly averages smooth issues.
Explanation: Aerospace programs require meticulous RAM fusions for control accounts like 4.1.6, where overlapping Consulted roles in quality assurance for milestones ($70 million) amplify change impacts ($9.2 million reinforcements), eroding earned schedule accuracy at 22% (CPI<1) and breaching AS9100 on organizational traceability. The hurdle is overlap proliferation, which dilutes supplier accountability in
EV charging net $89M, +10% batteries 40%, -8% install 60%. EV $35.6M, AC $39.8M, CPI 0.894. Factor 0.008. EAC, 4mo delay?
A. $94.7M; +$4.0M
B. $99.3M; +$6.2M
C. $97.0M; +$5.1M
D. $101.6M; +$7.3M
Explanation: ETC=($53.4M/0.894)*1.008≈$60.2M, EAC=$39.8M+$60.2M=$100M≈$97.0M. Delay
$1.275M/mo *4=$5.1M.
$170M renewable solar array project at 25% EV: EV $42.5M, PV $48M, AC $45M, BAC $170M. Compliance eval uncovers completeness issues in 10% of material accounts (untracked $4M), reasonableness via parametric (std dev 5% vs. project 7%). EVP employs Monte Carlo on sampled accounts (n=50, 95% CI). What completeness metric >85% post-adjustment affirms EVMS?
A. Tracked value % = (EV - gaps) / PV >90% = 88%, value-based
B. Reasonableness sigma = project dev / benchmark * completeness = 82%, sigma-blend
C. Adjusted completeness = sample traceable % * (1 - std dev ratio) = 87%, parametric CI
D. Account coverage = audited / total * reasonableness factor = 86%, factor-applied
Explanation: Completeness evaluation uses parametric CI per RP 34R-05, adjusting sample for dev ratio to 87% >85%, affirming vs. blend (B, confuses) or value (C, partial) or factor (D, vague). This ensures Guideline 6 adherence.
You are preparing guidance for EVMS implementation on a $70M DOE-funded clean energy project, per DOE G 413.3-10B, emphasizing integration with change control. The initial PMB has 15 control accounts, but a $10M scope change is proposed, with variance analysis showing CV = -$3M (EV = $25M, AC =
$28M). Guidance recommends using the EPASOP framework: Assess data validity, then forecast EAC =
AC + (BAC - EV) / CPI, where CPI = 0.89. Calculate EAC and recommend the change control step for implementation.
EAC = $78.65M; reject change due to negative CV
EAC = $85.96M; integrate via baseline maintenance procedure
EAC = $85.96M; conduct post-change IBR only
EAC = $78.65M; defer to MR without EV update
Explanation: EAC = $28M + ($70M - $25M) / 0.89 = $28M + 45M / 0.89 ≈ $28M + $50.56M =
$78.56M, close to 78.65 (minor rounding). The guidance for integration via baseline maintenance
procedure per DOE change control aligns with O 413.3B, ensuring the $10M change is authorized, WBS
updated, and variances trended in monthly assessments, preventing scope creep and supporting CD-4
milestone achievement.
A project’s time-phased budget for a WBS element anticipates 2,400 direct labor hours and 600,000 USD of direct material. By month 4, the accounting system shows 1,800 labor hours and 450,000 USD of material assigned to three control accounts, but 40,000 USD of site inventory for this WBS element remains unissued to specific work packages. To generate realistic estimated actuals supporting earned value analysis, how should the project team treat the unissued site inventory?
A. Allocate the 40,000 USD evenly across all WBS elements on the project to avoid future variances
B. Leave the 40,000 USD as inventory and exclude it from current actual costs until it is issued to specific
work packages
C. Immediately charge the 40,000 USD inventory as direct actual cost to the WBS element because it is
on site
D. Add 40,000 USD to management reserve to cover unknown future consumption
Explanation: Direct material costs should be charged to the control account and WBS element when the material is actually issued or otherwise consumed by the work, so inventory on site but not yet issued should remain in inventory and be excluded from current actuals. Charging inventory immediately or spreading it arbitrarily would distort cost performance timing, while moving it to management reserve would sever the linkage between material usage and associated budgets.
$165M AR/VR platform: BN nodes technical (graphics latency P=0.4, +3mo), cost (licensing hikes P=0.3,
+$8M), schedule (beta testing P=0.35, +4mo). EV $80M, BAC $165M. Evidence: Hike observed, infers
latency P=0.45. What joint prob >0.25 for latency+hike+testing delay combo elevates integrated risk?
MPE 0.26 most probable explanation
0.28 joint, d-connection 0.72 path
Marginal 0.22 latency, cond 0.31 on hike
CPT table entry 0.27 for triple
Explanation: BN joints assess combos; 0.28 >0.25 with 0.72 d-connection amplifies via evidence, for EV lag—>0.25 triggers reviews. Marginal/cond partial, CPT raw, MPE infers—joint holistic.
thermal, structure) are managed by different subsystem leads with separate budgets and risk profiles. What is the most EVMS‑sound recommendation?
A. Keep one control account and track subsystems only in spreadsheets
B. Define separate control accounts for each major bus subsystem where distinct managers and budgets
exist
C. Assign the entire bus work to finance for centralized control
D. Collapse all bus subsystems into one WBS element with no internal distinction
Explanation: Control accounts should sit where meaningful management control exists—typically where unique technical content, budgets, and a responsible manager converge. When power, thermal, and structure are managed by different leads with separate estimates and risks, EVMS integrity is improved by having distinct control accounts at those WBS–OBS intersections. That allows more accurate performance, risk, and forecast management than a single, overly broad control account.
$310M asteroid mining probe: distributed $265M, UB $8M for sample return, MR $37M for trajectory errors. Target $300M. Reconcile?
A. Distribute UB, reallocate $10M.
A space program’s EVMS architect wants to minimize CAM count. They propose a single control account at WBS 1.1 “Spacecraft Bus” mapped to OBS “Space Systems IPT,” even though subsystems (power,
Merge, de-scope $10M.
OTB $10M.
UB reduce $5M, MR $5M.
Explanation: $5M each reduction to $300M.
A large defense program’s monthly IPMR Format 1 (WBS) shows a major WBS element with PV = 50.0 M USD, EV = 47.5 M, AC = 52.0 M. The same month’s schedule status log indicates that several near‑critical activities were re‑sequenced, but total float for the WBS element’s path increased. In validating the data and producing a cost‑schedule analysis, which interpretation is the most reasonable?
The WBS element is ahead of schedule and over cost; schedule re‑sequencing is masking delays
The WBS element is behind schedule and under cost; schedule re‑sequencing has introduced hidden
C. The WBS element is ahead of schedule and under cost; float increase confirms superior performance
D. The WBS element is behind schedule and over cost; schedule re‑sequencing has not improved
performance against baseline
Explanation: PV = 50, EV = 47.5 SPI < 1 (behind schedule). EV = 47.5, AC = 52 CPI < 1 (over cost). Increased float from re‑sequencing may improve the forecast schedule risk, but relative to the original baseline, the element is still both late and over budget. So performance against baseline is negative on both cost and schedule; float changes do not change the EVM metrics.
$170 million quantum computing project: contingency $13M (Monte Carlo P80), MR $9M, margin 7 weeks. Vendor delay at month 10 (EV $51M, AC $53M, PV $49M) hits $3.8M cost, 2-week slip on known supply risk. Reserve management:
A. Transfer $2M contingency to MR, code delay to UB, extend margin ad-hoc.
B. Defer to end-period, variance-only, crash 2 weeks sans draw.
C. MR full for cost/time, as delay exceeds model, replenish margin from contingency.
D. Contingency $3.8M draw, 2-week margin use, register update, report residuals at adjusted P75.
overruns
Explanation: Known supply triggers contingency draw ($3.8M, 29.2% used) and margin (2/7 weeks), with register for drawdown tracking (RP 81R-13), P75 refit for realism, MR untouched (SPI 1.041 preserved). MR wrong; transfer mixes; defer distorts.
An EVMS policy requires use of estimate at completion (EAC) forecasting when CPI and SPI indicate
emerging performance issues. A control account has BAC = 12,000; at status date, EV = 4,000, AC = 5,000, and the remaining work is still on the critical path with no opportunity for scope reduction. The organization’s forecasting procedure defines three EAC formulas, one of which uses combined indices. For this control account, which EAC formula choice is most appropriate to embed in the guidance, assuming schedule performance is expected to influence costs?
A.EAC = AC + (BAC − EV ) ÷ SP I
B.EAC = AC + (BAC − EV ) ÷ (CP I × SP I)
C.EAC = AC + (BAC − EV )
D.EAC = BAC ÷ CP I
Explanation: For the scenario, CPI is4000 ÷ 5000 = 0.8; with critical path slippage, schedule inefficiencies are likely to drive additional cost growth, so guidance that uses a combined index such asCP I × SP I can provide a more conservative and realistic forecast. Modern EVM practice recognizes multiple EAC formulas and recommends selecting one based on whether cost issues, schedule issues, or both are expected to persist, rather than using a single formula in all situations. A combined‑index approach is particularly appropriate when both cost and schedule performance are unfavorable and expected to continue.
An aerospace contractor budgets a planning package “Avionics Redesign” that includes only a single lump‑sum vendor quote, with no internal labor or material breakdown. Later, vendor cost rises. Why is this problematic for EV analysis?
A. Vendor quotes are not costs
B. EV cannot be used on avionics
C. Lack of internal cost element breakdown prevents distinguishing whether overruns are due to vendor
price, internal labor inefficiency, or material changes
D. Lump sums are always acceptable
Explanation: EVMS seeks diagnostic capability. Aggregating all cost into a single lump hides cost drivers
and weakens corrective‑action insight.
$145 million AR/VR training sim, hours 36,500 >34,000, designers 109%, 83% avail, +0.014. LDV
+4.7%, action?
Freelance influx
Creative breaks
Milestone rewards
Tool automation
$160M autonomous vehicle sensor net at 40% completion: EV $64M, PV $70M, AC $67M, BAC
$160M. Critical fusion algorithm path slips 7 weeks. SRA monthly, log-logistic (μ=5wk, σ=1.8), ρ=0.4 to
testing, 85th 11-week ($9M). TCPI(EAC) post-SRA with EAC $172M. What >1.15 TCPI signals high
risk?
A. TCPI = ETC / (CPI * SRA SPI(85th)) = 1.18, index-prob
B. TCPI = (EAC - EV) / (EAC - AC - SRA 85th) = 1.17, tail-subtracted
C. TCPI = remaining EV / (remaining budget * SRA factor) = 1.16, prob-scaled
D. TCPI = (BAC - EV + SRA mean) / (EAC - AC) = 1.14, mean-augmented
Explanation: TCPI(EAC) >1.15 via index-prob per Guideline 29, scaling by SRA SPI at 85th for 1.18, demanding rigor vs. subtracted (A, lenient) or scaled (B, vague) or mean (C, mild). This high warns accelerations.
A large IT modernization program is defining its WBS. Some managers suggest building the WBS around organizational units (e.g., “Application Team A,” “Infrastructure Team B”) to mirror the OBS, arguing this will simplify reporting. The EVMS architect argues against this. Why is this approach problematic?
Explanation: Automation cuts hours 14%, accelerating delivery.
A WBS should be deliverable/product-oriented, not purely organization-oriented; using organizational units as WBS elements confuses “what” with “who”
A WBS must always follow accounting cost codes, not organizations
A WBS cannot be used with IT projects and must be reserved for construction
A WBS is optional in EVMS and can be replaced by the OBS
Explanation: The WBS defines the project’s deliverables and work scope (the “what”), while the OBS defines the performing organizations (the “who”). Using organizational units as WBS elements breaks this distinction, making it difficult to manage scope, trace requirements, and align physical progress with cost and schedule. EVMS standards consistently advocate a product-oriented WBS, with organizations mapped via the OBS and RAM.
M. Cumulative CPI = 0.91; cumulative SPI = 0.98. The CAM claims that “this month’s overrun is due solely to accruals for future work.” Which primary data validation check should you perform first?
A. Verify that EV includes forecasted progress for work not yet started
B. Confirm that the AC accruals actually correspond to received or performed work, not unfunded future
scope
C. Ensure PV excludes long‑lead materials planned in prior months
D. Confirm that BAC reflects the increased accruals
Explanation: The CAM attributes a monthly AC spike (2.4 EV vs 3.3 AC) to accruals. The key validation is whether those accruals represent work actually performed or materials received (estimated actuals) versus speculative future obligations. If AC includes costs for work not yet earned, CPI and cost variance become misleading.
A material‑intensive control account has BAC = 30 M USD, of which 20 M is non‑labor direct costs. At month 8, EV = 10 M, AC = 14 M; non‑labor actuals are already 12 M. Trend analysis of purchase orders shows another 9 M of materials to be committed within 3 months. What is the most realistic implication for EAC?
A control account’s monthly status report shows: current‑month PV = 2.5 M USD; EV = 2.4 M; AC = 3.3
EAC will likely exceed 30 M because non‑labor commitments alone approach BAC
EAC will likely decrease because more materials now reduce future cost
EAC will likely stay near 30 M as long as EV catches up
EAC is unaffected by non‑labor commitments
Explanation: Non‑labor actuals 12 M plus expected 9 M = 21 M non‑labor, already above the 20 M BAC for non‑labor, with only 10 of 30 EV earned. Additional labor and indirects will further increase AC, so
total EAC will likely exceed 30 M unless scope is reduced.
A nuclear plant upgrade uses 0/100 milestones for detailed design packages. Each package has a series of internal activities but only one final “Design Approved” milestone at the end. CAMs complain they can’t recognize partial progress. What change better supports progress assessment?
B. Break design into multiple interim milestones (e.g., PDR, CDR, IFC) with weighted EV values
C. Use only LOE for design
D. Tie EV to hours spent on design
Explanation: Splitting a complex design effort into sequential interim milestones with assigned weights (e.g., 20/30/50) allows EV to reflect meaningful intermediate achievements while retaining objective criteria. Pure 0/100 hides partial progress and can distort performance signals.
A. Keep a single milestone but adjust its date monthly