Context: Meta's PE role is essentially SRE-flavored. The OA mixes scripting + systems knowledge + debugging rather than pure algorithms, and the systems-knowledge weight has grown in the 2026 spring cycle. This piece pools 14 PE candidate debriefs into a concise distribution + prep path.
1. Basics
| Dimension | Detail |
|---|---|
| Platform | CoderPad (Meta template) |
| # questions | 3 |
| Time | 65–70 min |
| Language | Python (preferred), Bash, Perl, Java |
| Question mix | 1 algorithm + 1 script/data-processing + 1 Linux/systems |
Difference vs SDE: be fluent with stdlib (os, re, subprocess, collections) — PE OA problems almost always need them directly.
2. The 3 typical question slots
2.1 Q1: Algorithm + string processing
import re
from collections import Counter, defaultdict
def top_k_error_templates(lines, k):
pattern = re.compile(r'\[(\S+)\]\s+\[(\S+)\]\s+\[(\S+)\]\s+(.+)')
bucket = defaultdict(Counter)
for line in lines:
m = pattern.match(line)
if not m or m.group(2) != 'ERROR':
continue
service = m.group(3)
msg = re.sub(r'\b\d+\b', '<NUM>', m.group(4))
msg = re.sub(r'[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}', '<UUID>', msg)
bucket[service][msg] += 1
return {s: c.most_common(k) for s, c in bucket.items()}
Complexity: O(n·L).
2.2 Q2: Process / filesystem simulation
from collections import defaultdict, deque
def find_descendants(processes, target_name):
by_parent = defaultdict(list)
by_name = defaultdict(list)
for pid, ppid, name in processes:
by_parent[ppid].append(pid)
by_name[name].append(pid)
out = set()
for start in by_name[target_name]:
q = deque([start])
while q:
u = q.popleft()
if u in out:
continue
out.add(u)
q.extend(by_parent.get(u, []))
return sorted(out)
Complexity: O(N + E).
2.3 Q3: Debugging / systems knowledge
e.g., server has high
usr%, normalsys%, rising load avg, normal RX → pick most likely root cause + diagnostic commands.
Not coding — multiple choice + short answer. Drill:
top/htopfield semanticsiostat/vmstat/pidstatnetstat -tunapvsss -lntpstrace/perf/pprof- Symptoms and triage for OOM, TIME_WAIT pileup, disk IO saturation
3. PE-specific "systems mindset"
Meta PE interviews evaluate three things:
- Pinpoint the layer (app / middleware / OS / network / hardware)
- Produce an executable next command
- Quantify impact and propose a degradation path
Never answer "I'll check the logs" — say which log, what filter, exact grep.
4. 4-week prep roadmap
- Week 1: Python stdlib + regex deep dive
- Week 2: Linux basics (top, iostat, netstat, systemd) + 5 real outage case studies
- Week 3: 30 LC Easy/Medium algorithms
- Week 4: 3 timed mocks (65 min / 3 problems)
5. FAQ
Q1: How many problems in Meta PE OA?
A: 3 problems / 65–70 min. 1 algo + 1 script + 1 systems.
Q2: What language for Meta PE OA?
A: Python preferred. Bash works for some Q2 cases.
Q3: Same question pool as SDE?
A: No. PE problems are framed around production scenarios and require Linux/diagnostic fluency.
Q4: What's next after the PE OA?
A: Phone Screen (1 hr debugging + coding) → Onsite (4–5 rounds).
Q5: Does Meta PE sponsor H-1B?
A: Yes — Meta is one of the most sponsor-friendly North American PE employers.
Q6: Cooldown after a PE OA fail?
A: Meta's standard 6–12 months (role-dependent).
Q7: AI detection?
A: CoderPad has plagiarism detection; templated AI output gets flagged.
Q8: Is PE easier than SDE?
A: Not really — slightly lower algorithm bar, but a higher systems-knowledge + debugging bar.
6. Need Meta PE OA help?
- WeChat: Coding0201 · contact
- Email: [email protected]
- Telegram: @OAVOProxy
We offer: current-week Meta PE high-frequency questions, Linux outage case library, OA done-for-you, live VO support.
Last updated: 2026-05-11 | Author: oavoservice SRE team