mediumMultiple ChoiceObjective-mapped
PDE Practice Question: A company runs a real-time anomaly detection…
A company runs a real-time anomaly detection system on Google Cloud. Streaming data from IoT devices is ingested via Pub/Sub, processed by Dataflow (Apache Beam), and results are written to Bigtable for low-latency serving. Recently, the system has been experiencing increased latency and occasional data loss. The Dataflow pipeline shows high system lag and backlog in Pub/Sub. The Bigtable cluster has 3 nodes and is reporting high CPU utilization (over 90%). The team suspects the issue is with the pipeline configuration. They have already verified that there are no errors in the pipeline code and no network issues. Which action should they take to resolve the issue?
⚠ Common exam trap
Google Cloud often tests the misconception that scaling Dataflow workers or changing machine types always resolves pipeline latency, but the trap here is that the bottleneck is at the sink (Bigtable), so you must scale the sink first to relieve backpressure.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the number of Bigtable nodes to handle the write throughput.
The high CPU utilization on Bigtable (over 90%) indicates that the cluster is saturated and cannot keep up with the write throughput from Dataflow. This causes backpressure in the pipeline, leading to increased system lag and backlog in Pub/Sub, and eventually data loss when Pub/Sub messages expire. Increasing the number of Bigtable nodes directly addresses the bottleneck by distributing the write load and reducing CPU pressure, which allows the pipeline to drain the backlog and reduce latency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Increase the number of Bigtable nodes to handle the write throughput.
Why this is correct
High CPU utilization suggests Bigtable is overwhelmed; adding nodes increases capacity.
- ✗
Change the Dataflow worker machine type to n2-standard-8.
Why it's wrong here
Faster workers would also increase write throughput, not solving the bottleneck.
- ✗
Decrease the batch size in the Dataflow pipeline to reduce latency.
Why it's wrong here
Smaller batches increase the number of write requests, putting more load on Bigtable.
- ✗
Increase the number of Dataflow workers to process messages faster.
Why it's wrong here
More workers would write more data to Bigtable, potentially worsening the bottleneck.
Visual reference
Go deeper
Related to this question
About these practice questions
This PDE question is part of Courseiva's 890-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.