Question 634 of 1,446
Manage a solution that can span multiple database technologies →mediumMultiple ChoiceObjective-mapped
PCDE Practice Question: Manage a solution that can span multiple database technologies
You have a Cloud Bigtable instance with a single cluster. To improve availability and durability, you want to enable replication across two regions. After configuring replication, you notice that reads from the replica cluster show data that is not yet consistent with the primary. What is the expected consistency model for Bigtable replication?
⚠ Common exam trap
A common misconception is that Bigtable replication provides strongly consistent reads across all clusters, similar to synchronous replication, but it is eventually consistent. Only the primary cluster guarantees strong consistency.
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
✓
Eventually consistent reads from replicas
Cloud Bigtable replication uses an eventually consistent model for reads from replica clusters. When you write to the primary cluster, the data is asynchronously replicated to the replica cluster using a distributed replication pipeline. This means that reads from the replica cluster may return stale data until the replication process completes, and there is no guarantee of immediate consistency with the primary.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Read-your-writes consistency from the primary
Why it's wrong here
While the primary cluster provides read-your-writes consistency, the question is about the replica cluster.
- ✓
Eventually consistent reads from replicas
Why this is correct
Bigtable uses async replication, so replicas are eventually consistent. This is the expected behavior.
- ✗
Strongly consistent reads from any cluster
Why it's wrong here
Bigtable replication is eventually consistent; reads from replicas may not reflect the latest writes.
- ✗
Strict serializability across clusters
Why it's wrong here
Strict serializability is not provided by Bigtable replication; it is an eventually consistent system.
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Same concept, more angles
2 more ways this is tested on PCDE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A company is using Bigtable for a high-throughput write workload. They need to monitor replication lag between clusters in a replicated setup and ensure that reads are eventually consistent. Which two configurations should they check? (Choose TWO.)
medium- A.Create a replication dashboard in Data Studio
- B.Set the replication consistency level to 'strong'
- ✓ C.Ensure replication is set to asynchronous (default)
- ✓ D.Monitor the 'bigtable.googleapis.com/cluster/replication_lag' metric
- E.Enable synchronous replication for zero lag
Why C: Bigtable replication is asynchronous and eventually consistent by default. Replication lag can be monitored using the 'replication lag' metric in Cloud Monitoring. Consistency levels are not configurable; async is the default.
Variation 2. You need to monitor the replication lag between a Cloud SQL for MySQL primary instance and its read replica. Which metric should you use to set up an alert?
easy- A.cloudsql.googleapis.com/database/replication/replica_count
- B.cloudsql.googleapis.com/database/cpu/utilization
- C.cloudsql.googleapis.com/database/memory/utilization
- ✓ D.cloudsql.googleapis.com/database/replication/replication_lag
Why D: Cloud SQL exposes a metric 'replication/replication_lag' (or 'cloudsql.googleapis.com/database/replication/replication_lag') that measures the lag in seconds between the primary and replica. This is the correct metric for alerting.
Last reviewed: Jul 4, 2026
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