mediumMultiple Select
PDE Practice Question: A data engineer is migrating on-premises Hadoop…
A data engineer is migrating on-premises Hadoop jobs to Dataproc. Which TWO considerations are important?
⚠ Common exam trap
A common misconception is that HDFS must be retained for performance in the cloud, but the correct approach is to use Cloud Storage for data storage and Preemptible VMs for cost savings, while the Cloud Storage connector is a required component, not an overhead to avoid.
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
✓
Use Preemptible VMs for worker nodes to reduce cost
Option A is correct because Dataproc supports preemptible VMs (now called Spot VMs) for worker nodes, which can significantly reduce compute costs for fault-tolerant Hadoop workloads, though they may be reclaimed; Dataproc handles this by replacing preempted workers. Option B is correct because Cloud Storage (GCS) is the recommended storage layer for Dataproc, decoupling storage from compute, allowing clusters to be shut down without data loss, and providing higher durability and scalability than HDFS. Option C is incorrect because the Cloud Storage connector is essential for reading and writing data between Dataproc and GCS, and it does not introduce prohibitive overhead; avoiding it would prevent access to GCS. Option D is incorrect because keeping HDFS ties data to the cluster lifecycle, defeating the benefits of cloud elasticity and durability that GCS provides. Option E is incorrect because while master node availability matters, using on-demand VMs for the master is not a specific migration consideration highlighted here, and Dataproc already uses standard VMs for masters by default; the key cost and storage considerations are preemptible workers and GCS.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Preemptible VMs for worker nodes to reduce cost
Why this is correct
Preemptible VMs cost far less than standard instances, and Dataproc tolerates their eviction because HDFS is replaced by Cloud Storage. This satisfies the cost-reduction constraint while keeping jobs resilient, provided workers are not running single-node or stateful workloads.
- ✓
Use Cloud Storage instead of HDFS for data storage
Why this is correct
Hadoop jobs read and write through the HDFS API, but Dataproc decouples compute from storage: Cloud Storage acts as the durable layer, so clusters can be deleted without data loss. This satisfies the migration constraint by removing HDFS as the persistence dependency.
- ✗
Avoid using Cloud Storage connector to prevent overhead
Why it's wrong here
The Cloud Storage connector is the recommended way for Dataproc jobs to read and write data in Cloud Storage, so avoiding it removes the intended storage layer. It would be avoided only where workloads must stay entirely within HDFS on a persistent cluster.
- ✗
Keep HDFS for better performance
Why it's wrong here
Dataproc separates compute from storage, so keeping HDFS ties data to ephemeral cluster nodes and prevents jobs from reading Cloud Storage directly. HDFS suits persistent on-premises clusters where data locality is fixed, not ephemeral cloud clusters.
- ✗
Use on-demand VMs for master node to ensure availability
Why it's wrong here
On-demand master VMs do not ensure availability; a single master remains a single point of failure, and Dataproc already manages master placement. On-demand capacity suits short-lived, unpredictable clusters where preemption risk outweighs cost. For sustained production availability, the master should run on a standard VM with a secondary master, or use high-availability mode.
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