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Objective 2.2

PDE

Building the pipelines

Objective 2.2 sits in Ingesting and processing the data, which carries 25% of the Data Engineer exam. The questions below are original, written from the official objective title above, and each explanation cites the Google Cloud page it rests on.

Objective title verbatim from the official objectives. Google Cloud exam page ↗

A worked example

Shown solved, with the whole explanation open: this is what every question here carries.

2-2Ingesting and processing the data

A team keeps its cluster data in on-cluster HDFS and wants autoscaling to shrink the cluster overnight. What makes autoscaling a poor fit here?

Pending resource metrics are unavailable for storageThose metrics exist; they simply do not track storage.
Scaling policies apply to storage nodes before computeNo ordering between storage and compute is applied.
Secondary workers cannot join a cluster running HDFSSecondary workers can join, but hold no HDFS blocks.
Primary workers must remain sufficient to host HDFS dataCorrect · your answerCorrect. Storage placement pins the floor.

Correct.

Concept

Elasticity assumes the state a node holds can be abandoned. When a node also stores the only copy of data, removing it becomes a data movement problem rather than a capacity decision.

Why D

Autoscaling is not intended for on-cluster HDFS. HDFS data lives only on primary workers, whose count must cover all of it, and decommissioning DataNodes can take hours.

Source

HDFS: Autoscaling is not intended for scaling on-cluster HDFS because: HDFS utilization is not a signal for autoscaling. HDFS data is only hosted on primary workers. The number of primary workers must be sufficient to host all HDFS data. Decommissioning HDFS DataNodes can delay the removal of workers.

Google Cloud: Autoscale clusters, checked August 2026
#gcp#dataproc#autoscaling#hdfs

Now you: objective 2.2 questions

No account needed. The explanation opens when you answer.

Sample question 1 of 3

2-2Ingesting and processing the data

An engineer plans to run a Spark Structured Streaming job on a cluster governed by an autoscaling policy. What is the standing limitation?

Sample question 2 of 3

2-2Ingesting and processing the data

A team wants a cluster to shrink to its minimum size whenever it sits idle between nightly runs. What is recommended in place of autoscaling?

Sample question 3 of 3

2-2Ingesting and processing the data

A team runs many small jobs alongside one long-running job on a cluster, and scale-down for the small jobs never happens until the long job ends. Which change avoids that?

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Read the sources

These are the official pages the questions above cite. Reading them is studying the objective from the primary source, which is what the explanations point you toward anyway.

More objectives in Ingesting and processing the data