Google PCA Practice Question: Analyze and optimize technical and business processes

Exhibit

{
  "kind": "compute#instance",
  "name": "instance-1",
  "machineType": "https://www.googleapis.com/compute/v1/projects/my-project/zones/us-central1-a/machineTypes/n1-standard-2",
  "cpuPlatform": "Intel Skylake",
  "creationTimestamp": "2024-01-01T00:00:00.000-08:00",
  "status": "RUNNING"
}

Refer to the exhibit. The output is from `gcloud compute instances describe instance-1 --format=json`. What can you conclude from this output?

⚠ Common exam trap

Google Cloud often tests the distinction between predefined and custom machine types by hiding the machine type in the `machineType` URL, and candidates mistakenly think any non-standard name implies a custom type, but the key is checking for the `custom-` prefix or explicit CPU/memory fields.

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

✓

The instance is billed based on the n1-standard-2 machine type.

The output from `gcloud compute instances describe instance-1 --format=json` would include a `machineType` field that specifies the full URL of the machine type, such as `https://www.googleapis.com/compute/v1/projects/.../zones/.../machineTypes/n1-standard-2`. This confirms the instance is using the predefined n1-standard-2 machine type, which has 2 vCPUs and 7.5 GB of memory, and billing is based on that predefined type. The absence of a `custom` suffix or custom CPU/memory values in the machine type field indicates it is not a custom machine type.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✓
The instance is billed based on the n1-standard-2 machine type.
Why this is correct
The JSON output includes the machineType field referencing n1-standard-2, which defines the instance's CPU and memory allocation. Billing for Compute Engine instances derives from the machine type specified at creation, so the exhibited value confirms the instance is charged according to n1-standard-2 pricing.
✗
The instance is using a custom machine type.
Why it's wrong here
A custom machine type appears as machineType referencing a custom-* name with explicit vCPU and memory values; this output shows a standard predefined type instead. It tempts because custom machine types are a frequent Compute Engine exam topic, and they would be correct where the instance specifies non-standard vCPU or memory sizing.
✗
The instance is using committed use discounts.
Why it's wrong here
Committed use discounts are billing constructs recorded in the project's commitments, not in an instance's describe output; the instance JSON shows no commitment reference. It tempts because CUDs reduce compute cost and are commonly examined, but they would be the answer when the stem asks about a purchased commitment or its discount coverage.
✗
The instance has a GPU attached.
Why it's wrong here
A GPU attachment appears in the instance's guestAccelerators field, which is absent here, so no accelerator is configured. It tempts because GPU-enabled instances are a common Compute Engine topic, and a describe output would list accelerators if present; that field would be the correct evidence for a GPU-backed workload.

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Google PCA Practice Question: Analyze and optimize technical and business processes

Exhibit

Why each option matters

Option-by-option breakdown

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