PDE Practice Question: A data analyst runs a complex SQL query in…

Exhibit

Refer to the exhibit.

BigQuery job error log:
{
  "jobId": "job_12345",
  "errorResult": {
    "reason": "resourcesExceeded",
    "message": "Resources exceeded during query execution: The query could not be executed in the allocated memory. High memory usage caused the query to fail."
  },
  "statistics": {
    "query": {
      "statementType": "SELECT",
      "totalSlotMs": 12000000,
      "totalBytesProcessed": 5000000000000
    }
  }
}

A data analyst runs a complex SQL query in BigQuery that joins multiple large tables and receives the above error. Which action is most likely to resolve the issue?

⚠ Common exam trap

Google Cloud often tests the misconception that performance tuning (e.g., clustering or sampling) can resolve resource exhaustion errors, when in fact the root cause is insufficient compute capacity that must be addressed by increasing slot allocation.

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 slots allocated to the project.

The error indicates that the query exceeded the available slot resources in the BigQuery project. Increasing the number of slots allocated to the project (option D) directly addresses this by providing more compute capacity for parallel query execution, which is the correct action to resolve resource exhaustion in BigQuery's serverless architecture.

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 a larger number of workers in the query execution.
Why it's wrong here
BigQuery does not expose worker count; it manages resources internally.
✗
Use smaller tables by sampling data.
Why it's wrong here
Sampling would reduce accuracy and may not be acceptable.
✗
Add clustering on join columns.
Why it's wrong here
Clustering can help but may not resolve memory exhaustion if the query is complex.
✓
Increase the number of slots allocated to the project.
Why this is correct
More slots provide more memory and CPU, reducing resource exceeded errors.

Visual reference

Quick reference

Cloud Service Model Comparison

Model	You Manage	Provider Manages	Examples
IaaS	OS, runtime, apps, data	Hardware, hypervisor, networking	EC2, Azure VMs, GCP Compute Engine
PaaS	Apps and data	OS, runtime, middleware, hardware	Elastic Beanstalk, Azure App Service
SaaS	Data and settings only	Everything else	Microsoft 365, Salesforce, Workday
FaaS / Serverless	Function code only	Infra, scaling, runtime	Lambda, Azure Functions, Cloud Run
CaaS	Containers and apps	Kubernetes, OS, hardware	EKS, AKS, GKE

Go deeper

PDE Practice Question: A data analyst runs a complex SQL query in…

Exhibit

Why each option matters

Option-by-option breakdown

Cloud Service Model Comparison

Related to this question