PDE Designing Data Processing Systems Practice Question
A company wants to use Dataprep to clean and transform raw CSV files stored in Cloud Storage before loading into BigQuery. The data quality checks show missing values and inconsistent date formats. Which Dataprep feature should they use to handle these issues?
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
✓
Recipe steps
Recipe steps allow chaining transformations like fill missing values and format dates. Data quality profiling identifies issues but doesn't fix them. Scheduling automates execution. Wrangler is the UI, not a specific feature for transformations.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Data quality profiling
Why it's wrong here
Profiling detects issues but does not transform data.
- ✗
Scheduling
Why it's wrong here
Scheduling runs the job at a set time, but does not define transformations.
- ✗
Wrangler
Why it's wrong here
Wrangler is the Dataprep interface; recipe steps are the actual transformation units.
- ✓
Recipe steps
Why this is correct
Recipe steps define transformations such as impute missing values and parse dates.
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This PDE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PDE exam.