Exam Professional Cloud Architect All Questions

View all questions & answers for the Professional Cloud Architect exam

Exam Professional Cloud Architect topic 9 question 4 discussion

Actual exam question from Google's Professional Cloud Architect

Question #: 4
Topic #: 9

[All Professional Cloud Architect Questions]

For this question, refer to the TerramEarth case study. A new architecture that writes all incoming data to BigQuery has been introduced. You notice that the data is dirty, and want to ensure data quality on an automated daily basis while managing cost.
What should you do?

A. Set up a streaming Cloud Dataflow job, receiving data by the ingestion process. Clean the data in a Cloud Dataflow pipeline.
B. Create a Cloud Function that reads data from BigQuery and cleans it. Trigger the Cloud Function from a Compute Engine instance.
C. Create a SQL statement on the data in BigQuery, and save it as a view. Run the view daily, and save the result to a new table.
D. Use Cloud Dataprep and configure the BigQuery tables as the source. Schedule a daily job to clean the data.

Show Suggested Answer

Suggested Answer: D 🗳️

by Barniyah at Feb. 5, 2020, 8:39 p.m.

Comments

Submit Cancel

Sj10

Highly Voted 5 years, 4 months ago

Option D, as data needs to be cleaned .. Dataprep has the capabilities to clean dirty data

upvoted 35 times

tartar

4 years, 10 months ago

D is ok

upvoted 13 times

...

melono

2 years, 7 months ago

looks like D https://cloud.google.com/dataprep

upvoted 3 times

...

motty

4 years, 11 months ago

dataprep is GUI driven process to analyse adhoc data dumped on GCS, it has not place in this use case

upvoted 5 times

...

vindahake

Highly Voted 5 years, 3 months ago

automated daily ... answer is D

upvoted 12 times

...

nbneeraj

Most Recent 6 months ago

Selected Answer: A

Question says data is dirty. So we need to clean the dirty data before loading it into big query. Option D says using Dataprep once dirty data is in big query. Option A says use ETL: Dataflow to clean it first. A datawarehouse developer will always go with Option A. Use ETL tool to clean the data first and then load in big query warehouse. So the option is A

upvoted 1 times

...

odacir

1 year, 6 months ago

Selected Answer: D

Cloud Dataprep is not cheap. Today i will recommend to used a schedule DataForm or dbt for cleaning...

upvoted 1 times

...

red_panda

1 year, 11 months ago

Selected Answer: D

D without any doubt. Dataflow is for data elaboration. Dataprep is for data preparation (and cleaning).

upvoted 2 times

...

RVivek

2 years, 4 months ago

Selected Answer: D

B & C does not make sense. A is costly and in realtime The question says on daily basis and cost effective hence D

upvoted 4 times

...

surajkrishnamurthy

2 years, 6 months ago

Selected Answer: D

D is the correct answer

upvoted 2 times

...

megumin

2 years, 7 months ago

Selected Answer: D

D is ok

upvoted 1 times

...

cbarg

2 years, 9 months ago

Selected Answer: D

Ans is D. Please refer to this example: https://medium.com/google-cloud/how-to-schedule-a-bigquery-etl-job-with-dataprep-b1c314883ab9

upvoted 4 times

...

ShadowLord

2 years, 9 months ago

Selected Answer: A

Options should be A. 1. Cost in D would be higher. e.g. First load dirty data into DB and then run Data Prep Jobs to clean the data and load into some different target Data . Overall cost of scanning the data and the loading is like double the cost. Then identifying already clean data and dirty data is again a challenge on daily basis after the data growth is significant 2. Data Stream can be utilized to cleanse the data while loading

upvoted 5 times