Exam Professional Data Engineer topic 1 question 67 discussion

Answer: C A & B - Need to build your own model, so discarded as options C D can do the job here using Cloud Video Intelligence API. BigTable is better option. So C is correct

Answer: C Description: Why to build own model, Video API with Bigtable is best solution

I don't even know if MLLib has out-of-the-box Computer Vision models. Developing this in Dataproc would be a nightmare. Using the computer vision API on the other hand makes perfect sense. The fact that the filtering must happen very fast and that this is a customer facing application points to BigTable so that there is very little latency and high availability. BigTable is eventually consistent but that doesn't really matter for this application. CloudSQL will ensure strong consistency which we don't really need but it is slower and supports max 64 TB. The description mentions multiple TBs. Not really sure what several means here, but Cloud SQL doesn't have a high cap.

We need a model that extracts labels from videos, so Vision API could be used. Then we need a DB very fast and that can handle several TB of data, so BigTable is the best choice. Answer is C.

Option C is the correct choice because it utilizes the Cloud Video Intelligence API to generate labels for the entities in the videos, which would save time and resources compared to building and training a model from scratch. Additionally, by storing the data in Cloud Bigtable, it allows for fast and efficient filtering of the predicted labels based on the user's viewing history and preferences. This is a more efficient and cost-effective approach than storing the data in Cloud SQL and performing joins and filters.

Answer is C Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences. 1. Rather than building a new model - it is better to use Google provide APIs, here - Google Video Intelligence. So option A and B rules out 2. Between SQL and Bigtable - Bigtable is the better option as Bigtable support row-key filtering. Joining the filters is not required. Reference: https://cloud.google.com/video-intelligence/docs/feature-label-detection

C. The cloud video intelillence api does the label generation without the need of building any model, A and B are excluded. Now, the bbdd most suitable for this is bigtable and not SQL (this big joins would be anything but fast). https://cloud.google.com/video-intelligence/docs/feature-label-detection

Vote for C

Answer: C Reference https://cloud.google.com/video-intelligence/docs/feature-label-detection

answer C: If we presume that use label of video as a rowkey, Bigtable will be the best option. because it can store several TB, but Cloud SQL is limited to 30TB.

Answer: C

Option C is the correct answer. 1. Rather than building a new model - it is better to use Google provide APIs, here - Google Video Intelligence. So option A and B rules out 2) Between SQL and Bigtable - Bigtable is the better option as Bigtable support row-key filtering. Joining the filters is not required.

Answer is D : BigTable doesnt support JOIN and not built for transactions - https://cloud.google.com/bigtable/docs/overview

Correct C

I doubt if C can be an answer. Will Bigtable allow filtering on labels?

Answer is C.

C. The recommendation requires filtering based on several TB of data, therefore BigTable is the recommended option vs Cloud SQL which is limited to 10TB.