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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn't like to eat
The user account information: Name, address, preferred meal times
The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schem a. Which Google Cloud Platform product should you use?

A) BigQuery
B) Cloud Datastore
C) Cloud Bigtable
D) Cloud SQL

2. Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?

A) A sequential numeric ID
B) A non-sequential numeric ID
C) A timestamp followed by a stock symbol
D) A stock symbol followed by a timestamp

3. You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

A) BigQuery
B) Cloud Datastore
C) Cloud Bigtable
D) Cloud SQL

4. Which of the following statements is NOT true regarding Bigtable access roles?

A) To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.
B) You can configure access control only at the project level.
C) To give a user access to only one table in a project, you must configure access through your application.
D) Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.

5. You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

A) Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
B) Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
C) Create an application that provides an AP
D) Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
E) Create an application that writes to a Cloud SQL database to store the dat
F) Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.

Solutions: