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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. MJTelco is building a custom interface to share dat
a. They have these requirements:
They need to do aggregations over their petabyte-scale datasets.
They need to scan specific time range rows with a very fast response time (milliseconds). Which combination of Google Cloud Platform products should you recommend?

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

2. What are two methods that can be used to denormalize tables in BigQuery?

A) 1) Use nested repeated fields; 2) Use a partitioned table
B) 1) Split table into multiple tables; 2) Use a partitioned table
C) 1) Use a partitioned table; 2) Join tables into one table
D) 1) Join tables into one table; 2) Use nested repeated fields

3. An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

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

4. You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

A) Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
B) Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
C) Consume the stream of data in Cloud Dataflow using Kafka I
D) If that number falls below 4000, send an alert.
E) Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
F) If that number falls below 4000, send an alert.
G) Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
H) Set a sliding time window of 1 hour every 5 minute
I) Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
J) Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
K) Consume the stream of data in Cloud Dataflow using Kafka I
L) Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
M) Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

5. You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?

A) Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
B) Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
C) Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
D) The Python code should be stored in a revision control system and modified as the incoming data's schema changes
E) Merge the transformed tables together with a SQL query
F) Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery

Solutions: