Pay attention on Pass-king GCP-DE Practice Materials, clear exam 100% for sure

Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. 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) Create an application that writes to a Cloud SQL database to store the dat
C) Create an application that provides an AP
D) Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
E) 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.
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.

2. Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?

A) Add ", UNNEST(person)" before the WHERE clause.
B) Change "person" to "city.person".
C) Change "person" to "person.city".
D) Add ", UNNEST(city)" before the WHERE clause.

3. 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) Merge the transformed tables together with a SQL query
B) 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
C) Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
D) Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
E) Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
F) The Python code should be stored in a revision control system and modified as the incoming data's schema changes

4. Cloud Dataproc is a managed Apache Hadoop and Apache service.

A) Ignite
B) Fire
C) Blaze
D) Spark

5. Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer dat a. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

A) Increase the CPU size on your server.
B) Increase your network bandwidth from your datacenter to GCP.
C) Increase the size of the Google Persistent Disk on your server.
D) Increase your network bandwidth from Compute Engine to Cloud Storage.

Solutions: