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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

Question 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. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
B. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
C. 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.
D. 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.
E. Create an application that provides an AP
F. Create an application that writes to a Cloud SQL database to store the dat

Question 2

Which of these sources can you not load data into BigQuery from?

A. Google Drive
B. Google Cloud Storage
C. Google Cloud SQL
D. File upload

Question 3

Which Google Cloud Platform service is an alternative to Hadoop with Hive?

A. Cloud Datastore
B. BigQuery
C. Cloud Bigtable
D. Cloud Dataflow

Question 4

MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

A. Rowkey: dateColumn data: device_id, data_point
B. Rowkey: date#device_idColumn data: data_point
C. Rowkey: date#data_pointColumn data: device_id
D. Rowkey: device_idColumn data: date, data_point
E. Rowkey: data_pointColumn data: device_id, date

Question 5

You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?

A. PigLatin using Pig
B. HiveQL using Hive
C. Java using MapReduce
D. Python using MapReduce

Solutions: