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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

Question 1

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

Question 2

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

Question 3

Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

A. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
B. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
C. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
D. Cloud Pub/Sub, Cloud Dataflow, and Local SSD

Question 4

You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)

A. Mount the Hive tables from HDFS.
B. Replicate external Hive tables to the native ones.
C. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
D. Mount the Hive tables locally.
E. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
F. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
G. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
H. Then run the Hadoop utility to copy them do HDF
I. Replicate external Hive tables to the native ones.
J. Load the ORC files into BigQuer
K. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
L. Mount the Hive tables locally.

Question 5

Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

A. YARN
B. Pig
C. Spark
D. Hive

Solutions: