Google Data Engineer : GCP-DE

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

2. 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 Dataflow, and Local SSD
D) Cloud Pub/Sub, Cloud SQL, and Cloud Storage

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

4. You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the dat a. Which two actions should you take? (Choose two.)

A) Increase the number of nodes in the Cloud Bigtable cluster
B) Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions
C) Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable
D) Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable
E) Configure your Cloud Dataflow pipeline to use local execution

5. Flowlogistic's CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they've purchased a visualization tool to simplify the creation of BigQuery reports. However, they've been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

A) Export the data into a Google Sheet for virtualization.
B) Create a view on the table to present to the virtualization tool.
C) Create an additional table with only the necessary columns.
D) Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.

Solutions: