Google Data Engineer : GCP-DE

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Google Data Engineer Sample Questions:

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

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

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

3. You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

A) Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.
B) Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.
C) Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.
D) Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.

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

5. You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

A) cron
B) Cloud Composer
C) Workflow Templates on Cloud Dataproc
D) Cloud Scheduler

Solutions: