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Google Data Engineer Sample Questions:

1. What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

A) Sessions
B) Windows
C) OutputCriteria
D) Triggers

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

3. You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30-90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

A) Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
B) Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.
C) Partition the tables by a column containing a TIMESTAMP or DATE Type.
D) Re-create the tables using DD
E) Modify your pipeline to maintain the last 30-90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
F) Write an Apache Beam pipeline that creates a BigQuery table per data

4. You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

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

5. As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

A) For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
B) Create distinct groups for various teams, and specify groups in Cloud IAM policies.
C) Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
D) Introduce resource hierarchy to leverage access control policy inheritance.
E) Use Cloud Deployment Manager to automate access provision.
F) Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.

Solutions: