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Google Data Engineer Sample Questions:

1. Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

A) A processing time trigger
B) The with Allowed Lateness method
C) An event time trigger
D) An hourly watermark

2. You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

A) Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
B) Use BigQuery to export the data for the table to a CSV fil
C) Add a new column called FullName to the Users tabl
D) Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullNam G. Run a BigQuery load job to load the new CSV file into BigQuery.
E) Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.
F) Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.

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) Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.
B) Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger 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 are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?

A) PCollection
B) Pipeline
C) Sink API
D) Transform

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

Solutions: