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Google Data Engineer Sample Questions:

1. Cloud Dataproc is a managed Apache Hadoop and Apache service.

A) Ignite
B) Blaze
C) Spark
D) Fire

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

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

4. Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

A) Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.
B) Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
C) Introduce data compression for each file to increase the rate file of file transfer.
D) Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
E) Assemble 1,000 files into a tape archive (TAR) fil
F) Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.

5. You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

A) Create a temporary table in Cloud Bigtable that will act as a buffer for new data
B) Use a larger instance type for your Cloud Dataflow workers
C) Change the zone of your Cloud Dataflow pipeline to run in us-central1
D) Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery
E) Increase the number of max workers
F) Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery
G) Create a temporary table in Cloud Spanner that will act as a buffer for new data

Solutions: