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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. 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) Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
B) Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
C) 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.
D) Introduce data compression for each file to increase the rate file of file transfer.
E) Assemble 1,000 files into a tape archive (TAR) fil
F) Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.

2. Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?

A) Change "person" to "city.person".
B) Add ", UNNEST(city)" before the WHERE clause.
C) Change "person" to "person.city".
D) Add ", UNNEST(person)" before the WHERE clause.

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) Introduce resource hierarchy to leverage access control policy inheritance.
B) Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
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) Use Cloud Deployment Manager to automate access provision.
E) Create distinct groups for various teams, and specify groups in Cloud IAM policies.
F) For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces

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

5. 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) 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.
B) Write an Apache Beam pipeline that creates a BigQuery table per data
C) Re-create the tables using DD
D) 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.
E) Partition the tables by a column containing a TIMESTAMP or DATE Type.
F) Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.

Solutions: