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Google Data Engineer Sample Questions:

1. You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

A) Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
B) Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
C) Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
D) Create a cron schedule in Cloud Dataprep.

2. Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

A) Pig
B) Hive
C) YARN
D) Spark

3. You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

A) Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP
B) Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
C) Create an authorized view in BigQuery to restrict access to tables with sensitive data.
D) Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
E) Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.

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

5. You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

A) Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
B) Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
C) Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
D) Create an application that writes to a Cloud SQL database to store the dat
E) Create an application that provides an AP
F) Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.

Solutions: