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Google Data Engineer Sample Questions:

1. You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

A) Cloud Speech-to-Text API
B) Dialogflow Enterprise Edition
C) Cloud Natural Language API
D) Cloud AutoML Natural Language

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

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

4. You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the dat a. Which two actions should you take? (Choose two.)

A) Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable
B) Configure your Cloud Dataflow pipeline to use local execution
C) Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions
D) Increase the number of nodes in the Cloud Bigtable cluster
E) Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable

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) 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) Create distinct groups for various teams, and specify groups in Cloud IAM policies.
D) Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
E) For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
F) Use Cloud Deployment Manager to automate access provision.

Solutions: