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Google Data Engineer Sample Questions:

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

2. Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?

A) A sequential numeric ID
B) A stock symbol followed by a timestamp
C) A timestamp followed by a stock symbol
D) A non-sequential numeric ID

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

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

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

Solutions: