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Google Data Engineer Sample Questions:

1. You work for a manufacturing company that sources up to 750 different components, each from a different supplier. You've collected a labeled dataset that has on average 1000 examples for each unique component. Your team wants to implement an app to help warehouse workers recognize incoming components based on a photo of the component. You want to implement the first working version of this app (as Proof-Of-Concept) within a few working days. What should you do?

A) Use Cloud Vision AutoML with the existing dataset.
B) Use Cloud Vision API by providing custom labels as recognition hints.
C) Train your own image recognition model leveraging transfer learning techniques.
D) Use Cloud Vision AutoML, but reduce your dataset twice.

2. You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

A) Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
B) Add a new column called FullName to the Users tabl
C) Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.
D) Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullNam G. Run a BigQuery load job to load the new CSV file into BigQuery.
E) Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
F) Use BigQuery to export the data for the table to a CSV fil

3. You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

A) Use Cloud Dataproc to run your transformation
B) Configure the job to use non-default Compute Engine machine types when needed.
C) Use the diagnose command to generate an operational output archiv
D) Use Cloud Dataflow to run your transformation
E) Use Cloud Dataproc to run your transformation
F) Monitor the job system lag with Stackdrive
G) Use the default autoscaling setting for worker instances.
H) Monitor CPU utilization for the cluste
I) Monitor the total execution time for a sampling of job
J) Resize the number of worker nodes in your cluster via the command line.
K) Locate the bottleneck and adjust cluster resources.
L) Use Cloud Dataflow to run your transformation

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

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

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

Solutions: