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Google Data Engineer Sample Questions:

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

2. Which of these is not a supported method of putting data into a partitioned table?

A) Create a partitioned table and stream new records to it every day.
B) If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
C) Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
D) Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".

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

4. For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

A) Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
B) Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.
C) Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
D) Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.

5. Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

A) Use K-means Clustering to detect faces in the pixels.
B) Use feature engineering to add features for eyes, noses, and mouths to the input data.
C) Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
D) Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.

Solutions: