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Google Data Engineer Sample Questions:

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

2. You work for a global shipping company. You want to train a model on 40 TB of data to predict which ships in each geographic region are likely to cause delivery delays on any given day. The model will be based on multiple attributes collected from multiple sources. Telemetry data, including location in GeoJSON format, will be pulled from each ship and loaded every hour. You want to have a dashboard that shows how many and which ships are likely to cause delays within a region. You want to use a storage solution that has native functionality for prediction and geospatial processing. Which storage solution should you use?

A) Cloud Datastore
B) Cloud Bigtable
C) Cloud SQL for PostgreSQL
D) BigQuery

3. Which of the following statements is NOT true regarding Bigtable access roles?

A) To give a user access to only one table in a project, you must configure access through your application.
B) To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.
C) You can configure access control only at the project level.
D) Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.

4. You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

A) In the event of an emergency, use a point-in-time snapshot to recover the data.
B) In the event of an emergency, use a point-in-time snapshot to recover the data.
C) In the event of an emergency, use the backup copy of the table.
D) Set the BigQuery dataset to be regiona
E) Set the BigQuery dataset to be regiona
F) In the event of an emergency, use the backup copy of the table.
G) Set the BigQuery dataset to be multi-regional
H) Set the BigQuery dataset to be multi-regional
I) Create a scheduled query to make copies of the data to tables suffixed with the time of the backup
J) Create a scheduled query to make copies of the data to tables suffixed with the time of the backu

5. You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

A) Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
B) Preserve the structure of the data as much as possible.
C) Denormalize the data as must as possible.
D) Use BigQuery UPDATE to further reduce the size of the dataset.
E) Develop a data pipeline where status updates are appended to BigQuery instead of updated.
F) Use BigQuery'ssupport for external data sources to query.

Solutions: