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Google GCP-DE Exam Syllabus Topics:

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

2. Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer dat a. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

A) Increase your network bandwidth from your datacenter to GCP.
B) Increase the CPU size on your server.
C) Increase your network bandwidth from Compute Engine to Cloud Storage.
D) Increase the size of the Google Persistent Disk on your server.

3. You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

A) Use WHERE date BETWEEN YYYY-MM-DD AND YYYY-MM-DD
B) Use the WHERE_PARTITIONTIME pseudo column
C) Use the TABLE_DATE_RANGE function
D) Use SELECT IF.(date >= YYYY-MM-DD AND date <= YYYY-MM-DD

4. 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) Use BigQuery to export the data for the table to a CSV fil
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) Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
E) Add a new column called FullName to the Users tabl
F) 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.

5. Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

A) An hourly watermark
B) An event time trigger
C) A processing time trigger
D) The with Allowed Lateness method

Solutions: