Google Data Engineer : GCP-DE

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 deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
• 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.

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.

You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

• A. Dialogflow Enterprise Edition
• B. Cloud AutoML Natural Language
• C. Cloud Natural Language API
• D. Cloud Speech-to-Text API

You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?

• A. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
• B. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
• C. Merge the transformed tables together with a SQL query
• D. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
• E. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
• F. Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery

You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are: Decoupling producer from consumer Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely Near real-time SQL query Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?

• A. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
• B. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
• C. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
• D. Create an application that writes to a Cloud SQL database to store the dat
• E. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
• F. Create an application that provides an AP