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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

A) Deploy the model using a KubernetesEngine cluste
B) Call the Cloud Natural Language API from your applicatio
C) Call the model from your application and process the results as labels.
D) Process the generated Entity Analysis as labels.
E) Process the generated Sentiment Analysis as labels.
F) Build and train a text classification model using TensorFlo
G) Call the Cloud Natural Language API from your applicatio
H) Call the model from your application and process the results as labels.
I) Build and train a text classification model using TensorFlo
J) Deploy the model using Cloud Machine Learning Engin

2. When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.

A) VPN
B) HTTPS
C) HTTP
D) SOCKS

3. You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

A) Cloud Composer
B) Workflow Templates on Cloud Dataproc
C) Cloud Scheduler
D) cron

4. You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

A) Remove the features that have null values for more than 50% of the training records.
B) Instead of feeding in each feature individually, average their values in batches of 3.
C) Eliminate features that are highly correlated to the output labels.
D) Combine highly co-dependent features into one representative feature.

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

Solutions: