Google GCP-DE : Data Engineer

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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

Question 1

You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

A. Consume the stream of data in Cloud Dataflow using Kafka I
B. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
C. If that number falls below 4000, send an alert.
D. If that number falls below 4000, send an alert.
E. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
F. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
G. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
H. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
I. Set a sliding time window of 1 hour every 5 minute
J. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
K. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
L. Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
M. Consume the stream of data in Cloud Dataflow using Kafka I

Question 2

You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

A. Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.
B. Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.
C. Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.
D. Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.

Question 3

You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

A. Store the data in Google Cloud Datastor
B. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
C. Load the data every 30 minutes into a new partitioned table in BigQuery.
D. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.
E. Store the data in a file in a regional Google Cloud Storage bucke
F. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore

Question 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. Combine highly co-dependent features into one representative feature.
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. Remove the features that have null values for more than 50% of the training records.

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

Solutions: