Online Access Free Professional-Data-Engineer Practice Test

You are implementing a chatbot to help an online retailer streamline their customer service. The chatbot must be able to respond to both text and voice inquiries. You are looking for a low-code or no-code option, and you want to be able to easily train the chatbot to provide answers to keywords. What should you do?

• A.Use Dialogflow to implement the chatbot. defining the intents based on the most common queries collected.
• B.Use the Speech-to-Text API to build a Python application in a Compute Engine instance.
• C.Use the Speech-to-Text API to build a Python application in App Engine.
• D.Use Dialogflow for simple queries and the Speech-to-Text API for complex queries.

You have a petabyte of analytics data and need to design a storage and processing platform for it. You must be able to perform data warehouse-style analytics on the data in Google Cloud and expose the dataset as files for batch analysis tools in other cloud providers. What should you do?

• A.Store the full dataset in BigQuery, and store a compressed copy of the data in a Cloud Storage bucket.
• B.Store the warm data as files in Cloud Storage, and store theactive data inBigQuery. Keep this ratio as
  80% warm and 20% active.
• C.Store and process the entire dataset in Cloud Bigtable.
• D.Store and process the entire dataset in BigQuery.

You have an Apache Kafka Cluster on-prem with topics containing web application logs. You need to replicate the data to Google Cloud for analysis in BigQuery and Cloud Storage. The preferred replication method is mirroring to avoid deployment of Kafka Connect plugins.
What should you do?

• A.Deploy a Kafka cluster on GCE VM Instances. Configure your on-prem cluster to mirror your topics to the cluster running in GCE. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.
• B.Deploy a Kafka cluster on GCE VM Instances with the PubSub Kafka connector configured as a Sink connector. Use a Dataproc cluster or Dataflow job to read from Kafka and write to GCS.
• C.Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Source connector. Use a Dataflow job to read fron PubSub and write to GCS.
• D.Deploy the PubSub Kafka connector to your on-prem Kafka cluster and configure PubSub as a Sink connector. Use a Dataflow job to read fron PubSub and write to GCS.

You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones.
What should you do?

• A.Create a table on Cloud Spanner, and insert and delete rows with the job information
• B.Create a table on Cloud SQL, and insert and delete rows with the job information
• C.Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
• D.Create an API using App Engine to receive and send messages to the applications

Your software uses a simple JSON format for all messages. These messages are published to Google Cloud Pub/Sub, then processed with Google Cloud Dataflow to create a real-time dashboard for the CFO. During testing, you notice that some messages are missing in thedashboard. You check the logs, and all messages are being published to Cloud Pub/Sub successfully. What should you do next?

• A.Use Google Stackdriver Monitoring on Cloud Pub/Sub to find the missing messages.
• B.Switch Cloud Dataflow to pull messages from Cloud Pub/Sub instead of Cloud Pub/Sub pushing messages to Cloud Dataflow.
• C.Run a fixed dataset through the Cloud Dataflow pipeline and analyze the output.
• D.Check the dashboard application to see if it is not displaying correctly.