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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

A) The CSV data has not gone through an ETL phase before loading into BigQuery.
B) The CSV data loaded in BigQuery is not using BigQuery's default encoding.
C) The CSV data loaded in BigQuery is not flagged as CSV.
D) The CSV data has invalid rows that were skipped on import.

2. You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

A) Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
B) Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.
C) Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
D) Create a cron schedule in Cloud Dataprep.

3. Which Google Cloud Platform service is an alternative to Hadoop with Hive?

A) Cloud Bigtable
B) Cloud Datastore
C) Cloud Dataflow
D) BigQuery

4. Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

A) Cloud Pub/Sub, Cloud SQL, and Cloud Storage
B) Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
C) Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
D) Cloud Pub/Sub, Cloud Dataflow, and Local SSD

5. 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.

Solutions: