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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. Cloud Dataproc is a managed Apache Hadoop and Apache service.

A) Spark
B) Fire
C) Blaze
D) Ignite

2. You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn't like to eat
The user account information: Name, address, preferred meal times
The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schem a. Which Google Cloud Platform product should you use?

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

3. You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

A) Create separate views to cover each month, and query from these views
B) Enable query caching so you can cache data from previous months
C) Convert all daily log tables into date-partitioned tables
D) Convert the sharded tables into a single partitioned table

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

5. You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

A) Monitor the job system lag with Stackdrive
B) Use Cloud Dataflow to run your transformation
C) Configure the job to use non-default Compute Engine machine types when needed.
D) Use Cloud Dataproc to run your transformation
E) Monitor CPU utilization for the cluste
F) Use the default autoscaling setting for worker instances.
G) Locate the bottleneck and adjust cluster resources.
H) Resize the number of worker nodes in your cluster via the command line.
I) Use Cloud Dataproc to run your transformation
J) Use Cloud Dataflow to run your transformation
K) Use the diagnose command to generate an operational output archiv
L) Monitor the total execution time for a sampling of job

Solutions: