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Google Data Engineer Sample Questions:

1. 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) Cloud Bigtable
B) Cloud SQL
C) BigQuery
D) Cloud Datastore

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

3. You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

A) Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF
B) Deploy a Cloud Dataproc cluste
C) Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
D) Deploy a Cloud Dataproc cluste
E) Use an SSD persistent disk and 50% preemptible worker
F) Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance
G) Use a standard persistent disk and 50% preemptible worker
H) Change references in scripts from hdfs:// to gs://
I) Install the Cloud Storage connector, and store the data in Cloud Storag
J) Change references in scripts from hdfs:// to gs://
K) Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://

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

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

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

Solutions: