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Google Data Engineer Sample Questions:

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

2. You architect a system to analyze seismic dat
a. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

A) Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
B) Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
C) Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
D) Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.

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

4. You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

A) Dialogflow Enterprise Edition
B) Cloud AutoML Natural Language
C) Cloud Speech-to-Text API
D) Cloud Natural Language API

5. As your organization expands its usage of GCP, many teams have started to create their own projects. Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects. Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies. Which two steps should you take? Choose 2 answers.

A) Introduce resource hierarchy to leverage access control policy inheritance.
B) Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
C) Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
D) Create distinct groups for various teams, and specify groups in Cloud IAM policies.
E) For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
F) Use Cloud Deployment Manager to automate access provision.

Solutions: