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Google GCP-DE Exam Syllabus Topics:

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

2. 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) Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.
B) Develop an App Engine application to schedule and request the status of the jobs 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.

3. For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

A) Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
B) Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.
C) Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
D) Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.

4. You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?

A) The Python code should be stored in a revision control system and modified as the incoming data's schema changes
B) Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe.Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery
C) Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
D) Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
E) Merge the transformed tables together with a SQL query
F) Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio

5. Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?

A) Change "person" to "city.person".
B) Change "person" to "person.city".
C) Add ", UNNEST(person)" before the WHERE clause.
D) Add ", UNNEST(city)" before the WHERE clause.

Solutions: