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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

A) Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.
B) Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
C) Use BigQuery to export the data for the table to a CSV fil
D) Add a new column called FullName to the Users tabl
E) Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullNam G. Run a BigQuery load job to load the new CSV file into BigQuery.
F) Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.

2. What are two methods that can be used to denormalize tables in BigQuery?

A) 1) Use a partitioned table; 2) Join tables into one table
B) 1) Join tables into one table; 2) Use nested repeated fields
C) 1) Split table into multiple tables; 2) Use a partitioned table
D) 1) Use nested repeated fields; 2) Use a partitioned table

3. 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) Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
B) Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
C) Merge the transformed tables together with a SQL query
D) The Python code should be stored in a revision control system and modified as the incoming data's schema changes
E) Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
F) 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

4. You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the dat a. Which two actions should you take? (Choose two.)

A) Modify your Cloud Dataflow pipeline to use the CoGroupByKey transform before writing to Cloud Bigtable
B) Configure your Cloud Dataflow pipeline to use local execution
C) Modify your Cloud Dataflow pipeline to use the Flatten transform before writing to Cloud Bigtable
D) Increase the number of nodes in the Cloud Bigtable cluster
E) Increase the maximum number of Cloud Dataflow workers by setting maxNumWorkers in PipelineOptions

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

Solutions: