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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

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

2. Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

A) An event time trigger
B) The with Allowed Lateness method
C) A processing time trigger
D) An hourly watermark

3. You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)

A) Change the zone of your Cloud Dataflow pipeline to run in us-central1
B) Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery
C) Use a larger instance type for your Cloud Dataflow workers
D) Create a temporary table in Cloud Bigtable that will act as a buffer for new data
E) Increase the number of max workers
F) Create a temporary table in Cloud Spanner that will act as a buffer for new data
G) Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery

4. You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

A) Use BigQuery'ssupport for external data sources to query.
B) Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
C) Develop a data pipeline where status updates are appended to BigQuery instead of updated.
D) Use BigQuery UPDATE to further reduce the size of the dataset.
E) Preserve the structure of the data as much as possible.
F) Denormalize the data as must as possible.

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

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