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Google GCP-DE Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Building and operationalizing data processing systems | 28%-33% | - Building data processing systems
|
| Managing and optimizing solutions | 20%-25% | - Managing resources and costs
|
| Ensuring solution quality | 20%-25% | - Security and compliance
|
| Designing data processing systems | 22%-27% | - Designing for data ingestion
|
Google Data Engineer Sample Questions:
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. Use Cloud Deployment Manager to automate access provision.
- E. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
- F. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
Correct Answer: A,E 🗳️
You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)
- A. Replicate external Hive tables to the native ones.
- B. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
- C. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
- D. Then run the Hadoop utility to copy them do HDF
- E. Mount the Hive tables from HDFS.
- F. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
- G. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
- H. Mount the Hive tables locally.
- I. Load the ORC files into BigQuer
- J. Replicate external Hive tables to the native ones.
- K. Mount the Hive tables locally.
- L. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
Correct Answer: F,K 🗳️
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?
- A. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
- B. Create a table on Cloud SQL, and insert and delete rows with the job information
- C. Create an API using App Engine to receive and send messages to the applications
- D. Create a table on Cloud Spanner, and insert and delete rows with the job information
Correct Answer: A 🗳️
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. Add ", UNNEST(person)" before the WHERE clause.
- B. Change "person" to "city.person".
- C. Add ", UNNEST(city)" before the WHERE clause.
- D. Change "person" to "person.city".
Correct Answer: A 🗳️
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You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?
- A. PigLatin using Pig
- B. Python using MapReduce
- C. HiveQL using Hive
- D. Java using MapReduce
Correct Answer: B 🗳️
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