Google GCP-DE Exam | Valid GCP-DE Exam Prep - High Pass Rate Study Materials GCP-DE Review

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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Operationalizing data and ML pipelines	30%	- Pipeline automation and orchestration 1. Cloud Composer workflows 2. Scheduling and monitoring pipelines - Monitoring and troubleshooting 1. Performance optimization and debugging 2. Logging and observability
Topic 2: Designing data processing systems	20%	- Data pipeline architecture design 1. Batch vs streaming data processing selection 2. Scalable data ingestion design - Storage and data modeling 1. Data warehouse design using BigQuery 2. Data lake architecture on Google Cloud Storage
Topic 3: Maintaining and optimizing data and ML solutions	20%	- Machine learning integration 1. BigQuery ML usage 2. Vertex AI integration for pipelines - Security and governance 1. Data encryption and compliance 2. IAM and access control
Topic 4: Building and operationalizing data processing systems	30%	- Data ingestion and transformation 1. ETL/ELT workflows 2. Pub/Sub streaming ingestion - Data pipeline implementation 1. Dataproc and Spark-based processing 2. Dataflow pipeline development

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Free PDF 2026 Google GCP-DE: Authoritative Valid Data Engineer Exam Prep

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Google Data Engineer Sample Questions (Q15-Q20):

NEW QUESTION # 15
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster .

A. master node
B. worker node
C. application node
D. conditional node

Answer: A

Explanation:
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster master node. The cluster master-host-name is the name of your Cloud Dataproc cluster followed by an -m suffix-for example, if your cluster is named "my-cluster", the master-host-name would be "my-cluster-m". Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces

NEW QUESTION # 16
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. Create separate views to cover each month, and query from these views
B. Convert the sharded tables into a single partitioned table
C. Enable query caching so you can cache data from previous months
D. Convert all daily log tables into date-partitioned tables

Answer: D

NEW QUESTION # 17
You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

A. Cloud Scheduler
B. Workflow Templates on Cloud Dataproc
C. cron
D. Cloud Composer

Answer: B

NEW QUESTION # 18
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. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
B. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
C. Use BigQuery to export the data for the table to a CSV fil
D. 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.
E. Add a new column called FullName to the Users tabl
F. 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.

Answer: A

NEW QUESTION # 19
You need to deploy additional dependencies to all of a Cloud Dataproc cluster at startup using an existing initialization action. Company security policies require that Cloud Dataproc nodes do not have access to the Internet so public initialization actions cannot fetch resources. What should you do?

A. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
B. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
C. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
D. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter

Answer: B

NEW QUESTION # 20
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