2026 Professional Google GCP-DE Valid Exam Objectives

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

>> GCP-DE Valid Exam Objectives <<

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Google Data Engineer Sample Questions (Q43-Q48):

NEW QUESTION # 43
Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

A. The CSV data has not gone through an ETL phase before loading into BigQuery.
B. The CSV data loaded in BigQuery is not using BigQuery's default encoding.
C. The CSV data has invalid rows that were skipped on import.
D. The CSV data loaded in BigQuery is not flagged as CSV.

Answer: C

NEW QUESTION # 44
Which of the following is NOT true about Dataflow pipelines?

A. Dataflow pipelines can consume data from other Google Cloud services
B. Dataflow pipelines can be programmed in Java
C. Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner
D. Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources

Answer: C

Explanation:
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/

NEW QUESTION # 45
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. Mount the Hive tables locally.
C. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
D. Mount the Hive tables from HDFS.
E. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
F. Load the ORC files into BigQuer
G. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
H. Replicate external Hive tables to the native ones.
I. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
J. Mount the Hive tables locally.
K. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
L. Then run the Hadoop utility to copy them do HDF

Answer: B,G

NEW QUESTION # 46
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

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

Answer: C

Explanation:
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance

NEW QUESTION # 47
What are all of the BigQuery operations that Google charges for?

A. Storage, queries, and streaming inserts
B. Storage, queries, and loading data from a file
C. Queries and streaming inserts
D. Storage, queries, and exporting data

Answer: A

Explanation:
Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.
Reference: https://cloud.google.com/bigquery/pricing

NEW QUESTION # 48
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