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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q58-Q63):

NEW QUESTION # 58
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. Mount the Hive tables from HDFS.
B. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
C. Replicate external Hive tables to the native ones.
D. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
E. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
F. Mount the Hive tables locally.
G. Then run the Hadoop utility to copy them do HDF
H. Replicate external Hive tables to the native ones.
I. Load the ORC files into BigQuer
J. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
K. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
L. Mount the Hive tables locally.

Answer: B,F

NEW QUESTION # 59
You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?

A. Cloud Bigtable
B. Cloud Spanner
C. Cloud SQL
D. Cloud Datastore

Answer: C

NEW QUESTION # 60
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. Deploy the Cloud SQL Proxy on the Cloud Dataproc master
B. Use Resource Manager to add the service account used by the Cloud Dataproc cluster to the Network User role
C. Use an SSH tunnel to give the Cloud Dataproc cluster access to the Internet
D. Copy all dependencies to a Cloud Storage bucket within your VPC security perimeter

Answer: B

NEW QUESTION # 61
What are all of the BigQuery operations that Google charges for?

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

Answer: B

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 # 62
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster .

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

Answer: C

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 # 63
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