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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
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
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
Operationalizing data and ML pipelines	30%	- Monitoring and troubleshooting 1. Performance optimization and debugging 2. Logging and observability - Pipeline automation and orchestration 1. Cloud Composer workflows 2. Scheduling and monitoring 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 (Q48-Q53):

NEW QUESTION # 48
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. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
B. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
C. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
D. Mount the Hive tables locally.
E. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
F. Replicate external Hive tables to the native ones.
G. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
H. Load the ORC files into BigQuer
I. Mount the Hive tables locally.
J. Replicate external Hive tables to the native ones.
K. Then run the Hadoop utility to copy them do HDF
L. Mount the Hive tables from HDFS.

Answer: G,I

NEW QUESTION # 49
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

A. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
B. Create a cron schedule in Cloud Dataprep.
C. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
D. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.

Answer: C

NEW QUESTION # 50
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. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
B. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
C. Introduce resource hierarchy to leverage access control policy inheritance.
D. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
E. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
F. Use Cloud Deployment Manager to automate access provision.

Answer: A,C

NEW QUESTION # 51
Which of the following statements is NOT true regarding Bigtable access roles?

A. You can configure access control only at the project level.
B. To give a user access to only one table in a project, you must configure access through your application.
C. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.
D. To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.

Answer: D

Explanation:
For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:
Read from, but not write to, any table within the project.
Read from and write to any table within the project, but not manage instances. Read from and write to any table within the project, and manage instances. Reference: https://cloud.google.com/bigtable/docs/access-control

NEW QUESTION # 52
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster .

A. master node
B. application node
C. worker 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 # 53
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