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Google GCP-DE Exam Syllabus Topics:

Section	Weight	Objectives
Managing and optimizing solutions	20%-25%	- Managing resources and costs 1. Optimize storage and compute usage 2. Control operational costs 3. Tune query and pipeline performance - Reliability and scalability 1. Design highly available systems 2. Scale data workloads efficiently 3. Implement disaster recovery strategies
Building and operationalizing data processing systems	28%-33%	- Building data processing systems 1. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services 2. Implement streaming data systems 3. Implement batch processing systems - Operationalizing systems 1. Monitor data pipelines and workloads 2. Implement fault tolerance and recovery 3. Manage logging and alerting
Designing data processing systems	22%-27%	- Designing data pipelines 1. Build ETL and ELT pipelines 2. Design orchestration workflows 3. Optimize pipeline performance and cost - Designing for data ingestion 1. Design batch and streaming ingestion solutions 2. Design scalable and reliable ingestion architecture 3. Select appropriate storage systems
Ensuring solution quality	20%-25%	- Data quality management 1. Maintain metadata and lineage 2. Implement data governance 3. Validate and clean data - Security and compliance 1. Protect sensitive data 2. Implement encryption and compliance controls 3. Manage IAM and access control

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Google Data Engineer Sample Questions (Q60-Q65):

NEW QUESTION # 60
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. 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.
B. Use BigQuery to export the data for the table to a CSV fil
C. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
D. Add a new column called FullName to the Users tabl
E. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
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: E

NEW QUESTION # 61
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

A. Preemptible workers cannot store data.
B. A Dataproc cluster cannot have only preemptible workers.
C. If a preemptible worker is reclaimed, then a replacement worker must be added manually.
D. Preemptible workers cannot use persistent disk.

Answer: A,B

Explanation:
The following rules will apply when you use preemptible workers with a Cloud Dataproc cluster: Processing only-Since preemptibles can be reclaimed at any time, preemptible workers do not store data.
Preemptibles added to a Cloud Dataproc cluster only function as processing nodes.
No preemptible-only clusters-To ensure clusters do not lose all workers, Cloud Dataproc cannot create preemptible-only clusters.
Persistent disk size-As a default, all preemptible workers are created with the smaller of 100GB or the primary worker boot disk size. This disk space is used for local caching of data and is not available through HDFS.
The managed group automatically re-adds workers lost due to reclamation as capacity permits. Reference:
https://cloud.google.com/dataproc/docs/concepts/preemptible-vms

NEW QUESTION # 62
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. Use Cloud Deployment Manager to automate access provision.
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. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
D. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
E. Introduce resource hierarchy to leverage access control policy inheritance.
F. Create distinct groups for various teams, and specify groups in Cloud IAM policies.

Answer: E,F

NEW QUESTION # 63
Cloud Dataproc is a managed Apache Hadoop and Apache service.

A. Fire
B. Ignite
C. Spark
D. Blaze

Answer: C

Explanation:
Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
Reference: https://cloud.google.com/dataproc/docs/

NEW QUESTION # 64
You are a retailer that wants to integrate your online sales capabilities with different in-home assistants, such as Google Home. You need to interpret customer voice commands and issue an order to the backend systems. Which solutions should you choose?

A. Cloud Speech-to-Text API
B. Cloud AutoML Natural Language
C. Dialogflow Enterprise Edition
D. Cloud Natural Language API

Answer: B

NEW QUESTION # 65
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