GCP-DE PDF Questions | Online GCP-DE Training

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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Ingesting and processing data25%- Transforming data
  • 1. Applying data processing logic
  • 2. Optimizing transformations for performance
  • 3. Managing schema evolution
- Building data pipelines
  • 1. Developing batch and streaming workflows
  • 2. Handling data quality and consistency
  • 3. Implementing ingestion mechanisms
Topic 2: Storing and managing data20%- Optimizing storage performance and cost
  • 1. Implementing cost optimization strategies
  • 2. Configuring storage for access patterns
- Implementing storage solutions
  • 1. Designing data warehouses and data lakes
  • 2. Using data storage services appropriately
  • 3. Managing data lifecycle and retention
Topic 3: Designing data processing systems24%- Designing data pipelines
  • 1. Defining architecture for batch and streaming processing
  • 2. Planning for data migration and integration
  • 3. Designing for data transformation and enrichment
- Planning data solutions
  • 1. Selecting appropriate storage solutions
  • 2. Planning for data security and compliance
  • 3. Designing for reliability, scalability, and efficiency
Topic 4: Maintaining and automating data workloads18%- Automation and optimization
  • 1. Automating deployment and management
  • 2. Optimizing resource usage and costs
- Operationalizing workloads
  • 1. Ensuring reliability and recoverability
  • 2. Monitoring and troubleshooting pipelines
  • 3. Orchestrating and scheduling jobs
Topic 5: Preparing data for analysis and machine learning13%- Enabling data analysis
  • 1. Optimizing query performance
  • 2. Preparing datasets for querying and reporting
  • 3. Implementing data sharing and governance
- Preparing data for ML
  • 1. Supporting model training and serving
  • 2. Feature engineering and data preparation

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Google Data Engineer Sample Questions (Q28-Q33):

NEW QUESTION # 28
When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.

Answer: D

Explanation:
When using Cloud Dataproc clusters, configure your browser to use the SOCKS proxy. The SOCKS proxy routes data intended for the Cloud Dataproc cluster through an SSH tunnel.
Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces


NEW QUESTION # 29
MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

Answer: D


NEW QUESTION # 30
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?

Answer: A


NEW QUESTION # 31
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

Answer: D

Explanation:
Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections Reference: https://cloud.google.com/dataflow/model/pcollection


NEW QUESTION # 32
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

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 # 33
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