GCP-DE Exam Experience - Latest GCP-DE Exam Notes

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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Preparing data for analysis and machine learning13%- Enabling data analysis
  • 1. Optimizing query performance
  • 2. Implementing data sharing and governance
  • 3. Preparing datasets for querying and reporting
- Preparing data for ML
  • 1. Supporting model training and serving
  • 2. Feature engineering and data preparation
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
Storing and managing data20%- Optimizing storage performance and cost
  • 1. Configuring storage for access patterns
  • 2. Implementing cost optimization strategies
- Implementing storage solutions
  • 1. Managing data lifecycle and retention
  • 2. Designing data warehouses and data lakes
  • 3. Using data storage services appropriately
Ingesting and processing data25%- Building data pipelines
  • 1. Developing batch and streaming workflows
  • 2. Handling data quality and consistency
  • 3. Implementing ingestion mechanisms
- Transforming data
  • 1. Managing schema evolution
  • 2. Applying data processing logic
  • 3. Optimizing transformations for performance
Designing data processing systems24%- Designing data pipelines
  • 1. Designing for data transformation and enrichment
  • 2. Planning for data migration and integration
  • 3. Defining architecture for batch and streaming processing
- Planning data solutions
  • 1. Planning for data security and compliance
  • 2. Designing for reliability, scalability, and efficiency
  • 3. Selecting appropriate storage solutions

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Google Data Engineer Sample Questions (Q66-Q71):

NEW QUESTION # 66
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.)

Answer: A,H


NEW QUESTION # 67
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

Answer: A,D

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 # 68
An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

Answer: C


NEW QUESTION # 69
What are two methods that can be used to denormalize tables in BigQuery?

Answer: C

Explanation:
The conventional method of denormalizing data involves simply writing a fact, along with all its dimensions, into a flat table structure. For example, if you are dealing with sales transactions, you would write each individual fact to a record, along with the accompanying dimensions such as order and customer information. The other method for denormalizing data takes advantage of BigQuery's native support for nested and repeated structures in JSON or Avro input data. Expressing records using nested and repeated structures can provide a more natural representation of the underlying data. In the case of the sales order, the outer part of a JSON structure would contain the order and customer information, and the inner part of the structure would contain the individual line items of the order, which would be represented as nested, repeated elements.
Reference: https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data


NEW QUESTION # 70
By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

Answer: C

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 # 71
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