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Google GCP-DE Exam Syllabus Topics:

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

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Google Data Engineer Sample Questions (Q17-Q22):

NEW QUESTION # 17
If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

Answer: A

Explanation:
If you're running a performance test that depends upon Cloud Bigtable, be sure to follow these steps as you plan and execute your test:
Use a production instance. A development instance will not give you an accurate sense of how a production instance performs under load.
Use at least 300 GB of data. Cloud Bigtable performs best with 1 TB or more of data. However, 300 GB of data is enough to provide reasonable results in a performance test on a 3-node cluster. On larger clusters, use 100 GB of data per node.
Before you test, run a heavy pre-test for several minutes. This step gives Cloud Bigtable a chance to balance data across your nodes based on the access patterns it observes.
Run your test for at least 10 minutes. This step lets Cloud Bigtable further optimize your data, and it helps ensure that you will test reads from disk as well as cached reads from memory.
Reference: https://cloud.google.com/bigtable/docs/performance


NEW QUESTION # 18
You are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?

Answer: A

Explanation:
In Google Cloud, the Dataflow SDK provides a transform component. It is responsible for the data processing operation. You can use conditional, for loops, and other complex programming structure to create a branching pipeline.
Reference: https://cloud.google.com/dataflow/model/programming-model


NEW QUESTION # 19
You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?

Answer: B


NEW QUESTION # 20
Which of the following statements is NOT true regarding Bigtable access roles?

Answer: B

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 # 21
What are two methods that can be used to denormalize tables in BigQuery?

Answer: B

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 # 22
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