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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Ensuring solution quality20%-25%- Data quality management
  • 1. Implement data governance
  • 2. Validate and clean data
  • 3. Maintain metadata and lineage
- Security and compliance
  • 1. Manage IAM and access control
  • 2. Protect sensitive data
  • 3. Implement encryption and compliance controls
Building and operationalizing data processing systems28%-33%- Building data processing systems
  • 1. Implement streaming data systems
  • 2. Implement batch processing systems
  • 3. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
- Operationalizing systems
  • 1. Manage logging and alerting
  • 2. Implement fault tolerance and recovery
  • 3. Monitor data pipelines and workloads
Managing and optimizing solutions20%-25%- Managing resources and costs
  • 1. Tune query and pipeline performance
  • 2. Control operational costs
  • 3. Optimize storage and compute usage
- Reliability and scalability
  • 1. Implement disaster recovery strategies
  • 2. Scale data workloads efficiently
  • 3. Design highly available systems
Designing data processing systems22%-27%- Designing data pipelines
  • 1. Build ETL and ELT pipelines
  • 2. Optimize pipeline performance and cost
  • 3. Design orchestration workflows
- Designing for data ingestion
  • 1. Select appropriate storage systems
  • 2. Design batch and streaming ingestion solutions
  • 3. Design scalable and reliable ingestion architecture

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Google Data Engineer Sample Questions (Q40-Q45):

NEW QUESTION # 40
Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

Answer: C

Explanation:
When collecting and grouping data into windows, Beam uses triggers to determine when to emit the aggregated results of each window.
Processing time triggers. These triggers operate on the processing time - the time when the data element is processed at any given stage in the pipeline. Event time triggers. These triggers operate on the event time, as indicated by the timestamp on each data element. Beam's default trigger is event time-based.
Reference: https://beam.apache.org/documentation/programming-guide/#triggers


NEW QUESTION # 41
You are operating a Cloud Dataflow streaming pipeline. The pipeline aggregates events from a Cloud Pub/Sub subscription source, within a window, and sinks the resulting aggregation to a Cloud Storage bucket. The source has consistent throughput. You want to monitor an alert on behavior of the pipeline with Cloud Stackdriver to ensure that it is processing dat a. Which Stackdriver alerts should you create?

Answer: C


NEW QUESTION # 42
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?

Answer: C

Explanation:
Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/transforms/windowing/Tri


NEW QUESTION # 43
Which Google Cloud Platform service is an alternative to Hadoop with Hive?

Answer: A

Explanation:
Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis. Google BigQuery is an enterprise data warehouse. Reference: https://en.wikipedia.org/wiki/Apache_Hive


NEW QUESTION # 44
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

Answer: A

Explanation:
A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server.
Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys


NEW QUESTION # 45
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