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Google GCP-DE Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Managing and optimizing solutions20%-25%- Managing resources and costs
  • 1. Control operational costs
  • 2. Optimize storage and compute usage
  • 3. Tune query and pipeline performance
- Reliability and scalability
  • 1. Design highly available systems
  • 2. Implement disaster recovery strategies
  • 3. Scale data workloads efficiently
Topic 2: Designing data processing systems22%-27%- Designing for data ingestion
  • 1. Design scalable and reliable ingestion architecture
  • 2. Select appropriate storage systems
  • 3. Design batch and streaming ingestion solutions
- Designing data pipelines
  • 1. Build ETL and ELT pipelines
  • 2. Design orchestration workflows
  • 3. Optimize pipeline performance and cost
Topic 3: Building and operationalizing data processing systems28%-33%- Operationalizing systems
  • 1. Monitor data pipelines and workloads
  • 2. Implement fault tolerance and recovery
  • 3. Manage logging and alerting
- Building data processing systems
  • 1. Implement streaming data systems
  • 2. Use BigQuery, Dataflow, Pub/Sub, Dataproc and related services
  • 3. Implement batch processing systems
Topic 4: Ensuring solution quality20%-25%- Security and compliance
  • 1. Implement encryption and compliance controls
  • 2. Protect sensitive data
  • 3. Manage IAM and access control
- Data quality management
  • 1. Maintain metadata and lineage
  • 2. Validate and clean data
  • 3. Implement data governance

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Google Data Engineer Sample Questions (Q18-Q23):

NEW QUESTION # 18
Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?

Answer: A,B

Explanation:
using a timestamp as the first element of a row key can cause a variety of problems.
In brief, when a row key for a time series includes a timestamp, all of your writes will target a single node; fill that node; and then move onto the next node in the cluster, resulting in hotspotting.
Suppose your system assigns a numeric ID to each of your application's users. You might be tempted to use the user's numeric ID as the row key for your table. However, since new users are more likely to be active users, this approach is likely to push most of your traffic to a small number of nodes. [https://cloud.google.com/bigtable/docs/schema-design] Reference:
https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotti


NEW QUESTION # 19
You have enabled the free integration between Firebase Analytics and Google BigQuery. Firebase now automatically creates a new table daily in BigQuery in the format app_events_YYYYMMDD. You want to query all of the tables for the past 30 days in legacy SQL. What should you do?

Answer: B

Explanation:
Reference:
https://cloud.google.com/blog/products/gcp/using-bigquery-and-firebase-analytics-to-understandyour-mobile-ap


NEW QUESTION # 20
Which of these is not a supported method of putting data into a partitioned table?

Answer: B

Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name. Reference: https://cloud.google.com/bigquery/docs/partitioned-tables


NEW QUESTION # 21
You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

Answer: C


NEW QUESTION # 22
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: B

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 # 23
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