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Google Professional-Data-Engineer Exam Syllabus Topics:

SectionWeightObjectives
Designing data processing systems20%- Designing for regulatory and security requirements
  • 1. Implementing access control and data protection
  • 2. Ensuring data privacy and compliance
- Designing for business requirements
  • 1. Designing for reliability and fault tolerance
  • 2. Selecting appropriate storage solutions
  • 3. Designing for scalability and elasticity
Maintaining and automating data workloads18%- Resource optimization
  • 1. Choosing appropriate compute and storage options
  • 2. Cost management and resource allocation
- Automation and repeatability
  • 1. Automating deployment and updates
  • 2. Implementing CI/CD for data systems
Operationalizing machine learning models20%- Preparing data for ML
  • 1. Handling structured and unstructured data
  • 2. Feature engineering and data preparation
- Deploying and maintaining ML models
  • 1. Model serving and monitoring
  • 2. Optimizing model performance and cost
Building and operationalizing data processing systems25%- Deploying and managing systems
  • 1. Managing infrastructure and resources
  • 2. Monitoring and logging data processes
- Building data pipelines
  • 1. Transforming and cleaning data
  • 2. Orchestrating data workflows
  • 3. Ingesting data from various sources
Ensuring solution quality and reliability17%- Testing and validating data systems
  • 1. Data quality validation
  • 2. Performance and scalability testing
- Troubleshooting and optimization
  • 1. Optimizing queries and workloads
  • 2. Diagnosing performance issues

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Google Certified Professional Data Engineer Exam Sample Questions (Q361-Q366):

NEW QUESTION # 361
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: D

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 # 362
Your company is planning to migrate a large on-premises data warehouse to BigQuery. The data is currently stored in a proprietary, vendor-specific format. You need to perform a batch migration of this data to BigQuery. What should you do?

Answer: C

Explanation:
Comprehensive and Detailed Explanation:
The challenge here is dealing with a "proprietary, vendor-specific format" for a one-time batch migration.
Option C is the correct answer because it represents the most universal and reliable pattern for migration from any source system. By first exporting the data into a standard, interoperable format like CSV (or preferably, a self-describing format like Avro or Parquet), you decouple the process from the proprietary source. These standard files can then be easily uploaded to Cloud Storage (the recommended staging area for BigQuery loads) and loaded into BigQuery in a highly performant and parallelized manner.
Option A is incorrect because the bq command-line tool cannot connect directly to an on-premises data warehouse to pull data. It loads data from files or streams.
Option B is incorrect because the BigQuery Data Transfer Service (DTS) has connectors for specific, common data sources (like Teradata, Redshift, S3). It is unlikely to have a connector for a generic
"proprietary, vendor-specific format."
Option D is incorrect because Datastream is a Change Data Capture (CDC) service designed for real-time replication of databases, not for a large-scale, one-time batch migration of a data warehouse.
Reference (Google Cloud Documentation Concepts):Google Cloud's "Data warehouse migration to BigQuery" guide outlines several migration strategies. For batch data transfer, the recommended path is Extract, Transfer, Load (ETL). This involves extracting data from the source into files (in formats like CSV, Avro, Parquet), transferring those files to Cloud Storage, and then loading them into BigQuery. This approach is recommended for its reliability and compatibility with any source system


NEW QUESTION # 363
You've migrated a Hadoop job from an on-premises cluster to Dataproc and Good Storage. Your Spark job is a complex analytical workload fiat consists of many shuffling operations, and initial data are parquet toes (on average 200-400 MB size each) You see some degradation in performance after the migration to Dataproc so you'd like to optimize for it. Your organization is very cost-sensitive so you'd Idee to continue using Dataproc on preemptibles (with 2 non-preemptibles workers only) for this workload. What should you do?

Answer: C


NEW QUESTION # 364
You store and analyze your relational data in BigQuery on Google Cloud with all data that resides in US regions. You also have a variety of object stores across Microsoft Azure and Amazon Web Services (AWS), also in US regions. You want to query all your data in BigQuery daily with as little movement of data as possible. What should you do?

Answer: C

Explanation:
BigQuery Omni is a multi-cloud analytics solution that lets you use the BigQuery interface to analyze data stored in other public clouds, such as AWS and Azure, without moving or copying the data. BigLake tables are a type of external table that let you query structured data in external data stores with access delegation. By using BigQuery Omni and BigLake tables, you can query data in AWS and Azure object stores directly from BigQuery, with minimal data movement and consistent performance. References:
* 1: Introduction to BigLake tables
* 2: Deep dive on how BigLake accelerates query performance
* 3: BigQuery Omni and BigLake (Analytics Data Federation on GCP)


NEW QUESTION # 365
Which of the following IAM roles does your Compute Engine account require to be able to run pipeline jobs?

Answer: A

Explanation:
The dataflow.worker role provides the permissions necessary for a Compute Engine service
account to execute work units for a Dataflow pipeline


NEW QUESTION # 366
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