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Google ADP Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Management and Governance25%- Data quality and maintenance
  • 1. Data lifecycle management
  • 2. Data validation and cleaning
- Data security and access control
  • 1. IAM roles and permissions
  • 2. Data encryption and protection
- Compliance and governance
  • 1. Data stewardship and cataloging
  • 2. Data privacy and regulatory requirements
Topic 2: Data Preparation and Ingestion30%- Storage solutions selection
  • 1. Storage location types: regional, dual-regional, multi-regional, zonal
  • 2. Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner
- Data extraction and transfer tools
  • 1. Dataflow, BigQuery Data Transfer Service, Database Migration Service
  • 2. Cloud Data Fusion, Storage Transfer Service
- Data loading methods
  • 1. Batch and streaming ingestion
  • 2. gcloud, BQ CLI, client libraries
- Data formats and classification
  • 1. Formats: CSV, JSON, Parquet, Avro, database tables
  • 2. Structured, semi-structured, unstructured data
Topic 3: Data Pipeline Orchestration18%- Transformation tools selection
  • 1. Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, Dataform
- Data transformation concepts
  • 1. ETL vs ELT processes
  • 2. Data transformation requirements
- Pipeline automation and scheduling
  • 1. Schedule and monitor data jobs
  • 2. Error handling and monitoring
Topic 4: Data Analysis and Presentation27%- Data exploration and analysis
  • 1. BigQuery SQL queries
  • 2. Identify trends, patterns, insights
  • 3. Jupyter notebooks for analysis
- Data visualization and reporting
  • 1. Visualization best practices
  • 2. Looker dashboards and reports
- Business intelligence and decision support
  • 1. Answer business questions with data
  • 2. Communicate findings effectively

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Google Associate Data Practitioner Sample Questions (Q45-Q50):

NEW QUESTION # 45
Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one- time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?

Answer: A


NEW QUESTION # 46
You are designing a pipeline to process data files that arrive in Cloud Storage by 3:00 am each day. Data processing is performed in stages, where the output of one stage becomes the input of the next. Each stage takes a long time to run. Occasionally a stage fails, and you have to address the problem. You need to ensure that the final output is generated as quickly as possible. What should you do?

Answer: C


NEW QUESTION # 47
You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of dat a. What should you do?

Answer: B


NEW QUESTION # 48
Your company's customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files' metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files. What should you do?

Answer: C


NEW QUESTION # 49
Your organization uses scheduled queries to perform transformations on data stored in BigQuery. You discover that one of your scheduled queries has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

Answer: B


NEW QUESTION # 50
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