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- Google ADP Exam Syllabus Topics:
- Section Weight Objectives
- Topic 1: Data Analysis and Presentation 27% - Data exploration and analysis
- BigQuery SQL queries
- Identify trends, patterns, insights
- Jupyter notebooks for analysis
- Data visualization and reporting
- Visualization best practices
- Looker dashboards and reports
- Business intelligence and decision support
- Answer business questions with data
- Communicate findings effectively
- Topic 2: Data Preparation and Ingestion 30% - Storage solutions selection
- Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner
- Storage location types: regional, dual-regional, multi-regional, zonal
- Data extraction and transfer tools
- Cloud Data Fusion, Storage Transfer Service
- Dataflow, BigQuery Data Transfer Service, Database Migration Service
- Data formats and classification
- Formats: CSV, JSON, Parquet, Avro, database tables
- Structured, semi-structured, unstructured data
- Data loading methods
- Batch and streaming ingestion
- gcloud, BQ CLI, client libraries
- Topic 3: Data Management and Governance 25% - Data quality and maintenance
- Data lifecycle management
- Data validation and cleaning
- Compliance and governance
- Data privacy and regulatory requirements
- Data stewardship and cataloging
- Data security and access control
- IAM roles and permissions
- Data encryption and protection
- Topic 4: Data Pipeline Orchestration 18% - Pipeline automation and scheduling
- Error handling and monitoring
- Schedule and monitor data jobs
- Transformation tools selection
- Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, Dataform
- Data transformation concepts
- ETL vs ELT processes
- Data transformation requirements
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- Google Associate Data Practitioner Sample Questions (Q30-Q35):
- NEW QUESTION # 30
- You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?
- A. Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.
- B. Use Cloud Run functions to trigger the Spark job and email the report.
- C. Use Cloud Composer to orchestrate the Spark job and email the report.
- D. Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
- Answer: A
- NEW QUESTION # 31
- Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive dat a. What should you do?
- A. Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.
- B. Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.
- C. Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.
- D. Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.
- Answer: B
- NEW QUESTION # 32
- Your company currently uses an on-premises network file system (NFS) and is migrating data to Google Cloud. You want to be able to control how much bandwidth is used by the data migration while capturing detailed reporting on the migration status. What should you do?
- A. Use a Transfer Appliance.
- B. Use Storage Transfer Service.
- C. Use gcloud storage commands.
- D. Use Cloud Storage FUSE.
- Answer: B
- NEW QUESTION # 33
- Your organization's website uses an on-premises MySQL as a backend database. You need to migrate the on-premises MySQL database to Google Cloud while maintaining MySQL features. You want to minimize administrative overhead and downtime. What should you do?
- A. Use Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.
- B. Export the database tables to CSV files, and upload the files to Cloud Storage. Convert the MySQL schema to a Spanner schema, create a JSON manifest file, and run a Google-provided Dataflow template to load the data into Spanner.
- C. Install MySQL on a Compute Engine virtual machine. Export the database files using the mysqldump command. Upload the files to Cloud Storage, and import them into the MySQL instance on Compute Engine.
- D. Use a Google-provided Dataflow template to replicate the MySQL database in BigQuery.
- Answer: A
- NEW QUESTION # 34
- You are working with a large dataset of customer reviews stored in Cloud Storage. The dataset contains several inconsistencies, such as missing values, incorrect data types, and duplicate entries. You need toclean the data to ensure that it is accurate and consistent before using it for analysis. What should you do?
- A. Use BigQuery to batch load the data into BigQuery. Use SQL for cleaning and analysis.
- B. Use the PythonOperator in Cloud Composer to clean the data and load it into BigQuery. Use SQL for analysis.
- C. Use Cloud Run functions to clean the data and load it into BigQuery. Use SQL for analysis.
- D. Use Storage Transfer Service to move the data to a different Cloud Storage bucket. Use event triggers to invoke Cloud Run functions to load the data into BigQuery. Use SQL for analysis.
- Answer: A
- NEW QUESTION # 35
- .....
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