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| Section | Weight | Objectives |
|---|
| Topic 1: Data Preparation and Ingestion | 30% | - Data extraction and transfer tools
- 1. Dataflow, BigQuery Data Transfer Service, Database Migration Service
- 2. Cloud Data Fusion, Storage Transfer Service
- Data formats and classification
- 1. Formats: CSV, JSON, Parquet, Avro, database tables
- 2. Structured, semi-structured, unstructured data
- Storage solutions selection
- 1. Storage location types: regional, dual-regional, multi-regional, zonal
- 2. Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner
- Data loading methods
- 1. Batch and streaming ingestion
- 2. gcloud, BQ CLI, client libraries
|
| Topic 2: Data Analysis and Presentation | 27% | - Business intelligence and decision support
- 1. Answer business questions with data
- 2. Communicate findings effectively
- Data exploration and analysis
- 1. Jupyter notebooks for analysis
- 2. Identify trends, patterns, insights
- 3. BigQuery SQL queries
- Data visualization and reporting
- 1. Looker dashboards and reports
- 2. Visualization best practices
|
| Topic 3: Data Management and Governance | 25% | - Data security and access control
- 1. IAM roles and permissions
- 2. Data encryption and protection
- Compliance and governance
- 1. Data privacy and regulatory requirements
- 2. Data stewardship and cataloging
- Data quality and maintenance
- 1. Data validation and cleaning
- 2. Data lifecycle management
|
| Topic 4: Data Pipeline Orchestration | 18% | - Pipeline automation and scheduling
- 1. Error handling and monitoring
- 2. Schedule and monitor data jobs
- Transformation tools selection
- 1. Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, Dataform
- Data transformation concepts
- 1. Data transformation requirements
- 2. ETL vs ELT processes
|
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Google Associate Data Practitioner Sample Questions (Q22-Q27):
NEW QUESTION # 22
You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?
- A. Use Vertex Al Workbench to develop a custom model for purchase prediction.
- B. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
- C. Use Colab Enterprise to develop a custom model for purchase prediction.
- D. Use BigQuery ML to create a logistic regression model for purchase prediction.
Answer: D
NEW QUESTION # 23
Your organization is building a new application on Google Cloud. Several data files will need to be stored in Cloud Storage. Your organization has approved only two specific cloud regions where these data files can reside. You need to determine a Cloud Storage bucket strategy that includes automated high availability.
What should you do?
- A. Create a single-region bucket in each of the two regions, and use Storage Transfer Service to replicate the data across the buckets in both regions.
- B. Create a dual-region bucket, and upload the files to this bucket.
- C. Create a multi-region bucket, and upload the files to this bucket.
- D. Create a single-region bucket in each of the two regions, and use the gcloud storage command to replicate the data across the buckets in both regions.
Answer: B
NEW QUESTION # 24
You work for a home insurance company. You are frequently asked to create and save risk reports with charts for specific areas using a publicly available storm event dataset. You want to be able to quickly create and re- run risk reports when new data becomes available. What should you do?
- A. Export the storm event dataset as a CSV file. Import the file to Google Sheets, and use cell data in the worksheets to create charts.
- B. Reference and query the storm event dataset using SQL in a Colab Enterprise notebook. Display the table results and document with Markdown, and use Matplotlib to create charts.
- C. Reference and query the storm event dataset using SQL in BigQuery Studio. Export the results to Google Sheets, and use cell data in the worksheets to create charts.
- D. Copy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.
Answer: D
NEW QUESTION # 25
You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage.
The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?
Answer: C
NEW QUESTION # 26
You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rows and transactional dat a. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?
- A. Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.
- B. Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.
- C. Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.
- D. Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.
Answer: B
NEW QUESTION # 27
......
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