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Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|
| Topic 1: Data Management and Governance | 25% | - Data security and access control
- 1. Data encryption and protection
- 2. IAM roles and permissions
- Compliance and governance
- 1. Data stewardship and cataloging
- 2. Data privacy and regulatory requirements
- Data quality and maintenance
- 1. Data lifecycle management
- 2. Data validation and cleaning
|
| Topic 2: Data Preparation and Ingestion | 30% | - 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
- Data extraction and transfer tools
- 1. Dataflow, BigQuery Data Transfer Service, Database Migration Service
- 2. Cloud Data Fusion, Storage Transfer Service
- Storage solutions selection
- 1. Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner
- 2. Storage location types: regional, dual-regional, multi-regional, zonal
|
| Topic 3: Data Analysis and Presentation | 27% | - Business intelligence and decision support
- 1. Communicate findings effectively
- 2. Answer business questions with data
- Data visualization and reporting
- 1. Looker dashboards and reports
- 2. Visualization best practices
- Data exploration and analysis
- 1. BigQuery SQL queries
- 2. Jupyter notebooks for analysis
- 3. Identify trends, patterns, insights
|
| Topic 4: Data Pipeline Orchestration | 18% | - Transformation tools selection
- 1. Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, Dataform
- Pipeline automation and scheduling
- 1. Schedule and monitor data jobs
- 2. Error handling and monitoring
- Data transformation concepts
- 1. ETL vs ELT processes
- 2. Data transformation requirements
|
>> ADP PDF題庫 <<
最新的Google ADP考古題
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最新的 Google Cloud Certified ADP 免費考試真題 (Q54-Q59):
問題 #54
You manage a Cloud Storage bucket that stores temporary files created during data processing. These temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days. What should you do?
- A. Create a Cloud Run function that runs daily and deletes files older than seven days.
- B. Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.
- C. Develop a batch process using Dataflow that runs weekly and deletes files based on their age.
- D. Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.
答案:D
問題 #55
Your retail company wants to analyze customer reviews to understand sentiment and identify areas for improvement. Your company has a large dataset of customer feedback text stored in BigQuery that includes diverse language patterns, emojis, and slang. You want to build a solution to classify customer sentiment from the feedback text. What should you do?
- A. Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.
- B. Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.
- C. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.
- D. Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
答案:D
問題 #56
Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?
- A. Create directed acyclic graphs (DAGS) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
- B. Use Cloud Scheduler to schedule the jobs to run.
- C. Create directed acyclic graphs (DAGS) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
- D. Use Cloud Tasks to schedule and run the jobs asynchronously.
答案:A
問題 #57
You created a curated dataset of market trends in BigQuery that you want to share with multiple external partners. You want to control the rows and columns that each partner has access to. You want to follow Google-recommended practices. What should you do?
- A. Grant each partner read access to the BigQuery dataset by using 1AM roles.
- B. Create a separate Cloud Storage bucket for each partner. Export the dataset to each bucket and assign each partner to their respective bucket. Grant bucket-level access by using 1AM roles.
- C. Create a separate project for each partner and copy the dataset into each project. Publish each dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
- D. Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
答案:D
問題 #58
Your company uses Looker to visualize and analyze sales dat
a. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?
- A. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
- B. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
- C. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
- D. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
答案:B
問題 #59
......
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