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Google ADP Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation and Exploration | 20-30% | - Perform exploratory data analysis (EDA) - Transform and prepare data for analysis - Ingest and acquire data - Identify data quality issues - Explore data through visualization and queries |
| Topic 2: Data Visualization and Insights | 20-30% | - Choose appropriate visualization types - Interpret and communicate findings - Present data insights to stakeholders - Build visualizations using Looker Studio - Create dashboards and reports |
| Topic 3: Data Processing and Analytics | 20-30% | - Build and maintain data pipelines - Use BigQuery and SQL for analytics - Aggregate and summarize data - Query and analyze datasets - Apply statistical methods for analysis |
| Topic 4: Data-Driven Decision Making | 10-20% | - Define success metrics - Translate business requirements into data solutions - Assess data quality and completeness - Identify stakeholders and requirements |
Google Associate Data Practitioner Sample Questions:
1. Your organization uses Dataflow pipelines to process real-time financial transactions. You discover that one of your Dataflow jobs has failed. You need to troubleshoot the issue as quickly as possible. What should you do?
A) Create a custom script to periodically poll the Dataflow API for job status updates, and send email alerts if any errors are identified.
B) Use the gcloud CLI tool to retrieve job metrics and logs, and analyze them for errors and performance bottlenecks.
C) Navigate to the Dataflow Jobs page in the Google Cloud console. Use the job logs and worker logs to identify the error.
D) Set up a Cloud Monitoring dashboard to track key Dataflow metrics, such as data throughput, error rates, and resource utilization.
2. 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?
A) ETLT
B) EL
C) ETL
D) ELT
3. Your company is migrating their batch transformation pipelines to Google Cloud. You need to choose a solution that supports programmatic transformations using only SQL. You also want the technology to support Git integration for version control of your pipelines. What should you do?
A) Use Dataform workflows.
B) Use Cloud Data Fusion pipelines.
C) Use Dataflow pipelines.
D) Use Cloud Composer operators.
4. Your organization has several datasets in BigQuery. The datasets need to be shared with your external partners so that they can run SQL queries without needing to copy the data to their own projects. You have organized each partner's data in its own BigQuery dataset. Each partner should be able to access only their dat a. You want to share the data while following Google-recommended practices. What should you do?
A) Use Analytics Hub to create a listing on a private data exchange for each partner dataset. Allow each partner to subscribe to their respective listings.
B) Grant the partners the bigquery.user IAM role on the BigQuery project.
C) Create a Dataflow job that reads from each BigQuery dataset and pushes the data into a dedicated Pub /Sub topic for each partner. Grant each partner the pubsub. subscriber IAM role.
D) Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.
5. Your organization is conducting analysis on regional sales metrics. Data from each regional sales team is stored as separate tables in BigQuery and updated monthly. You need to create a solution that identifies the top three regions with the highest monthly sales for the next three months. You want the solution to automatically provide up-to-date results. What should you do?
A) Create a BigQuery table that performs a union across all of the regional sales tables. Use the row_number() window function to query the new table.
B) Create a BigQuery materialized view that performs a cross join across all of the regional sales tables. Use the row_number() window function to query the new materialized view.
C) Create a BigQuery table that performs a cross join across all of the regional sales tables. Use the rank() window function to query the new table.
D) Create a BigQuery materialized view that performs a union across all of the regional sales tables. Use the rank() window function to query the new materialized view.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: C | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: D |
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