Google ADP Exam Dumps in PDF Format

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| Section | Weight | Objectives |
|---|
| Data Management and Governance | 25% | - Data quality and maintenance
- 1. Data validation and cleaning
- 2. Data lifecycle management
- 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 Analysis and Presentation | 27% | - 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
- Data exploration and analysis
- 1. Identify trends, patterns, insights
- 2. Jupyter notebooks for analysis
- 3. BigQuery SQL queries
|
| Data Pipeline Orchestration | 18% | - Data transformation concepts
- 1. ETL vs ELT processes
- 2. Data transformation requirements
- Transformation tools selection
- 1. Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, Dataform
- Pipeline automation and scheduling
- 1. Error handling and monitoring
- 2. Schedule and monitor data jobs
|
| Data Preparation and Ingestion | 30% | - 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. gcloud, BQ CLI, client libraries
- 2. Batch and streaming ingestion
- Data extraction and transfer tools
- 1. Cloud Data Fusion, Storage Transfer Service
- 2. Dataflow, BigQuery Data Transfer Service, Database Migration Service
- Data formats and classification
- 1. Formats: CSV, JSON, Parquet, Avro, database tables
- 2. Structured, semi-structured, unstructured data
|
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Google Associate Data Practitioner Sample Questions (Q26-Q31):
NEW QUESTION # 26
You are designing a BigQuery data warehouse with a team of experienced SQL developers. You need to recommend a cost- effective, fully-managed, serverless solution to build ELT processes with SQL pipelines.
Your solution must include source code control, environment parameterization, and data quality checks. What should you do?
- A. Use Dataform to build, orchestrate, and monitor the pipelines.
- B. Use Cloud Composer to orchestrate and run data workflows.
- C. Use Dataproc to run MapReduce jobs for distributed data processing.
- D. Use Cloud Data Fusion to visually design and manage the pipelines.
Answer: A
NEW QUESTION # 27
You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?
- A. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.
- B. Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.
- C. Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.
- D. Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.
Answer: B
NEW QUESTION # 28
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 Cloud Data Fusion pipelines.
- B. Use Dataflow pipelines.
- C. Use Dataform workflows.
- D. Use Cloud Composer operators.
Answer: C
NEW QUESTION # 29
Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard. What should you do?
- A. Create a BigQuery table that uses the SUM() function and the DATE_DIFF() function.
- B. Create a saved query in the BigQuery console that uses the SUM() function and the DATE_SUB() function. Re-run the saved query every month, and save the results to a BigQuery table.
- C. Create a materialized view in BigQuery that uses the SUM() function and the DATE_SUB() function.
- D. Create a BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.
Answer: C
NEW QUESTION # 30
You are working on a project that requires analyzing daily social media dat a. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing.
You need to transform and load this data into BigQuery for analysis. You want to follow the Google-recommended approach. What should you do?
- A. Use Cloud Run functions to transform and load the data into BigQuery.
- B. Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
- C. Use Dataflow to transform the data and write the transformed data to BigQuery.
- D. Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
Answer: C
NEW QUESTION # 31
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