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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Data Management and Governance	25%	- Data security and access control 1. IAM roles and permissions 2. Data encryption and protection - Data quality and maintenance 1. Data validation and cleaning 2. Data lifecycle management - Compliance and governance 1. Data privacy and regulatory requirements 2. Data stewardship and cataloging
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. 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. Cloud Data Fusion, Storage Transfer Service 2. Dataflow, BigQuery Data Transfer Service, Database Migration Service
Data Analysis and Presentation	27%	- Data exploration and analysis 1. Identify trends, patterns, insights 2. BigQuery SQL queries 3. Jupyter notebooks for analysis - Data visualization and reporting 1. Looker dashboards and reports 2. Visualization best practices - Business intelligence and decision support 1. Communicate findings effectively 2. Answer business questions with data
Data Pipeline Orchestration	18%	- Transformation tools selection 1. Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, Dataform - Data transformation concepts 1. Data transformation requirements 2. ETL vs ELT processes - Pipeline automation and scheduling 1. Schedule and monitor data jobs 2. Error handling and monitoring

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Google Associate Data Practitioner Sample Questions (Q21-Q26):

NEW QUESTION # 21
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. Copy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.
D. 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.

Answer: C

NEW QUESTION # 22
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 five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue 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. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.
D. Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.

Answer: B

NEW QUESTION # 23
You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of dat a. What should you do?

A. Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
B. Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
C. Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.
D. Use Dataflow to create a streaming pipeline that includes validation and transformation steps.

Answer: D

NEW QUESTION # 24
You work for a healthcare company. You have a daily ETL pipeline that extracts patient data from a legacy system, transforms it, and loads it into BigQuery for analysis. The pipeline currently runs manually using a shell script. You want to automate this process and add monitoring to ensure pipeline observability and troubleshooting insights. You want one centralized solution, using open-source tooling, without rewriting the ETL code. What should you do?

A. Use Cloud Scheduler to trigger a Dataproc job to execute the pipeline daily. Monitor the job's progress using the Dataproc job web interface and Cloud Monitoring.
B. Create a Cloud Run function that runs the pipeline daily. Monitor the functions execution using Cloud Monitoring.
C. Create a direct acyclic graph (DAG) in Cloud Composer to orchestrate a pipeline trigger daily. Monitor the pipeline's execution using the Apache Airflow web interface and Cloud Monitoring.
D. Configure Cloud Dataflow to implement the ETL pipeline, and use Cloud Scheduler to trigger the Dataflow pipeline daily. Monitor the pipelines execution using the Dataflow job monitoring interface and Cloud Monitoring.

Answer: C

NEW QUESTION # 25
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. Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.
D. Develop a batch process using Dataflow that runs weekly and deletes files based on their age.

Answer: C

NEW QUESTION # 26
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