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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation and Ingestion	30%	- 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 - Data loading methods 1. gcloud, BQ CLI, client libraries 2. Batch and streaming ingestion - Storage solutions selection 1. Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner 2. Storage location types: regional, dual-regional, multi-regional, zonal
Topic 2: Data Management and Governance	25%	- 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 - Data security and access control 1. IAM roles and permissions 2. Data encryption and protection
Topic 3: Data Analysis and Presentation	27%	- Business intelligence and decision support 1. Answer business questions with data 2. Communicate findings effectively - Data visualization and reporting 1. Visualization best practices 2. Looker dashboards and reports - Data exploration and analysis 1. Identify trends, patterns, insights 2. Jupyter notebooks for analysis 3. BigQuery SQL queries
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 (Q14-Q19):

NEW QUESTION # 14
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 materialized view in BigQuery that uses the SUM() function and the DATE_SUB() function.
B. Create a BigQuery table that uses the SUM() function and the DATE_DIFF() function.
C. 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.
D. Create a BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.

Answer: A

NEW QUESTION # 15
Your company's customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files' metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files. What should you do?

A. Create an object table.
B. Create a temporary table.
C. Create an external table.
D. Create a native table.

Answer: A

NEW QUESTION # 16
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 Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
B. Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
C. Use Cloud Run functions to transform and load the data into BigQuery.
D. Use Dataflow to transform the data and write the transformed data to BigQuery.

Answer: D

NEW QUESTION # 17
You recently inherited a task for managing Dataflow streaming pipelines in your organization and noticed that proper access had not been provisioned to you. You need to request a Google-provided IAM role so you can restart the pipelines. You need to follow the principle of least privilege. What should you do?

A. Request the Dataflow Developer role.
B. Request the Dataflow Worker role.
C. Request the Dataflow Admin role.
D. Request the Dataflow Viewer role.

Answer: A

NEW QUESTION # 18
Your company uses Looker to generate and share reports with various stakeholders. You have a complex dashboard with several visualizations that needs to be delivered to specific stakeholders on a recurring basis, with customized filters applied for each recipient. You need an efficient and scalable solution to automate the delivery of this customized dashboard. You want to follow the Google- recommended approach. What should you do?

A. Use the Looker Scheduler with a user attribute filter on the dashboard, and send the dashboard with personalized filters to each stakeholder based on their attributes.
B. Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.
C. Embed the Looker dashboard in a custom web application, and use the application's scheduling features to send the report with personalized filters.
D. Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.

Answer: A

NEW QUESTION # 19
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