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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Data Analysis and Presentation	27%	- Data exploration and analysis 1. BigQuery SQL queries 2. Jupyter notebooks for analysis 3. Identify trends, patterns, insights - 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%	- 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. ETL vs ELT processes 2. Data transformation requirements
Data Preparation and Ingestion	30%	- 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 - Storage solutions selection 1. Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner 2. Storage location types: regional, dual-regional, multi-regional, zonal - Data formats and classification 1. Formats: CSV, JSON, Parquet, Avro, database tables 2. Structured, semi-structured, unstructured data
Data Management and Governance	25%	- Data quality and maintenance 1. Data validation and cleaning 2. Data lifecycle management - Data security and access control 1. Data encryption and protection 2. IAM roles and permissions - Compliance and governance 1. Data privacy and regulatory requirements 2. Data stewardship and cataloging

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Google Associate Data Practitioner Sample Questions (Q23-Q28):

NEW QUESTION # 23
Your organization uses scheduled queries to perform transformations on data stored in BigQuery. You discover that one of your scheduled queries has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

A. Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.
B. Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.
C. Navigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.
D. Set up a log sink using the gcloud CLI to export BigQuery audit logs to BigQuery. Query those logs to identify the error associated with the failed job I

Answer: A

NEW QUESTION # 24
You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team's existing skillset, processes data at scale, and minimizes cost. What should you do?

A. Use Dataproc to run the transformations on a cluster.
B. Use Cloud Data Fusion to visually design and manage the pipeline.
C. Use Dataform to define the transformations in SQLX.
D. Use Dataflow with a custom template for the transformation logic.

Answer: A

NEW QUESTION # 25
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. Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.
B. Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.
C. Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.
D. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.

Answer: C

NEW QUESTION # 26
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. Create a separate LookML model for each stakeholder with predefined filters, and schedule the dashboards using the Looker Scheduler.
B. Create a script using the Looker Python SDK, and configure user attribute filter values. Generate a new scheduled plan for each stakeholder.
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. 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.

Answer: D

NEW QUESTION # 27
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 # 28
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