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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation and Ingestion	30%	- Storage solutions selection 1. Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner 2. Storage location types: regional, dual-regional, multi-regional, zonal - Data extraction and transfer tools 1. Cloud Data Fusion, Storage Transfer Service 2. Dataflow, BigQuery Data Transfer Service, Database Migration Service - Data loading methods 1. gcloud, BQ CLI, client libraries 2. Batch and streaming ingestion - Data formats and classification 1. Formats: CSV, JSON, Parquet, Avro, database tables 2. Structured, semi-structured, unstructured data
Topic 2: Data Analysis and Presentation	27%	- Data visualization and reporting 1. Looker dashboards and reports 2. Visualization best practices - Data exploration and analysis 1. BigQuery SQL queries 2. Identify trends, patterns, insights 3. Jupyter notebooks for analysis - Business intelligence and decision support 1. Answer business questions with data 2. Communicate findings effectively
Topic 3: Data Pipeline Orchestration	18%	- 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 - Transformation tools selection 1. Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, Dataform
Topic 4: Data Management and Governance	25%	- 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 quality and maintenance 1. Data lifecycle management 2. Data validation and cleaning

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

NEW QUESTION # 23
You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?

A. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
B. Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.
C. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.
D. Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.

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

Answer: A

NEW QUESTION # 25
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 cross join across all of the regional sales tables. Use the rank() window function to query the new table.
B. 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.
C. 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.
D. 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.

Answer: B

NEW QUESTION # 26
You are developing a data ingestion pipeline to load small CSV files into BigQuery from Cloud Storage. You want to load these files upon arrival to minimize data latency. You want to accomplish this with minimal cost and maintenance. What should you do?

A. Create a Cloud Run function to load the data into BigQuery that is triggered when data arrives in Cloud Storage.
B. Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
C. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
D. Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.

Answer: A

NEW QUESTION # 27
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 Cloud Composer operators.
D. Use Dataform workflows.

Answer: D

NEW QUESTION # 28
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