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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Pipeline Orchestration	18%	- Pipeline automation and scheduling 1. Error handling and monitoring 2. Schedule and monitor data jobs - Data transformation concepts 1. ETL vs ELT processes 2. Data transformation requirements - Transformation tools selection 1. Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, Dataform
Topic 2: 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 stewardship and cataloging 2. Data privacy and regulatory requirements - Data quality and maintenance 1. Data lifecycle management 2. Data validation and cleaning
Topic 3: 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. Jupyter notebooks for analysis 3. Identify trends, patterns, insights - Business intelligence and decision support 1. Communicate findings effectively 2. Answer business questions with data
Topic 4: Data Preparation and Ingestion	30%	- Data formats and classification 1. Structured, semi-structured, unstructured data 2. Formats: CSV, JSON, Parquet, Avro, database tables - Storage solutions selection 1. Storage location types: regional, dual-regional, multi-regional, zonal 2. Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner - 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. Batch and streaming ingestion 2. gcloud, BQ CLI, client libraries

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Google Associate Data Practitioner Sample Questions (Q40-Q45):

NEW QUESTION # 40
Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse.
You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?

A. Create BigQuery scheduled queries to define the transformations in SQL.
B. Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
C. Use Dataform to define the transformations in SQLX.
D. Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.

Answer: C

NEW QUESTION # 41
Your company currently uses an on-premises network file system (NFS) and is migrating data to Google Cloud. You want to be able to control how much bandwidth is used by the data migration while capturing detailed reporting on the migration status. What should you do?

A. Use Cloud Storage FUSE.
B. Use Storage Transfer Service.
C. Use gcloud storage commands.
D. Use a Transfer Appliance.

Answer: B

NEW QUESTION # 42
You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rows and transactional dat a. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?

A. Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.
B. Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.
C. Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.
D. Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.

Answer: C

NEW QUESTION # 43
You need to transfer approximately 300 TB of data from your company's on-premises data center to Cloud Storage. You have 100 Mbps internet bandwidth, and the transfer needs to be completed as quickly as possible. What should you do?

A. Use the gcloud storage command to transfer the data over the internet.
B. Compress the data, upload it to multiple cloud storage providers, and then transfer the data to CloudStorage.
C. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.
D. Use Cloud Client Libraries to transfer the data over the internet.

Answer: C

NEW QUESTION # 44
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. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.
C. Create a Cloud Composer pipeline to load new files from Cloud Storage to BigQuery and schedule it to run every 10 minutes.
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 # 45
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