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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Data Preparation and Ingestion	30%	- Data loading methods 1. gcloud, BQ CLI, client libraries 2. Batch and streaming ingestion - Storage solutions selection 1. Storage location types: regional, dual-regional, multi-regional, zonal 2. Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner - Data formats and classification 1. Structured, semi-structured, unstructured data 2. Formats: CSV, JSON, Parquet, Avro, database tables - Data extraction and transfer tools 1. Dataflow, BigQuery Data Transfer Service, Database Migration Service 2. Cloud Data Fusion, Storage Transfer Service
Data Management and Governance	25%	- Data quality and maintenance 1. Data lifecycle management 2. Data validation and cleaning - 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 Pipeline Orchestration	18%	- Transformation tools selection 1. Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, Dataform - Pipeline automation and scheduling 1. Error handling and monitoring 2. Schedule and monitor data jobs - Data transformation concepts 1. Data transformation requirements 2. ETL vs ELT processes
Data Analysis and Presentation	27%	- Data exploration and analysis 1. BigQuery SQL queries 2. Jupyter notebooks for analysis 3. Identify trends, patterns, insights - 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

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Google Associate Data Practitioner Sample Questions (Q39-Q44):

NEW QUESTION # 39
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 Dataflow to transform the data and write the transformed data to BigQuery.
B. Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
C. Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
D. Use Cloud Run functions to transform and load the data into BigQuery.

Answer: A

NEW QUESTION # 40
Your company stores historical data in Cloud Storage. You need to ensure that all data is saved in a bucket for at least three years. What should you do?

A. Set a bucket retention policy.
B. Set temporary object holds.
C. Change the bucket storage class to Archive.
D. Enable Object Versioning.

Answer: A

NEW QUESTION # 41
You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?

A. Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
B. Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.
C. Use Cloud Run functions to trigger the Spark job and email the report.
D. Use Cloud Composer to orchestrate the Spark job and email the report.

Answer: B

NEW QUESTION # 42
You manage a large amount of data in Cloud Storage, including raw data, processed data, and backups. Your organization is subject to strict compliance regulations that mandate data immutability for specific data types.
You want to use an efficient process to reduce storage costs while ensuring that your storage strategy meets retention requirements. What should you do?

A. Move objects to different storage classes based on their age and access patterns. Use Cloud Key Management Service (Cloud KMS) to encrypt specific objects with customer-managed encryption keys (CMEK) to meet immutability requirements.
B. Use object holds to enforce immutability for specific objects, and configure lifecycle management rules to transition objects to appropriate storage classes based on age and access patterns.
C. Configure lifecycle management rules to transition objects to appropriate storage classes based on access patterns. Set up Object Versioning for all objects to meet immutability requirements.
D. Create a Cloud Run function to periodically check object metadata, and move objects to the appropriate storage class based on age and access patterns. Use object holds to enforce immutability for specific objects.

Answer: B

NEW QUESTION # 43
You have a BigQuery dataset containing sales dat
a. This data is actively queried for the first 6 months. After that, the data is not queried but needs to be retained for 3 years for compliance reasons. You need to implement a data management strategy that meets access and compliance requirements, while keeping cost and administrative overhead to a minimum. What should you do?

A. Store all data in a single BigQuery table without partitioning or lifecycle policies.
B. Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.
C. Partition a BigQuery table by month. After 6 months, export the data to Coldline storage. Implement a lifecycle policy to delete the data from Cloud Storage after 3 years.
D. Set up a scheduled query to export the data to Cloud Storage after 6 months. Write a stored procedure to delete the data from BigQuery after 3 years.

Answer: C

NEW QUESTION # 44
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