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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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
Topic 2: Data Preparation and Ingestion	30%	- Data extraction and transfer tools 1. Cloud Data Fusion, Storage Transfer Service 2. Dataflow, BigQuery Data Transfer Service, Database Migration Service - 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 loading methods 1. gcloud, BQ CLI, client libraries 2. Batch and streaming ingestion
Topic 3: Data Pipeline Orchestration	18%	- Data transformation concepts 1. Data transformation requirements 2. ETL vs ELT processes - Transformation tools selection 1. Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, Dataform - Pipeline automation and scheduling 1. Schedule and monitor data jobs 2. Error handling and monitoring
Topic 4: Data Analysis and Presentation	27%	- Data exploration and analysis 1. Jupyter notebooks for analysis 2. Identify trends, patterns, insights 3. BigQuery SQL queries - Data visualization and reporting 1. Looker dashboards and reports 2. Visualization best practices - Business intelligence and decision support 1. Answer business questions with data 2. Communicate findings effectively

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Google Associate Data Practitioner Sample Questions (Q16-Q21):

NEW QUESTION # 16
Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards.
You need to implement a solution quickly and efficiently. What should you do?

A. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
B. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
C. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
D. Apply a filter to only show products with a positive profit margin.

Answer: A

NEW QUESTION # 17
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. 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.
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. Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.

Answer: C

NEW QUESTION # 18
Following a recent company acquisition, you inherited an on- premises data infrastructure that needs to move to Google Cloud. The acquired system has 250 Apache Airflow directed acyclic graphs (DAGs) orchestrating data pipelines. You need to migrate the pipelines to a Google Cloud managed service with minimal effort. What should you do?

A. Create a new Cloud Composer environment and copy DAGS to the Cloud Composer dags/folder.
B. Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.
C. Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.
D. Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.

Answer: A

NEW QUESTION # 19
You are a data analyst working with sensitive customer data in BigQuery. You need to ensure that only authorized personnel within your organization can query this data, while following the principle of least privilege. What should you do?

A. Export the data to Cloud Storage, and use signed URLs to authorize access.
B. Update dataset privileges by using the SQL GRANT statement.
C. Enable access control by using IAM roles.
D. Encrypt the data by using customer-managed encryption keys (CMEK).

Answer: C

NEW QUESTION # 20
Your organization needs to store historical customer order dat
a. The data will only be accessed once a month for analysis and must be readily available within a few seconds when it is accessed. You need to choose a storage class that minimizes storage costs while ensuring that the data can be retrieved quickly. What should you do?

A. Store the data in Cloud Storage using Standard storage.
B. Store the data in Cloud Storage using Coldline storage.
C. Store the data in Cloud Storage using Nearline storage.
D. Store the data in Cloud Storage using Archive storage.

Answer: C

NEW QUESTION # 21
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