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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Data Analysis and Presentation	27%	- 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 - Data exploration and analysis 1. BigQuery SQL queries 2. Identify trends, patterns, insights 3. Jupyter notebooks for analysis
Data Management and Governance	25%	- Compliance and governance 1. Data stewardship and cataloging 2. Data privacy and regulatory requirements - Data security and access control 1. IAM roles and permissions 2. Data encryption and protection - Data quality and maintenance 1. Data lifecycle management 2. Data validation and cleaning
Data Preparation and Ingestion	30%	- Data formats and classification 1. Structured, semi-structured, unstructured data 2. Formats: CSV, JSON, Parquet, Avro, database tables - Data loading methods 1. Batch and streaming ingestion 2. gcloud, BQ CLI, client libraries - Data extraction and transfer tools 1. Dataflow, BigQuery Data Transfer Service, Database Migration Service 2. Cloud Data Fusion, Storage Transfer Service - Storage solutions selection 1. Storage location types: regional, dual-regional, multi-regional, zonal 2. Cloud Storage, BigQuery, Cloud SQL, Firestore, Bigtable, Spanner
Data Pipeline Orchestration	18%	- Data transformation concepts 1. ETL vs ELT processes 2. Data transformation requirements - 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

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Google Associate Data Practitioner Sample Questions (Q24-Q29):

NEW QUESTION # 24
You need to create a weekly aggregated sales report based on a large volume of data. You want to use Python to design an efficient process for generating this report. What should you do?

A. Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
B. Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
C. Create a Cloud Data Fusion and Wrangler flow. Schedule the flow to run once a week.
D. Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.

Answer: B

NEW QUESTION # 25
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. Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.
C. Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.
D. Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.

Answer: A

NEW QUESTION # 26
Your company uses Looker to visualize and analyze sales dat
a. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?

A. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
B. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
C. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
D. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.

Answer: A

NEW QUESTION # 27
Your company is building a near real-time streaming pipeline to process JSON telemetry data from small appliances. You need to process messages arriving at a Pub/Sub topic, capitalize letters in the serial number field, and write results to BigQuery. You want to use a managed service and write a minimal amount of code for underlying transformations. What should you do?

A. Use a Pub/Sub to BigQuery subscription, write results directly to BigQuery, and schedule a transformation query to run every five minutes.
B. Use a Pub/Sub to Cloud Storage subscription, write a Cloud Run service that is triggered when objects arrive in the bucket, performs the transformations, and writes the results to BigQuery.
C. Use the "Pub/Sub to BigQuery" Dataflow template with a UDF, and write the results to BigQuery.
D. Use a Pub/Sub push subscription, write a Cloud Run service that accepts the messages, performs the transformations, and writes the results to BigQuery.

Answer: C

NEW QUESTION # 28
Your organization's website uses an on-premises MySQL as a backend database. You need to migrate the on-premises MySQL database to Google Cloud while maintaining MySQL features. You want to minimize administrative overhead and downtime. What should you do?

A. Install MySQL on a Compute Engine virtual machine. Export the database files using the mysqldump command. Upload the files to Cloud Storage, and import them into the MySQL instance on Compute Engine.
B. Use Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.
C. Use a Google-provided Dataflow template to replicate the MySQL database in BigQuery.
D. Export the database tables to CSV files, and upload the files to Cloud Storage. Convert the MySQL schema to a Spanner schema, create a JSON manifest file, and run a Google-provided Dataflow template to load the data into Spanner.

Answer: B

NEW QUESTION # 29
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