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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q57-Q62):

NEW QUESTION # 57
You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?

A. Use Vertex Al Workbench to develop a custom model for purchase prediction.
B. Use BigQuery ML to create a logistic regression model for purchase prediction.
C. Use Colab Enterprise to develop a custom model for purchase prediction.
D. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.

Answer: B

NEW QUESTION # 58
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. Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
B. Use Cloud Run functions to transform and load 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 Dataflow to transform the data and write the transformed data to BigQuery.

Answer: D

NEW QUESTION # 59
You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage.
The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery. Which data manipulation methodology should you choose?

A. EL
B. ETLT
C. ELT
D. ETL

Answer: D

NEW QUESTION # 60
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 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.
B. Use a Pub/Sub push subscription, write a Cloud Run service that accepts the messages, performs the transformations, and writes the results to BigQuery.
C. Use a Pub/Sub to BigQuery subscription, write results directly to BigQuery, and schedule a transformation query to run every five minutes.
D. Use the "Pub/Sub to BigQuery" Dataflow template with a UDF, and write the results to BigQuery.

Answer: D

NEW QUESTION # 61
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. Create a Dataproc cluster to pull CSV files from Cloud Storage, process them using Spark, and write the results to BigQuery.
D. Use the bq command-line tool within a Cloud Shell instance to load the data into BigQuery.

Answer: A

NEW QUESTION # 62
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