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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q18-Q23):

NEW QUESTION # 18
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 Nearline storage.
B. Store the data in Cloud Storage using Archive storage.
C. Store the data in Cloud Storage using Standard storage.
D. Store the data in Cloud Storage using Coldline storage.

Answer: A

NEW QUESTION # 19
Your retail company wants to predict customer churn using historical purchase data stored in BigQuery. The dataset includes customer demographics, purchase history, and a label indicating whether the customer churned or not. You want to build a machine learning model to identify customers at risk of churning. You need to create and train a logistic regression model for predicting customer churn, using the customer_data table with the churned column as the target label. Which BigQuery ML query should you use?

A. CREATE OR REPLACE MODEL churn_prediction_model options(model_type='logistic_reg*) as select ' except(churned) FROM customer data;
B. CREATE OR REPLACE MODEL churn_prediction_model OPTIONS (rr.odel_type=' logisric_reg *) AS select * except(churned), churned AS label FROM customer_data;
C. CREATE OR REPLACE MODEL churn_prediction_model OPTIONS(model_uype='logisric_reg') AS SELECT * from cusromer_data;
D. CREATE OR REPLACE MODEL churn_prediction_model options (model type='logistic_reg') AS select churned as label FROM customer_data;

Answer: B

NEW QUESTION # 20
Your company has an on-premises file server with 5 TB of data that needs to be migrated to Google Cloud.
The network operations team has mandated that you can only use up to 250 Mbps of the total available bandwidth for the migration. You need to perform an online migration to Cloud Storage. What should you do?

A. Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the --no- clobber option.
B. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.
C. Use Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.
D. Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the -- daisy-chain option.

Answer: C

NEW QUESTION # 21
Your retail company wants to analyze customer reviews to understand sentiment and identify areas for improvement. Your company has a large dataset of customer feedback text stored in BigQuery that includes diverse language patterns, emojis, and slang. You want to build a solution to classify customer sentiment from the feedback text. What should you do?

A. Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.
B. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.
C. Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.
D. Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.

Answer: A

NEW QUESTION # 22
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. Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.
D. Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.

Answer: A

NEW QUESTION # 23
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