ADP Exam Objectives | ADP Exam Bootcamp

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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Data Management and Governance	25%	- Compliance and governance 1. Data privacy and regulatory requirements 2. Data stewardship and cataloging - Data quality and maintenance 1. Data lifecycle management 2. Data validation and cleaning - Data security and access control 1. Data encryption and protection 2. IAM roles and permissions
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 visualization and reporting 1. Visualization best practices 2. Looker dashboards and reports - Business intelligence and decision support 1. Communicate findings effectively 2. Answer business questions with data - Data exploration and analysis 1. Identify trends, patterns, insights 2. BigQuery SQL queries 3. Jupyter notebooks for analysis
Data Preparation and Ingestion	30%	- Data loading methods 1. Batch and streaming ingestion 2. gcloud, BQ CLI, client libraries - Data extraction and transfer tools 1. Cloud Data Fusion, Storage Transfer Service 2. Dataflow, BigQuery Data Transfer Service, Database Migration Service - 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

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Google Associate Data Practitioner Sample Questions (Q22-Q27):

NEW QUESTION # 22
Your company currently uses an on-premises network file system (NFS) and is migrating data to Google Cloud. You want to be able to control how much bandwidth is used by the data migration while capturing detailed reporting on the migration status. What should you do?

A. Use Cloud Storage FUSE.
B. Use gcloud storage commands.
C. Use Storage Transfer Service.
D. Use a Transfer Appliance.

Answer: C

NEW QUESTION # 23
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_uype='logisric_reg') AS SELECT * from cusromer_data;
B. CREATE OR REPLACE MODEL churn_prediction_model options(model_type='logistic_reg*) as select ' except(churned) FROM customer data;
C. CREATE OR REPLACE MODEL churn_prediction_model OPTIONS (rr.odel_type=' logisric_reg *) AS select * except(churned), churned AS label FROM customer_data;
D. CREATE OR REPLACE MODEL churn_prediction_model options (model type='logistic_reg') AS select churned as label FROM customer_data;

Answer: C

NEW QUESTION # 24
Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse.
You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?

A. Use Dataform to define the transformations in SQLX.
B. Use Dataproc to create an Apache Spark cluster and implement the transformations by using PySpark SQL.
C. Create BigQuery scheduled queries to define the transformations in SQL.
D. Create a Cloud Composer environment, and orchestrate the transformations by using the BigQueryinsertJob operator.

Answer: A

NEW QUESTION # 25
You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?

A. Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.
B. Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.
C. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
D. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.

Answer: C

NEW QUESTION # 26
You manage data at an ecommerce company. You have a Dataflow pipeline that processes order data from Pub/Sub, enriches the data with product information from Bigtable, and writes the processed data to BigQuery for analysis. The pipeline runs continuously and processes thousands of orders every minute. You need to monitor the pipeline's performance and be alerted if errors occur. What should you do?

A. Use BigQuery to analyze the processed data in Cloud Storage and identify anomalies or inconsistencies. Set up scheduled alerts based when anomalies or inconsistencies occur.
B. Use Cloud Logging to view the pipeline logs and check for errors. Set up alerts based on specific keywords in the logs.
C. Use Cloud Monitoring to track key metrics. Create alerting policies in Cloud Monitoring to trigger notifications when metrics exceed thresholds or when errors occur.
D. Use the Dataflow job monitoring interface to visually inspect the pipeline graph, check for errors, and configure notifications when critical errors occur.

Answer: C

NEW QUESTION # 27
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