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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Pipeline Orchestration	18%	- Pipeline automation and scheduling 1. Schedule and monitor data jobs 2. Error handling and monitoring - Transformation tools selection 1. Dataproc, Dataflow, Cloud Data Fusion, Cloud Composer, Dataform - Data transformation concepts 1. ETL vs ELT processes 2. Data transformation requirements
Topic 2: 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. BigQuery SQL queries 2. Identify trends, patterns, insights 3. Jupyter notebooks for analysis - Data visualization and reporting 1. Looker dashboards and reports 2. Visualization best practices
Topic 3: Data Management and Governance	25%	- Data security and access control 1. Data encryption and protection 2. IAM roles and permissions - 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
Topic 4: 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 loading methods 1. gcloud, BQ CLI, client libraries 2. Batch and streaming ingestion - 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

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Google Associate Data Practitioner Sample Questions (Q23-Q28):

NEW QUESTION # 23
You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of dat a. What should you do?

A. Use Dataflow to create a streaming pipeline that includes validation and transformation steps.
B. Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
C. Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
D. Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.

Answer: A

NEW QUESTION # 24
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 BigQuery subscription in Pub/Sub.
B. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
C. 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.
D. 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.

Answer: B

NEW QUESTION # 25
You manage a Cloud Storage bucket that stores temporary files created during data processing. These temporary files are only needed for seven days, after which they are no longer needed. To reduce storage costs and keep your bucket organized, you want to automatically delete these files once they are older than seven days. What should you do?

A. Set up a Cloud Scheduler job that invokes a weekly Cloud Run function to delete files older than seven days.
B. Configure a Cloud Storage lifecycle rule that automatically deletes objects older than seven days.
C. Create a Cloud Run function that runs daily and deletes files older than seven days.
D. Develop a batch process using Dataflow that runs weekly and deletes files based on their age.

Answer: B

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

Answer: A

NEW QUESTION # 27
Your organization uses a BigQuery table that is partitioned by ingestion time. You need to remove data that is older than one year to reduce your organization's storage costs. You want to use the most efficient approach while minimizing cost. What should you do?

A. Create a view that filters out rows that are older than one year.
B. Set the table partition expiration period to one year using the ALTER TABLE statement in SQL.
C. Create a scheduled query that periodically runs an update statement in SQL that sets the "deleted" column to "yes" for data that is more than one year old. Create a view that filters out rows that have been marked deleted.
D. Require users to specify a partition filter using the alter table statement in SQL.

Answer: B

NEW QUESTION # 28
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