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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Data Pipeline Orchestration	18%	- Data transformation concepts 1. ETL vs ELT processes 2. Data transformation requirements - 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 Preparation and Ingestion	30%	- Data loading methods 1. gcloud, BQ CLI, client libraries 2. Batch and streaming ingestion - 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. 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
Data Management and Governance	25%	- Data security and access control 1. Data encryption and protection 2. IAM roles and permissions - Data quality and maintenance 1. Data lifecycle management 2. Data validation and cleaning - Compliance and governance 1. Data privacy and regulatory requirements 2. Data stewardship and cataloging
Data Analysis and Presentation	27%	- Data visualization and reporting 1. Looker dashboards and reports 2. Visualization best practices - Business intelligence and decision support 1. Answer business questions with data 2. Communicate findings effectively - Data exploration and analysis 1. Identify trends, patterns, insights 2. BigQuery SQL queries 3. Jupyter notebooks for analysis

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Google Associate Data Practitioner Sample Questions (Q28-Q33):

NEW QUESTION # 28
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 Storage Transfer Service to configure an agent-based transfer. Set the appropriate bandwidth limit for the agent pool.
B. Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the --no- clobber option.
C. Use the gcloud storage cp command to copy all files from on- premises to Cloud Storage using the -- daisy-chain option.
D. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google Cloud.

Answer: A

NEW QUESTION # 29
Your organization uses scheduled queries to perform transformations on data stored in BigQuery. You discover that one of your scheduled queries has failed. You need to troubleshoot the issue as quickly as possible. What should you do?

A. Request access from your admin to the BigQuery information_schema. Query the jobs view with the failed job ID, and analyze error details.
B. Set up a log sink using the gcloud CLI to export BigQuery audit logs to BigQuery. Query those logs to identify the error associated with the failed job I
C. Navigate to the Logs Explorer page in Cloud Logging. Use filters to find the failed job, and analyze the error details.
D. Navigate to the Scheduled queries page in the Google Cloud console. Select the failed job, and analyze the error details.

Answer: D

NEW QUESTION # 30
You recently inherited a task for managing Dataflow streaming pipelines in your organization and noticed that proper access had not been provisioned to you. You need to request a Google-provided IAM role so you can restart the pipelines. You need to follow the principle of least privilege. What should you do?

A. Request the Dataflow Admin role.
B. Request the Dataflow Worker role.
C. Request the Dataflow Developer role.
D. Request the Dataflow Viewer role.

Answer: C

NEW QUESTION # 31
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 Cloud Data Fusion and Wrangler flow. Schedule the flow to run 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 Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.
D. Create a Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.

Answer: B

NEW QUESTION # 32
You used BigQuery ML to build a customer purchase propensity model six months ago. You want to compare the current serving data with the historical serving data to determine whether you need to retrain the model.
What should you do?

A. Compare the two different models.
B. Evaluate the data skewness.
C. Evaluate data drift.
D. Compare the confusion matrix.

Answer: C

NEW QUESTION # 33
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