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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Data Pipeline Orchestration	18%	- Pipeline design and automation 1. Workflow orchestration concepts (e.g., Cloud Composer) 2. Dataflow pipelines
Data Management	25%	- Data governance and security 1. Data lifecycle management 2. IAM and access control - Storage and data organization 1. Cloud Storage lifecycle policies 2. BigQuery datasets and tables management
Data Analysis and Presentation	27%	- Data visualization 1. Looker / Looker Studio reporting - Querying and analyzing data 1. Data aggregation and transformation 2. BigQuery SQL usage
Data Preparation and Ingestion	30%	- Data ingestion into Google Cloud services 1. Streaming ingestion with Pub/Sub concepts 2. Cloud Storage ingestion patterns

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PrepAwayETE also offers the ADP web-based practice exam with the same characteristics as desktop simulation software but with minor differences. It is online Google Certification Exam which is accessible from any location with an active internet connection. This Associate Data Practitioner ADP Practice Exam not only works on Windows but also on Linux, Mac, Android, and iOS. Additionally, you can attempt the OMG ADP practice test through these browsers: Opera, Safari, Firefox, Chrome, MS Edge, and Internet Explorer.

Google Associate Data Practitioner Sample Questions (Q58-Q63):

NEW QUESTION # 58
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 confusion matrix.
B. Evaluate the data skewness.
C. Evaluate data drift.
D. Compare the two different models.

Answer: C

NEW QUESTION # 59
Your company is migrating their batch transformation pipelines to Google Cloud. You need to choose a solution that supports programmatic transformations using only SQL. You also want the technology to support Git integration for version control of your pipelines. What should you do?

A. Use Cloud Data Fusion pipelines.
B. Use Dataflow pipelines.
C. Use Dataform workflows.
D. Use Cloud Composer operators.

Answer: C

NEW QUESTION # 60
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 Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
B. Create a Cloud Data Fusion and Wrangler flow. Schedule the flow 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: A

NEW QUESTION # 61
You work for a home insurance company. You are frequently asked to create and save risk reports with charts for specific areas using a publicly available storm event dataset. You want to be able to quickly create and re- run risk reports when new data becomes available. What should you do?

A. Reference and query the storm event dataset using SQL in a Colab Enterprise notebook. Display the table results and document with Markdown, and use Matplotlib to create charts.
B. Reference and query the storm event dataset using SQL in BigQuery Studio. Export the results to Google Sheets, and use cell data in the worksheets to create charts.
C. Copy the storm event dataset into your BigQuery project. Use BigQuery Studio to query and visualize the data in Looker Studio.
D. Export the storm event dataset as a CSV file. Import the file to Google Sheets, and use cell data in the worksheets to create charts.

Answer: C

NEW QUESTION # 62
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 Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.
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 a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.

Answer: B

NEW QUESTION # 63
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