ADP Valid Study Questions & ADP Valid Exam Forum

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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Data-Driven Decision Making	10-20%	- Define success metrics - Identify stakeholders and requirements - Assess data quality and completeness - Translate business requirements into data solutions
Data Processing and Analytics	20-30%	- Aggregate and summarize data - Apply statistical methods for analysis - Build and maintain data pipelines - Use BigQuery and SQL for analytics - Query and analyze datasets
Data Preparation and Exploration	20-30%	- Transform and prepare data for analysis - Explore data through visualization and queries - Ingest and acquire data - Identify data quality issues - Perform exploratory data analysis (EDA)
Data Visualization and Insights	20-30%	- Choose appropriate visualization types - Interpret and communicate findings - Build visualizations using Looker Studio - Present data insights to stakeholders - Create dashboards and reports

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Google Associate Data Practitioner Sample Questions (Q10-Q15):

NEW QUESTION # 10
Your organization has several datasets in BigQuery. The datasets need to be shared with your external partners so that they can run SQL queries without needing to copy the data to their own projects. You have organized each partner's data in its own BigQuery dataset. Each partner should be able to access only their dat a. You want to share the data while following Google-recommended practices. What should you do?

A. Use Analytics Hub to create a listing on a private data exchange for each partner dataset. Allow each partner to subscribe to their respective listings.
B. Grant the partners the bigquery.user IAM role on the BigQuery project.
C. Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.
D. Create a Dataflow job that reads from each BigQuery dataset and pushes the data into a dedicated Pub /Sub topic for each partner. Grant each partner the pubsub. subscriber IAM role.

Answer: A

NEW QUESTION # 11
Your organization plans to move their on-premises environment to Google Cloud. Your organization's network bandwidth is less than 1 Gbps. You need to move over 500 ## of data to Cloud Storage securely, and only have a few days to move the dat a. What should you do?

A. Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.
B. Connect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage.
C. Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.
D. Request multiple Transfer Appliances, copy the data to the appliances, and ship the appliances back to Google Cloud to upload the data to Cloud Storage.

Answer: D

NEW QUESTION # 12
Your team wants to create a monthly report to analyze inventory data that is updated daily. You need to aggregate the inventory counts by using only the most recent month of data, and save the results to be used in a Looker Studio dashboard. What should you do?

A. Create a BigQuery table that uses the SUM() function and the _PARTITIONDATE filter.
B. Create a BigQuery table that uses the SUM() function and the DATE_DIFF() function.
C. Create a saved query in the BigQuery console that uses the SUM() function and the DATE_SUB() function. Re-run the saved query every month, and save the results to a BigQuery table.
D. Create a materialized view in BigQuery that uses the SUM() function and the DATE_SUB() function.

Answer: D

NEW QUESTION # 13
Your data science team needs to collaboratively analyze a 25 TB BigQuery dataset to support the development of a machine learning model. You want to use Colab Enterprise notebooks while ensuring efficient data access and minimizing cost. What should you do?

A. Use BigQuery magic commands within a Colab Enterprise notebook to query and analyze the data.
B. Export the BigQuery dataset to Google Drive. Load the dataset into the Colab Enterprise notebook using Pandas.
C. Copy the BigQuery dataset to the local storage of the Colab Enterprise runtime, and analyze the data using Pandas.
D. Create a Dataproc cluster connected to a Colab Enterprise notebook, and use Spark to process the data in BigQuery.

Answer: A

NEW QUESTION # 14
You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rows and transactional dat a. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?

A. Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.
B. Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.
C. Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.
D. Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.

Answer: C

NEW QUESTION # 15
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