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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q16-Q21):

NEW QUESTION # 16
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. 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.
D. Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.

Answer: A

NEW QUESTION # 17
Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards.
You need to implement a solution quickly and efficiently. What should you do?

A. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
B. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
C. Apply a filter to only show products with a positive profit margin.
D. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).

Answer: B

NEW QUESTION # 18
Your organization has decided to move their on-premises Apache Spark-based workload to Google Cloud.
You want to be able to manage the code without needing to provision and manage your own cluster. What should you do?

A. Configure a Google Kubernetes Engine cluster with Spark operators, and deploy the Spark jobs.
B. Migrate the Spark jobs to Dataproc on Google Kubernetes Engine.
C. Migrate the Spark jobs to Dataproc on Compute Engine.
D. Migrate the Spark jobs to Dataproc Serverless.

Answer: D

NEW QUESTION # 19
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 VPN. Use Storage Transfer Service to move the data to Cloud Storage.
B. Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.
C. Connect to Google Cloud using Dedicated Interconnect. 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 # 20
Your organization is conducting analysis on regional sales metrics. Data from each regional sales team is stored as separate tables in BigQuery and updated monthly. You need to create a solution that identifies the top three regions with the highest monthly sales for the next three months. You want the solution to automatically provide up-to-date results. What should you do?

A. Create a BigQuery table that performs a cross join across all of the regional sales tables. Use the rank() window function to query the new table.
B. Create a BigQuery table that performs a union across all of the regional sales tables. Use the row_number() window function to query the new table.
C. Create a BigQuery materialized view that performs a cross join across all of the regional sales tables. Use the row_number() window function to query the new materialized view.
D. Create a BigQuery materialized view that performs a union across all of the regional sales tables. Use the rank() window function to query the new materialized view.

Answer: D

NEW QUESTION # 21
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