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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q25-Q30):

NEW QUESTION # 25
Your organization has a BigQuery dataset that contains sensitive employee information such as salaries and performance reviews. The payroll specialist in the HR department needs to have continuous access to aggregated performance data, but they do not need continuous access to other sensitive dat a. You need to grant the payroll specialist access to the performance data without granting them access to the entire dataset using the simplest and most secure approach. What should you do?

A. Create a SQL query with the aggregated performance data. Export the results to an Avro file in a Cloud Storage bucket. Share the bucket with the payroll specialist.
B. Use authorized views to share query results with the payroll specialist.
C. Create a table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.
D. Create row-level and column-level permissions and policies on the table that contains performance data in the dataset. Provide the payroll specialist with the appropriate permission set.

Answer: B

NEW QUESTION # 26
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 Colab Enterprise notebook and use the bigframes.pandas library. Schedule the notebook to execute once a week.
C. Create a Dataflow directed acyclic graph (DAG) coded in Python. Use Cloud Scheduler to schedule the code to run once a week.
D. Create a Cloud Run function that uses NumPy. Use Cloud Scheduler to schedule the function to run once a week.

Answer: C

NEW QUESTION # 27
You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?

A. Create asales_regionuser attribute, and assign each manager's region as the value of their user attribute.
B. Add anaccess_filterExplore filter on theregion_namedimension by using thesales_regionuser attribute.
C. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.
D. Create five different Explores with thesql_always_filterExplore filter applied on theregion_namedimension. Set eachregion_namevalue to the corresponding region for each manager.

Answer: A

NEW QUESTION # 28
Your company uses Looker to visualize and analyze sales dat
a. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?

A. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
B. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
C. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
D. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.

Answer: C

NEW QUESTION # 29
Your company stores historical data in Cloud Storage. You need to ensure that all data is saved in a bucket for at least three years. What should you do?

A. Set a bucket retention policy.
B. Enable Object Versioning.
C. Set temporary object holds.
D. Change the bucket storage class to Archive.

Answer: A

NEW QUESTION # 30
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