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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q22-Q27):

NEW QUESTION # 22
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 table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.
B. 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.
C. Use authorized views to share query results with the payroll specialist.
D. 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.

Answer: C

NEW QUESTION # 23
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 # 24
You are predicting customer churn for a subscription-based service. You have a 50 PB historical customer dataset in BigQuery that includes demographics, subscription information, and engagement metrics. You want to build a churn prediction model with minimal overhead. You want to follow the Google-recommended approach. What should you do?

A. Use the BigQuery Python client library in a Jupyter notebook to query and preprocess the data in BigQuery. Use the CREATE MODEL statement in BigQueryML to train the churn prediction model.
B. Create a Looker dashboard that is connected to BigQuery. Use LookML to predict churn.
C. Use Dataproc to create a Spark cluster. Use the Spark MLlib within the cluster to build the churn prediction model.
D. Export the data from BigQuery to a local machine. Use scikit- learn in a Jupyter notebook to build the churn prediction model.

Answer: A

NEW QUESTION # 25
Your company currently uses an on-premises network file system (NFS) and is migrating data to Google Cloud. You want to be able to control how much bandwidth is used by the data migration while capturing detailed reporting on the migration status. What should you do?

A. Use Cloud Storage FUSE.
B. Use Storage Transfer Service.
C. Use a Transfer Appliance.
D. Use gcloud storage commands.

Answer: B

NEW QUESTION # 26
You need to create a new data pipeline. You want a serverless solution that meets the following requirements:
* Data is streamed from Pub/Sub and is processed in real-time.
* Data is transformed before being stored.
* Data is stored in a location that will allow it to be analyzed with SQL using Looker.
Which Google Cloud services should you recommend for the pipeline?

A. Cloud Composer Cloud SQL for MySQL
B. Dataproc Serverless Bigtable
C. BigQuery Analytics Hub
D. Dataflow BigQuery

Answer: D

NEW QUESTION # 27
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