Google - Unparalleled ADP - Associate Data Practitioner Exam Objectives

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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q36-Q41):

NEW QUESTION # 36
You are working on a project that requires analyzing daily social media dat a. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing.
You need to transform and load this data into BigQuery for analysis. You want to follow the Google-recommended approach. What should you do?

A. Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
B. Use Cloud Run functions to transform and load the data into BigQuery.
C. Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
D. Use Dataflow to transform the data and write the transformed data to BigQuery.

Answer: D

NEW QUESTION # 37
You recently inherited a task for managing Dataflow streaming pipelines in your organization and noticed that proper access had not been provisioned to you. You need to request a Google-provided IAM role so you can restart the pipelines. You need to follow the principle of least privilege. What should you do?

A. Request the Dataflow Admin role.
B. Request the Dataflow Developer role.
C. Request the Dataflow Viewer role.
D. Request the Dataflow Worker role.

Answer: B

NEW QUESTION # 38
Following a recent company acquisition, you inherited an on- premises data infrastructure that needs to move to Google Cloud. The acquired system has 250 Apache Airflow directed acyclic graphs (DAGs) orchestrating data pipelines. You need to migrate the pipelines to a Google Cloud managed service with minimal effort. What should you do?

A. Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.
B. Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.
C. Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.
D. Create a new Cloud Composer environment and copy DAGS to the Cloud Composer dags/folder.

Answer: D

NEW QUESTION # 39
You have a BigQuery dataset containing sales dat
a. This data is actively queried for the first 6 months. After that, the data is not queried but needs to be retained for 3 years for compliance reasons. You need to implement a data management strategy that meets access and compliance requirements, while keeping cost and administrative overhead to a minimum. What should you do?

A. Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.
B. Set up a scheduled query to export the data to Cloud Storage after 6 months. Write a stored procedure to delete the data from BigQuery after 3 years.
C. Partition a BigQuery table by month. After 6 months, export the data to Coldline storage. Implement a lifecycle policy to delete the data from Cloud Storage after 3 years.
D. Store all data in a single BigQuery table without partitioning or lifecycle policies.

Answer: C

NEW QUESTION # 40
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 union across all of the regional sales tables. Use the row_number() window function to query the new table.
B. 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.
C. 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.
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 # 41
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