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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation and Exploration	20-30%	- Identify data quality issues - Ingest and acquire data - Explore data through visualization and queries - Transform and prepare data for analysis - Perform exploratory data analysis (EDA)
Topic 2: Data Processing and Analytics	20-30%	- Build and maintain data pipelines - Apply statistical methods for analysis - Use BigQuery and SQL for analytics - Aggregate and summarize data - Query and analyze datasets
Topic 3: Data-Driven Decision Making	10-20%	- Identify stakeholders and requirements - Assess data quality and completeness - Define success metrics - Translate business requirements into data solutions
Topic 4: Data Visualization and Insights	20-30%	- Interpret and communicate findings - Build visualizations using Looker Studio - Choose appropriate visualization types - Present data insights to stakeholders - Create dashboards and reports

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Google Associate Data Practitioner Sample Questions (Q59-Q64):

NEW QUESTION # 59
Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive dat a. What should you do?

A. Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.
B. Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.
C. Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.
D. Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.

Answer: D

NEW QUESTION # 60
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. Use Cloud Data Fusion to transfer the data into BigQuery raw tables, and use SQL to transform it.
B. Manually download the data from Cloud Storage. Use a Python script to transform and upload the data into BigQuery.
C. Use Dataflow to transform the data and write the transformed data to BigQuery.
D. Use Cloud Run functions to transform and load the data into BigQuery.

Answer: C

NEW QUESTION # 61
You are working on a data pipeline that will validate and clean incoming data before loading it into BigQuery for real-time analysis. You want to ensure that the data validation and cleaning is performed efficiently and can handle high volumes of dat a. What should you do?

A. Write custom scripts in Python to validate and clean the data outside of Google Cloud. Load the cleaned data into BigQuery.
B. Load the raw data into BigQuery using Cloud Storage as a staging area, and use SQL queries in BigQuery to validate and clean the data.
C. Use Cloud Run functions to trigger data validation and cleaning routines when new data arrives in Cloud Storage.
D. Use Dataflow to create a streaming pipeline that includes validation and transformation steps.

Answer: D

NEW QUESTION # 62
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 Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.
C. 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.
D. Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.

Answer: B

NEW QUESTION # 63
You are designing a BigQuery data warehouse with a team of experienced SQL developers. You need to recommend a cost- effective, fully-managed, serverless solution to build ELT processes with SQL pipelines.
Your solution must include source code control, environment parameterization, and data quality checks. What should you do?

A. Use Cloud Data Fusion to visually design and manage the pipelines.
B. Use Dataproc to run MapReduce jobs for distributed data processing.
C. Use Dataform to build, orchestrate, and monitor the pipelines.
D. Use Cloud Composer to orchestrate and run data workflows.

Answer: C

NEW QUESTION # 64
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