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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q64-Q69):

NEW QUESTION # 64
Your company's customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files' metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files. What should you do?

A. Create a native table.
B. Create an external table.
C. Create a temporary table.
D. Create an object table.

Answer: D

NEW QUESTION # 65
Your organization's ecommerce website collects user activity logs using a Pub/Sub topic. Your organization's leadership team wants a dashboard that contains aggregated user engagement metrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried. What should you do?

A. Create a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.
B. Create an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.
C. Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.
D. Create a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting

Answer: C

NEW QUESTION # 66
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. 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.
B. 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.
C. Store all data in a single BigQuery table without partitioning or lifecycle policies.
D. Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.

Answer: B

NEW QUESTION # 67
Your organization's website uses an on-premises MySQL as a backend database. You need to migrate the on-premises MySQL database to Google Cloud while maintaining MySQL features. You want to minimize administrative overhead and downtime. What should you do?

A. Export the database tables to CSV files, and upload the files to Cloud Storage. Convert the MySQL schema to a Spanner schema, create a JSON manifest file, and run a Google-provided Dataflow template to load the data into Spanner.
B. Use a Google-provided Dataflow template to replicate the MySQL database in BigQuery.
C. Use Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.
D. Install MySQL on a Compute Engine virtual machine. Export the database files using the mysqldump command. Upload the files to Cloud Storage, and import them into the MySQL instance on Compute Engine.

Answer: C

NEW QUESTION # 68
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 Composer to orchestrate and run data workflows.
B. Use Cloud Data Fusion to visually design and manage the pipelines.
C. Use Dataform to build, orchestrate, and monitor the pipelines.
D. Use Dataproc to run MapReduce jobs for distributed data processing.

Answer: C

NEW QUESTION # 69
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