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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q24-Q29):

NEW QUESTION # 24
You need to transfer approximately 300 TB of data from your company's on-premises data center to Cloud Storage. You have 100 Mbps internet bandwidth, and the transfer needs to be completed as quickly as possible. What should you do?

A. Use the gcloud storage command to transfer the data over the internet.
B. Compress the data, upload it to multiple cloud storage providers, and then transfer the data to CloudStorage.
C. Use Cloud Client Libraries to transfer the data over the internet.
D. Request a Transfer Appliance, copy the data to the appliance, and ship it back to Google.

Answer: D

NEW QUESTION # 25
You are working with a small dataset in Cloud Storage that needs to be transformed and loaded into BigQuery for analysis. The transformation involves simple filtering and aggregation operations. You want to use the most efficient and cost-effective data manipulation approach. What should you do?

A. Use BigQuery's SQL capabilities to load the data from Cloud Storage, transform it, and store the results in a new BigQuery table.
B. Create a Cloud Data Fusion instance and visually design an ETL pipeline that reads data from Cloud Storage, transforms it using built-in transformations, and loads the results into BigQuery.
C. Use Dataflow to perform the ETL process that reads the data from Cloud Storage, transforms it using Apache Beam, and writes the results to BigQuery.
D. Use Dataproc to create an Apache Hadoop cluster, perform the ETL process using Apache Spark, and load the results into BigQuery.

Answer: A

NEW QUESTION # 26
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 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.
B. 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.
C. 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
D. Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.

Answer: D

NEW QUESTION # 27
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. Use a Google-provided Dataflow template to replicate the MySQL database in BigQuery.
B. Use Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.
C. 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.
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: B

NEW QUESTION # 28
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 new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
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 derived table that pre-calculates the profit margin for each product, and include it in the Looker model.

Answer: B

NEW QUESTION # 29
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