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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q39-Q44):

NEW QUESTION # 39
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. 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.
B. Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.
C. 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.
D. Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.

Answer: B

NEW QUESTION # 40
Your retail company wants to analyze customer reviews to understand sentiment and identify areas for improvement. Your company has a large dataset of customer feedback text stored in BigQuery that includes diverse language patterns, emojis, and slang. You want to build a solution to classify customer sentiment from the feedback text. What should you do?

A. Develop a custom sentiment analysis model using TensorFlow. Deploy it on a Compute Engine instance.
B. Preprocess the text data in BigQuery using SQL functions. Export the processed data to AutoML Natural Language for model training and deployment.
C. Use Dataproc to create a Spark cluster, perform text preprocessing using Spark NLP, and build a sentiment analysis model with Spark MLlib.
D. Export the raw data from BigQuery. Use AutoML Natural Language to train a custom sentiment analysis model.

Answer: D

NEW QUESTION # 41
Your organization plans to move their on-premises environment to Google Cloud. Your organization's network bandwidth is less than 1 Gbps. You need to move over 500 ## of data to Cloud Storage securely, and only have a few days to move the dat a. What should you do?

A. Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.
B. Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.
C. Connect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage.
D. Request multiple Transfer Appliances, copy the data to the appliances, and ship the appliances back to Google Cloud to upload the data to Cloud Storage.

Answer: D

NEW QUESTION # 42
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 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.
B. 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.
C. 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.
D. 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.

Answer: B

NEW QUESTION # 43
You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team's existing skillset, processes data at scale, and minimizes cost. What should you do?

A. Use Dataform to define the transformations in SQLX.
B. Use Cloud Data Fusion to visually design and manage the pipeline.
C. Use Dataproc to run the transformations on a cluster.
D. Use Dataflow with a custom template for the transformation logic.

Answer: C

NEW QUESTION # 44
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