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Google ADP Exam Syllabus Topics:

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

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Google Associate Data Practitioner Sample Questions (Q32-Q37):

NEW QUESTION # 32
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. Apply a filter to only show products with a positive profit margin.
C. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
D. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.

Answer: C

NEW QUESTION # 33
You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?

A. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
B. Use Colab Enterprise to develop a custom model for purchase prediction.
C. Use Vertex Al Workbench to develop a custom model for purchase prediction.
D. Use BigQuery ML to create a logistic regression model for purchase prediction.

Answer: D

NEW QUESTION # 34
Your company uses Looker to visualize and analyze sales dat
a. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?

A. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
B. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
C. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
D. Create a single Explore with all sales metrics. Build the dashboard using this Explore.

Answer: D

NEW QUESTION # 35
You have a Dataproc cluster that performs batch processing on data stored in Cloud Storage. You need to schedule a daily Spark job to generate a report that will be emailed to stakeholders. You need a fully-managed solution that is easy to implement and minimizes complexity. What should you do?

A. Use Cloud Composer to orchestrate the Spark job and email the report.
B. Use Cloud Scheduler to trigger the Spark job. and use Cloud Run functions to email the report.
C. Use Dataproc workflow templates to define and schedule the Spark job, and to email the report.
D. Use Cloud Run functions to trigger the Spark job and email the report.

Answer: C

NEW QUESTION # 36
Your team is building several data pipelines that contain a collection of complex tasks and dependencies that you want to execute on a schedule, in a specific order. The tasks and dependencies consist of files in Cloud Storage, Apache Spark jobs, and data in BigQuery. You need to design a system that can schedule and automate these data processing tasks using a fully managed approach. What should you do?

A. Create directed acyclic graphs (DAGS) in Cloud Composer. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
B. Use Cloud Scheduler to schedule the jobs to run.
C. Create directed acyclic graphs (DAGS) in Apache Airflow deployed on Google Kubernetes Engine. Use the appropriate operators to connect to Cloud Storage, Spark, and BigQuery.
D. Use Cloud Tasks to schedule and run the jobs asynchronously.

Answer: A

NEW QUESTION # 37
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