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Google ADP Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Visualization and Insights	20-30%	- Build visualizations using Looker Studio - Choose appropriate visualization types - Create dashboards and reports - Interpret and communicate findings - Present data insights to stakeholders
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%	- Query and analyze datasets - Apply statistical methods for analysis - Aggregate and summarize data - Build and maintain data pipelines - Use BigQuery and SQL for analytics
Topic 4: Data Preparation and Exploration	20-30%	- Identify data quality issues - Transform and prepare data for analysis - Explore data through visualization and queries - Ingest and acquire data - Perform exploratory data analysis (EDA)

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No doubt the Google ADP certification is a valuable credential that helps you to put your career on the right track and assist you to achieve your professional career goals. To achieve this goal you need to pass the Associate Data Practitioner (ADP) exam. To pass the Associate Data Practitioner (ADP) exam you need to start this journey with valid, updated, and real Google ADP PDF QUESTIONS. The Prep4sures ADP exam practice test questions are essential study material for quick Google ADP exam preparation.

Google Associate Data Practitioner Sample Questions (Q51-Q56):

NEW QUESTION # 51
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 Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
B. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
C. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
D. Create a single Explore with all sales metrics. Build the dashboard using this Explore.

Answer: D

NEW QUESTION # 52
You are storing data in Cloud Storage for a machine learning project. The data is frequently accessed during the model training phase, minimally accessed after 30 days, and unlikely to be accessed after 90 days. You need to choose the appropriate storage class for the different stages of the project to minimize cost. What should you do?

A. Store the data in Standard storage during the model training phase. Transition the data to Durable Reduced Availability (DRA) storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
B. Store the data in Nearline storage during the model training phase. Transition the data to Archive storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.
C. Store the data in Nearline storage during the model training phase. Transition the data to Coldline storage 30 days after model deployment, and to Archive storage 90 days after model deployment.
D. Store the data in Standard storage during the model training phase. Transition the data to Nearline storage 30 days after model deployment, and to Coldline storage 90 days after model deployment.

Answer: D

NEW QUESTION # 53
You created a curated dataset of market trends in BigQuery that you want to share with multiple external partners. You want to control the rows and columns that each partner has access to. You want to follow Google-recommended practices. What should you do?

A. Create a separate project for each partner and copy the dataset into each project. Publish each dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
B. Create a separate Cloud Storage bucket for each partner. Export the dataset to each bucket and assign each partner to their respective bucket. Grant bucket-level access by using 1AM roles.
C. Grant each partner read access to the BigQuery dataset by using 1AM roles.
D. Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.

Answer: D

NEW QUESTION # 54
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 temporary table.
B. Create a native table.
C. Create an object table.
D. Create an external table.

Answer: C

NEW QUESTION # 55
Following a recent company acquisition, you inherited an on- premises data infrastructure that needs to move to Google Cloud. The acquired system has 250 Apache Airflow directed acyclic graphs (DAGs) orchestrating data pipelines. You need to migrate the pipelines to a Google Cloud managed service with minimal effort. What should you do?

A. Create a Cloud Data Fusion instance. For each DAG, create a Cloud Data Fusion pipeline.
B. Convert each DAG to a Cloud Workflow and automate the execution with Cloud Scheduler.
C. Create a Google Kubernetes Engine (GKE) standard cluster and deploy Airflow as a workload. Migrate all DAGs to the new Airflow environment.
D. Create a new Cloud Composer environment and copy DAGS to the Cloud Composer dags/folder.

Answer: D

NEW QUESTION # 56
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