Associate Data Practitioner: Google Cloud Associate Data Practitioner
The Google Cloud Associate Data Practitioner certification is designed to validate foundational skills in data preparation, analysis, and visualization using Google Cloud tools and services. Practicing with real exam questions shared by those who have passed the exam can significantly boost your chances of success. In this guide, we provide Associate Data Practitioner practice test questions and answers contributed by certified professionals.
Exam Details:
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Exam Name: Google Cloud Associate Data Practitioner
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Exam Format: Multiple-choice and multiple-select questions
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Test Duration: 2 hours
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Number of Questions: 50–60
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Passing Score: Google does not publicly disclose the exact passing score, but it typically hovers around 70%.
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Recommended Experience: At least 6 months of hands-on experience working with data on Google Cloud
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Exam Topics Covered:
- Data Foundations: Understanding core data concepts, data types, and data quality principles.
- Data Preparation: Cleaning, transforming, and preparing data for analysis using Google Cloud tools and services.
- Data Analysis: Exploring, visualizing, and interpreting data to uncover insights.
- Machine Learning: Applying machine learning techniques to build and deploy models on Google Cloud Platform.
Why Use This Associate Data Practitioner Practice Test?
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Real Exam Experience: Questions closely match the actual test format.
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Identify Weak Areas: Helps pinpoint topics requiring further study.
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Up-to-Date Content: Regularly updated to align with Google Cloud exam objectives.
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Boost Confidence: Reduces exam anxiety through consistent practice.
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Improve Time Management: Helps you practice answering within the time limit.
Take advantage of these Associate Data Practitioner practice test questions shared by certified professionals. Start practicing today and get one step closer to becoming a Google Cloud Associate Data Practitioner!
Related questions
You manage a large amount of data in Cloud Storage, including raw data, processed data, and backups. Your organization is subject to strict compliance regulations that mandate data immutability for specific data types. You want to use an efficient process to reduce storage costs while ensuring that your storage strategy meets retention requirements. What should you do?
Configure lifecycle management rules to transition objects to appropriate storage classes based on access patterns. Set up Object Versioning for all objects to meet immutability requirements.
Move objects to different storage classes based on their age and access patterns. Use Cloud Key Management Service (Cloud KMS) to encrypt specific objects with customer-managed encryption keys (CMEK) to meet immutability requirements.
Create a Cloud Run function to periodically check object metadata, and move objects to the appropriate storage class based on age and access patterns. Use object holds to enforce immutability for specific objects.
Use object holds to enforce immutability for specific objects, and configure lifecycle management rules to transition objects to appropriate storage classes based on age and access patterns.
Using object holds and lifecycle management rules is the most efficient and compliant strategy for this scenario because:
Immutability: Object holds (temporary or event-based) ensure that objects cannot be deleted or overwritten, meeting strict compliance regulations for data immutability.
Cost efficiency: Lifecycle management rules automatically transition objects to more cost-effective storage classes based on their age and access patterns.
Compliance and automation: This approach ensures compliance with retention requirements while reducing manual effort, leveraging built-in Cloud Storage features.
You work for a retail company that collects customer data from various sources:
Online transactions: Stored in a MySQL database
Customer feedback: Stored as text files on a company server
Social media activity: Streamed in real-time from social media platforms
You need to design a data pipeline to extract and load the data into the appropriate Google Cloud storage system(s) for further analysis and ML model training. What should you do?
You have a Cloud SQL for PostgreSQL database that stores sensitive historical financial data. You need to ensure that the data is uncorrupted and recoverable in the event that the primary region is destroyed. The data is valuable, so you need to prioritize recovery point objective (RPO) over recovery time objective (RTO). You want to recommend a solution that minimizes latency for primary read and write operations. What should you do?
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?
You have an existing weekly Storage Transfer Service transfer job from Amazon S3 to a Nearline Cloud Storage bucket in Google Cloud. Each week, the job moves a large number of relatively small files. As the number of files to be transferred each week has grown over time, you are at risk of no longer completing the transfer in the allocated time frame. You need to decrease the total transfer time by replacing the process. Your solution should minimize costs where possible. What should you do?
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?
Your company wants to implement a data transformation (ETL) pipeline for their BigQuery data warehouse. You need to identify a managed transformation solution that allows users to develop with SQL and JavaScript, has version control, allows for modular code, and has data quality checks. What should you do?
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?
You are working on a project that requires analyzing daily social media data. You have 100 GB of JSON formatted data stored in Cloud Storage that keeps growing.
You need to transform and load this data into BigQuery for analysis. You want to follow the Google-recommended approach. What should you do?
You are building a batch data pipeline to process 100 GB of structured data from multiple sources for daily reporting. You need to transform and standardize the data prior to loading the data to ensure that it is stored in a single dataset. You want to use a low-code solution that can be easily built and managed. What should you do?
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