Available Number of Questions: Maximum of
106 Questions
Exam Name: Google Cloud Associate Data Practitioner
Exam Duration: 120 Minutes
Related Certification(s):
Google Cloud Certified, Google Data Practitioner Certifications
Google Associate Data Practitioner Exam Topics - You’ll Be Tested in Actual Exam
The Google Associate-Data-Practitioner exam is a rigorous assessment designed to evaluate your proficiency in various aspects of data analysis and machine learning. It covers a wide range of topics, including data processing techniques, data storage and management, data analysis and visualization, machine learning fundamentals, and practical applications of machine learning. Throughout the exam, you'll encounter questions that test your understanding of data structures, algorithms, and programming languages. Additionally, you'll need to demonstrate your ability to work with Google Cloud Platform (GCP) tools and services, such as BigQuery, Cloud Storage, and Cloud Machine Learning Engine. The exam also assesses your knowledge of data security and privacy practices, as well as your skills in designing and implementing data-driven solutions. By mastering these topics and gaining hands-on experience with real-world scenarios, you'll be well-prepared to tackle the challenges presented in the Google Associate-Data-Practitioner exam and showcase your expertise as a data practitioner.
Google Associate Data Practitioner Exam Short Quiz
Attempt this Google Associate Data Practitioner exam quiz to self-assess your preparation for the actual Google Cloud Associate Data Practitioner exam. CertBoosters also provides premium Google Associate Data Practitioner exam questions to pass the Google Cloud Associate Data Practitioner exam in the shortest possible time. Be sure to try our free practice exam software for the Google Associate Data Practitioner exam.
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Google Associate Data Practitioner Exam Quiz
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GoogleAssociate Data Practitioner
Q1:
You work for a healthcare company that has a large on-premises data system containing patient records with personally identifiable information (PII) such as names, addresses, and medical diagnoses. You need a standardized managed solution that de-identifies PII across all your data feeds prior to ingestion to Google Cloud. What should you do?
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AUse Cloud Run functions to create a serverless data cleaning pipeline. Store the cleaned data in BigQuery.
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BUse Cloud Data Fusion to transform the data. Store the cleaned data in BigQuery.
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CLoad the data into BigQuery, and inspect the data by using SQL queries. Use Dataflow to transform the data and remove any errors.
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DUse Apache Beam to read the data and perform the necessary cleaning and transformation operations. Store the cleaned data in BigQuery.
GoogleAssociate Data Practitioner
Q2:
You are constructing a data pipeline to process sensitive customer data stored in a Cloud Storage bucket. You need to ensure that this data remains accessible, even in the event of a single-zone outage. What should you do?
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ASet up a Cloud CDN in front of the bucket.
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BEnable Object Versioning on the bucket.
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CStore the data in a multi-region bucket.
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DStore the data in Nearline storaqe.
GoogleAssociate Data Practitioner
Q3:
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?
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AUse BigQuery ML to create a logistic regression model for purchase prediction.
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BUse Vertex AI Workbench to develop a custom model for purchase prediction.
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CUse Colab Enterprise to develop a custom model for purchase prediction.
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DExport the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
GoogleAssociate Data Practitioner
Q4:
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?
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AInstall 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.
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BUse Database Migration Service to transfer the data to Cloud SQL for MySQL, and configure the on premises MySQL database as the source.
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CUse a Google-provided Dataflow template to replicate the MySQL database in BigQuery.
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DExport 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.
GoogleAssociate Data Practitioner
Q5:
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?
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AUse Cloud Client Libraries to transfer the data over the internet.
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BUse the gcloud storage command to transfer the data over the internet.
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CCompress the data, upload it to multiple cloud storage providers, and then transfer the data to Cloud Storage.
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DRequest a Transfer Appliance, copy the data to the appliance, and ship it back to Google.
🎉 Google Associate Data Practitioner Quiz Complete!
