Introduction to DE

<p>All of the above (D)</p> Signup and view all the answers

<p>All of the above (D)</p> Signup and view all the answers

<p>All of the above (D)</p> Signup and view all the answers

<p>All of the above (D)</p> Signup and view all the answers

<p>The availability of data at prediction time. (C)</p> Signup and view all the answers

<p>A column in a dataset representing a particular attribute. (B)</p> Signup and view all the answers

<p>To ensure the model can accurately predict future outcomes based on historical patterns. (C)</p> Signup and view all the answers

<p>All of the above. (E)</p> Signup and view all the answers

<p>It enables the models to learn from historical patterns and predict future outcomes. (B)</p> Signup and view all the answers

<p>BigQuery employs a dedicated Identity and Access Management system, replacing SQL GRANT and REVOKE statements used in traditional databases (B)</p> Signup and view all the answers

<p>BigQuery provides on-demand storage and compute resources, eliminating the need for resource provisioning upfront (A)</p> Signup and view all the answers

<p>Dynamically allocating resources based on actual usage patterns (B)</p> Signup and view all the answers

<p>BigQuery enables data engineers to focus their efforts on high-value tasks, rather than managing infrastructure (B)</p> Signup and view all the answers

<p>How BigQuery dynamically allocates storage and query resources based on usage patterns (A)</p> Signup and view all the answers

<p>Data engineers can focus more on analyzing data insights rather than infrastructure management (D)</p> Signup and view all the answers

<p>Enhanced security through a centralized resource management system (C)</p> Signup and view all the answers

<p>Cloud Composer simplifies the scheduling and execution of tasks involved in ML model training, such as data preprocessing and model evaluation. (D)</p> Signup and view all the answers

<p>Data warehouses are more efficient at querying data due to data transformations and structured storage. (D)</p> Signup and view all the answers

<p>Data visualization and reporting. (B)</p> Signup and view all the answers

<p>All of the above. (D)</p> Signup and view all the answers

<p>All of the above. (D)</p> Signup and view all the answers

<p>All of the above. (D)</p> Signup and view all the answers

<p>It enables the automation of complex data processing workflows. (C)</p> Signup and view all the answers

A consolidated storage system for cleaned and structured data, optimized for querying.

Extraction, Transformation, and Loading; the process of preparing data for a data warehouse.

A storage repository holding raw data in its native format until needed.

Source systems where raw data is generated or collected from various activities.

Questions asked by users to extract specific data from the warehouse.

Decision Support System; a type of database aiding in decision-making based on data analysis.

Data related to marketing efforts, tracking their performance and effectiveness.

The most effective marketing strategies based on measurable results, such as sales or customer engagement.

A system for managing permissions in BigQuery instead of SQL GRANT and REVOKE.

Ability to do more with less by focusing on customized tasks in the cloud.

Dynamic allocation of storage and query resources based on usage patterns.

Units of computation used for CPU and RAM during queries in BigQuery.

Resources are allocated as needed without prior provisioning in BigQuery.

In the cloud, resources are dynamically managed unlike traditional RDBMS systems.

A professional who builds data pipelines for processing and storing data.

Series of data processing steps that collect, transform, and store data.

Practicing data engineering using cloud services for scalability and efficiency.

Google Cloud's serverless, petabyte-scale data warehouse service.

Policies and processes that ensure data is usable, secure, and compliant.

Data pipelines that are fully automated and reliable for periodic data processing.

A structured process that allows for the collection and transformation of raw data into features for ML models.

The unprocessed data collected from various sources before any cleaning or transformation.

A mathematical model that uses data to make predictions or decisions based on patterns found in the data.

Characteristics or properties of the data that can be used for analysis and building ML models.

The period when data is used for making predictions with an ML model.

The ease with which data can be discovered and used by machine learning teams.

A fully-managed version of Apache Airflow used for workflow orchestration in Google Cloud.

An open-source tool for designing and managing data workflows.

The automated organization of tasks and processes into a cohesive flow.

Workflows that involve executing and monitoring tasks in a live environment.

The process of feeding data to algorithms to create predictive models.

A tool for tracking and reporting website traffic and user behavior.

A service for storing and retrieving any amount of data at any time, on Google’s infrastructure.

Automatic initiation of workflows based on specific events, like new file uploads.

A series of steps that process raw data into a usable format.

A collection of servers used for storing and processing big data efficiently.

Using SQL commands to extract insights from databases and datasets.

Making data accessible to non-technical users for analysis and insights.