CDL practice questions 121–130

1. Question 121 · 288 in bank

   A vacation home rental organization wants to predict the popularity of properties in their upcoming busy season. They do not have a data science team, and want to use their in-house database administration skills to create a machine learning model. What should the organization do?

   • AUse custom training in Vertex AI with TensorFlow.
   • BIntegrate pre-trained APIs into their application.
   • CUse BigQuery ML and create models using SQL.
   • DBuild a model in AutoML using labeled data.

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   Answer: C

   Explanation

   C: BigQuery ML enables users with SQL skills to create and execute machine learning models directly within Google BigQuery. This approach is particularly suitable for organizations like the one described, which may not have specialized machine learning expertise but does have database administration skills. BigQuery ML allows these users to leverage their existing SQL knowledge to build and train predictive models, making it an accessible option for generating insights from their data.

   Source: Adapted from community discussion — Vivek007

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2. Question 122 · 288 in bank

   An organization needs to rapidly scale its use of computing resources and honor its commitment to environmental sustainability. What should the organization do?

   • ARefactor application software to use less energy.
   • BUse a public cloud provider with energy-efficient data centers.
   • CUse a carbon-neutral energy provider for an existing on-premises data center.
   • DPurchase energy-efficient servers for an existing on-premises data center.

   Show answer

   Answer: B

   Explanation

   B: Energy-Efficient Data Centers: Major public cloud providers invest heavily in optimizing their data centers for energy efficiency and often use renewable energy sources. These companies leverage advanced technologies to reduce power consumption and have the capital to invest in large-scale sustainable energy projects. By using a public cloud provider known for energy-efficient practices, the organization can scale computing resources more flexibly and sustainably compared to maintaining their own data center.

   Source: Adapted from community discussion — Vivek007

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3. Question 123 · 288 in bank

   What is a defining feature of a non-relational database?

   • AReporting across multiple data sources
   • BA strictly enforced schema
   • CA flexible data model
   • DQueries that join multiple tables

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   Answer: C

   Explanation

   C. A flexible data model A non-relational database (often called NoSQL) is characterized by its flexible data model, which allows for: - Storing unstructured, semi-structured, or structured data. - Avoiding the need for a strictly enforced schema, enabling dynamic changes to data structure. - Supporting various data models, such as key-value, document, column-family, and graph formats, depending on the use case. This flexibility makes non-relational databases ideal for scenarios with rapidly changing data requirements, large-scale datasets, or applications that need high performance and scalability.

   Source: Adapted from community discussion — joshnort

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4. Question 124 · 288 in bank

   Your organization needs to build streaming data pipelines. You don't want to manage the individual servers that do the data processing in the pipelines. Instead, you want a managed service that will automatically scale with the amount of data to be processed. Which Google Cloud product or feature should your organization choose?

   • APub/Sub
   • BDataflow
   • CData Catalog
   • DDataprep by Trifacta

   Show answer

   Answer: B

   Explanation

   For building streaming data pipelines without the need to manage individual servers and with automatic scalability, Google Cloud offers a managed service called Cloud Dataflow. Cloud Dataflow is a fully managed, serverless data processing service that allows you to build and execute data pipelines for both batch and streaming data processing. It provides a unified programming model for developing data processing logic and automatically handles the underlying infrastructure and resource management.

   Source: Adapted from community discussion — cookieMr

   References

   • Google Cloud documentation

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5. Question 125 · 288 in bank

   What is an example of unstructured data?

   • AHistorical stock inventory
   • BProduct ratings
   • CCustomer orders
   • DCall center transcripts

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   Answer: D

   Explanation

   D: Call Center Transcripts: This type of data is considered unstructured because it consists of free-form text that does not follow a predictable data model. Call center transcripts are typically generated from spoken language and recorded during customer service calls, containing varied vocabulary, sentence structures, and topics that do not fit neatly into pre-defined database fields.

   Source: Adapted from community discussion — Vivek007

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6. Question 126 · 288 in bank

   An organization has migrated all workloads to the cloud and is reviewing their cloud security posture. Who is now responsible for securing the physical infrastructure of the data centers?

   • AThe organization and the cloud service provider
   • BThird-party security service providers
   • CThe cloud service provider
   • DThe organization

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   Answer: C

   Explanation

   C. The cloud service provider In a cloud environment, the cloud service provider (CSP) is typically responsible for securing the physical infrastructure of the data centers. This includes: - The physical servers, networking equipment, and storage devices. - Physical security controls, such as access management to data center facilities, environmental controls, and surveillance. Cloud providers invest heavily in securing the underlying physical infrastructure to ensure reliability and safety for all customers using their services.

   Source: Adapted from community discussion — joshnort

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7. Question 127 · 288 in bank

   An organization wants to lease the resources they need for their customized servers from a cloud provider on a pay-as-you-go basis, instead of paying one time for hardware. Which service model should they use?

   • AHybrid cloud
   • BSoftware as a service
   • CInfrastructure as a service
   • DPlatform as a service

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   Answer: C

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8. Question 128 · 288 in bank

   An organization has petabytes of data gathered from a wide range of sources. They want to use the data for strategic analysis and to guide business decisions. What type of service should they use?

   • AMulti cloud environment
   • BVirtual machine environment
   • CHybrid cloud environment
   • DContainer environment

   Show answer

   Answer: A

   Explanation

   D: Container Environment: While containers themselves aren't specifically for handling data analytics, they are part of an infrastructure that can efficiently manage and scale data-intensive applications across various environments. Container orchestration platforms like Kubernetes can manage containers that run big data tools and applications, providing flexibility, scalability, and efficiency. This allows for the deployment of complex applications and data processing tasks that can handle large volumes of data effectively.

   Source: Adapted from community discussion — Vivek007

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9. Question 129 · 288 in bank

   An organization wants a purpose-built AI solution to increase efficiency and provide personalized interactions for their customer care team. Which Google Cloud AI solution should they use?

   • AText-to-Speech API
   • BCloud Talent Solution
   • CDocument AI
   • DContact Center AI

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   Answer: D

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10. Question 130 · 288 in bank

    An organization wants to use an open source library with a flexible ecosystem of tools to create and train its own machine learning models. Which product or solution should the organization use?

    • ACloud Functions
    • BApache Beam
    • CDataflow
    • DTensorFlow

    Show answer

    Answer: D

    Explanation

    D: TensorFlow is an open-source machine learning library developed by Google. It provides a comprehensive, flexible ecosystem of tools, libraries, and community resources that let researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. TensorFlow supports deep learning along with a suite of tools for model building, training, and deployment, making it ideal for a wide variety of machine learning applications across different platforms and languages.

    Source: Adapted from community discussion — Vivek007

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CDL practice, question 121. 288 questions available.