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Data Cloud Consultant Free Sample Questions

12 free sample questions150 in the full practice test

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Data-Con-101 Sample Questions

  1. Question 1

    Intermediate

    Data Ingestion and Modeling · Ingestion Connectors

    A consultant is designing a data ingestion strategy for a retail client who needs to bring in historical transaction data from an AWS S3 bucket. The data is approximately 50 GB in size and consists of CSV files. The client requires this data to be available in Data Cloud for segmentation within 24 hours. Which ingestion method provides the most efficient and reliable path for this specific requirement?

    Answer and explanation

    Correct answer: C

    The Cloud Storage Connector is designed specifically to ingest data from external cloud storage services like AWS S3. It handles large batch files (like a 50 GB CSV history) efficiently and is the standard method for bringing bulk historical data into Data Cloud.

  2. Question 2

    Advanced

    Identity Resolution · Reconciliation Rules

    A Data Cloud consultant is configuring Identity Resolution for a banking client. The client has three sources of customer data: Core Banking (High Trust), Credit Card System (Medium Trust), and Marketing Leads (Low Trust). When merging the 'Email Address' field, the requirement is to always prefer the value from Core Banking if it exists; otherwise, use the Credit Card System, and finally the Marketing Leads. Which reconciliation rule should be applied?

    Answer and explanation

    Correct answer: C

    Source Priority reconciliation rules allow the consultant to define an ordered list of data sources. Data Cloud will select the value from the highest-priority source that has a non-null value, which perfectly matches the client's tiered trust requirement.

  3. Question 3

    Beginner

    Data Ingestion and Modeling · Data Model Objects

    Which of the following objects in the Data Cloud data model is strictly non-materialized and acts as a view on top of Data Lake Objects (DLOs) during the harmonization process?

    Answer and explanation

    Correct answer: B

    Data Model Objects (DMOs) are logical views or groupings of data that map to Data Lake Objects (DLOs). They are not physically materialized storage themselves but represent the harmonized schema that other services (like Segmentation) query against.

  4. Question 4

    Intermediate

    Segmentation and Insights · Calculated Insights

    A company wants to calculate the 'Lifetime Value' (LTV) of their customers. This calculation requires aggregating transaction data over the last 5 years. Which Data Cloud feature is designed to perform this complex aggregation and make the metric available for segmentation?

    Answer and explanation

    Correct answer: C

    Calculated Insights allow users to define complex metrics (like sums, averages, counts) across multiple dimensions (like CustomerId) using SQL. This is the correct tool for deriving LTV from historical transaction data.

  5. Question 5

    Intermediate

    Data Ingestion and Modeling · Data Mapping

    True or False: In Data Cloud, a single Data Stream can populate multiple Data Model Objects (DMOs) directly without the use of a data transform.

    Answer and explanation

    Correct answer: A

    True. When mapping a Data Lake Object (DLO) to the data model, you can map fields from one DLO to fields in multiple different DMOs. For example, a single 'Contact' CSV might map fields to both the 'Individual' DMO and the 'Contact Point Email' DMO.

  6. Question 6

    Advanced

    Act on Data · Troubleshooting

    A consultant is troubleshooting a segmentation issue where recent purchases are not appearing in the target segment. The data stream is active, and the data model is mapped. Upon investigation, the consultant notices that the 'Data Transforms' used to clean the data run on a schedule that does not align with the segment publication. What is the most likely cause of the data latency?

    Answer and explanation

    Correct answer: B

    If a Batch Data Transform is used to populate a DMO that the segment relies on, the segment will only reflect data available after the transform job has successfully finished. If the segment publishes before the transform runs, the new data is missed.

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