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| Section | Objectives |
|---|---|
| Topic 1: Understanding Tableau Concepts | - Core Tableau terminology
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| Topic 2: Connecting to Data | - Data sources and connections
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| Topic 3: Sharing Insights | - Publishing and sharing
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| Topic 4: Exploring and Analyzing Data | - Basic calculations
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>> New Analytics-101 Study Notes <<
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NEW QUESTION # 38
When should a Tableau associate use a relationship instead of a join for two data sets?
Answer: A
Explanation:
A relationship is particularly appropriate when the tables being combined contain data at different levels of detail, making D correct. Tableau relationships operate in the logical layer of the data model and preserve each logical table's native granularity. Tableau determines the necessary joins dynamically during analysis according to the fields used in the visualization. This behavior is materially different from a physical join. A join merges records into a single physical table before analysis. If tables have different granularities, a physical join can duplicate measure values or remove records depending on the join structure and matching keys. Relationships reduce that risk because Tableau aggregates relevant information according to each table's natural level of detail before combining results as required by the visualization. Official Tableau guidance explicitly recommends relationships for combining data from different levels of detail. Relationships also preserve independent logical tables rather than flattening them into a single physical table. Option B merely describes where tables reside and does not determine whether a relationship or join is preferable. Options A and C likewise do not identify the principal modeling scenario addressed by relationships. Relevant Tableau Desktop concepts include logical tables, physical tables, relationships, joins, aggregation behavior, and preservation of level of detail.
NEW QUESTION # 39
Which two statements accurately describe relationships in Tableau Desktop?
Answer: A,C
Explanation:
Relationships are designed to preserve each table's level of detail and keep logical tables separate within the data source, making A and B correct. Unlike physical joins, relationships do not flatten participating tables into one combined row-level table when the data source is created. Tableau keeps each logical table distinct and evaluates the relationship dynamically based on fields actually used in a visualization. This architecture helps Tableau analyze data residing at different granularities while reducing the duplication and data-loss risks that can occur with fixed physical joins. Tableau's official relationship documentation explicitly states that relationships maintain each table's native level of detail and create independent domains at multiple granularities. The tables are not merged together in the data source. Option C describes the behavior of joins or unions in the physical layer. Physical tables can be combined to form one logical table, but this is not what a relationship does. Option D is also incorrect because relationships do not require the author to choose a fixed join type such as inner, left, right, or full outer. Tableau determines appropriate joins contextually during analysis. Relevant Tableau Desktop concepts include logical tables, physical tables, relationships, contextual joins, cardinality, and preservation of granularity.
NEW QUESTION # 40
A Tableau associate is creating a combined axis chart.
Where should the associate drag the second measure after dragging the first measure to the Rows shelf?
Answer: C
Explanation:
Because the first measure is on the Rows shelf, Tableau creates a vertical quantitative axis in the visualization. To construct a combined axis, the associate should drag the second measure directly onto that existing vertical axis, making B correct. A combined axis allows multiple measures to share the same axis instead of displaying them in separate panes or on independent dual axes. When the second measure is dragged onto the existing axis and Tableau displays the appropriate drop indicator, Tableau creates a shared Measure Values-based view containing both measures. The orientation matters. Fields on Rows normally produce a vertical y-axis, whereas fields on Columns normally generate a horizontal x-axis. Consequently, because the first measure was specifically placed on Rows, the target is the vertical axis rather than the horizontal axis. Dropping the second measure on the Marks card would encode that field through a mark property rather than create a combined quantitative axis. Dropping it on Filters would restrict records or values and would not combine the measures. This mechanism should also be distinguished from a dual-axis chart, where two independent axes are layered. A combined axis instead places multiple measures on a shared axis and commonly uses Measure Names and Measure Values for differentiation.
NEW QUESTION # 41
Which two types of fields appear blue?
Answer: B,C
Explanation:
In Tableau, blue identifies discrete fields, regardless of whether the field is classified as a dimension or a measure. Therefore, both Discrete dimensions and Discrete measures appear blue, making B and C correct.
Tableau separates two different field characteristics. The first is the semantic role: dimension or measure. The second is the behavioral classification: discrete or continuous. These concepts are related but not equivalent.
A discrete field contains distinct values and normally creates headers when added to Rows or Columns.
Tableau represents the pill in blue. Most dimensions are discrete by default, so blue dimensions are common.
Measures are generally continuous, but a measure can be converted to discrete; once it is discrete, its pill also becomes blue. Continuous fields appear green. Continuous dimensions-most commonly continuous date dimensions-and continuous measures therefore use green pills and typically create axes rather than headers.
Tableau's official Dimensions and Measures documentation explicitly presents four valid combinations:
discrete dimensions, continuous dimensions, discrete measures, and continuous measures. It shows discrete dimensions and discrete measures as blue, while both continuous forms are green. This distinction is foundational to predicting how Tableau will structure a visualization.
NEW QUESTION # 42
A Tableau associate has a data set that builds a union between two tables. The associate needs to extract the data set.
What should the associate use to extract the data set?
Answer: B
Explanation:
The correct configuration is Logical tables that use a single table extract, making C correct. In Tableau's data model, a union operates in the physical layer. The two unioned physical tables are merged into one resulting logical table. Tableau explicitly describes logical tables as containers that can contain multiple physical tables joined or unioned together. When an extract is stored using Logical Tables, Tableau creates one extract table for each logical table in the data source. Because the scenario contains one logical table composed of the two unioned physical tables, the extract is consequently represented as a single extract table. This is different from using Physical Tables storage. Physical storage retains an extract table for each physical table and is subject to additional eligibility restrictions. Tableau's current terminology replaced the older "Single Table" and
"Multiple Tables" extract terminology with Logical Tables and Physical Tables, respectively. Official Tableau documentation states that physical tables joined or unioned inside a logical table are merged as part of that logical table, and that Logical Tables extract storage creates one extract table per logical table. Therefore, a two-table union forming one logical table corresponds to a logical/single-table extract structure.
NEW QUESTION # 43
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