Analytics-101の学習教材で20〜30時間準備したと主張することができます。Analytics-101試験に簡単に合格して、期待されるスコアを取得できます。 またSalesforce、Analytics-101試験問題の無料デモを提供しており、Analytics-101トレーニング資料の有効性と正確性を確認できます。MogiExam やって来てみてください！ Analytics-101トレーニング資料の高い精度に驚かれることでしょう。 そして、Analytics-101練習問題集の高い合格率は99％から100％なので、Salesforce Certified Tableau Desktop Foundations試験に簡単に合格します。
| Section | Weight | Objectives |
|---|---|---|
| Exploring and Analyzing Data | 37% | - Organize data and apply filters - Create basic charts - Apply analytics to a worksheet |
| Sharing Insights | 25% | - View and share workbook data - Create and modify a dashboard - Format view for presentation |
| Understanding Tableau Concepts | 15% | - Understand dimensions and measures - Understand aggregations - Understand discrete and continuous fields |
| Connecting to and Preparing Data | 23% | - Create live connections and extracts - Create and manage the data model - Manage data properties |
IT認定試験の中でどんな試験を受けても、MogiExamのAnalytics-101試験参考資料はあなたに大きなヘルプを与えることができます。それは MogiExamのAnalytics-101問題集には実際の試験に出題される可能性がある問題をすべて含んでいて、しかもあなたをよりよく問題を理解させるように詳しい解析を与えますから。真剣にMogiExamのSalesforce Analytics-101問題集を勉強する限り、受験したい試験に楽に合格することができるということです。
質問 # 38
Which aggregation is available without requiring a table calculation or calculated field?
正解：C
解説：
Standard deviation is available as a native aggregation for a measure and does not require the author to create either a table calculation or a calculated field. Therefore, C is correct. When a measure is used in a Tableau view, its aggregation can be changed through the field menu. In addition to commonly used functions such as SUM, AVG, MIN, MAX, and COUNT, Tableau provides Standard Deviation and Standard Deviation (Population) as built-in aggregation choices. The calculation is performed directly over the records contributing to the current mark. Running Total and Percent of Total are different because they are table calculations. They operate on already aggregated values after Tableau has constructed the visualization and depend on the addressing and partitioning of the view. Sample covariance is supported through calculation functionality such as covariance functions but is not presented as the equivalent direct aggregation option in the basic measure aggregation menu represented by this question. Implementing covariance typically involves specifying the two expressions whose covariance is required. Tableau's official Data Aggregation documentation explicitly lists standard deviation among the aggregations available for measures, including sample and population forms. Consequently, it can be selected directly without constructing an additional calculated field or applying a quick table calculation.
質問 # 39
When should a Tableau associate use a relationship instead of a join for two data sets?
正解：C
解説：
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.
質問 # 40
Which type of date filter can a Tableau associate use to choose a range of dates based on TODAY()?
正解：A
解説：
A Relative dates filter is designed for date ranges that dynamically change relative to the current date, making B correct. This filter can represent periods such as the current week, previous month, year to date, or the last specified number of days, weeks, months, quarters, or years. The important distinction is that a relative date filter is dynamic. As the current date advances, Tableau recalculates the applicable range when the view is opened or refreshed. This behavior is appropriate for requirements based conceptually on TODAY() because the filter should continue tracking the present period rather than retaining fixed start and end dates. A Range of dates filter establishes a specific date interval and is better suited to fixed boundaries. A Starting Date or Ending Date represents an open-ended boundary from a particular date rather than a dynamically shifting interval relative to today. Tableau's official documentation states that relative date filters dynamically update to display a time period relative to when the view is opened. Examples include the current week, year to date, or past ten days. This functionality allows analysts to create dashboards that consistently display the most recent period without manually editing the filter as calendar time progresses. Relevant topics include date fields, relative date filtering, dynamic analysis, and interactive filter configuration.
質問 # 41
What are two outcomes when a Tableau associate drags a continuous date value to the Rows shelf?
正解：A、C
解説：
A continuous date field produces a green pill and generates an axis, making B and C correct. Tableau uses color as an immediate visual cue for discrete versus continuous fields. Green pills represent continuous fields, whereas blue pills represent discrete fields. A date can function in either form. When the analyst chooses a continuous date value such as continuous Month or continuous Year, Tableau represents the field as an unbroken temporal range rather than a finite collection of date-part headers. When this continuous date is placed on the Rows shelf, Tableau generates a continuous axis corresponding to the selected temporal granularity. The axis behaves similarly to other continuous quantitative axes in the sense that values occupy positions along an uninterrupted scale. Option A instead characterizes a discrete date part, such as discrete Month, where Tableau creates categorical headers or labels like January, February, and March. Option D is likewise associated with discrete fields because discrete pills are blue. Tableau's official Continuous Dates documentation states that continuous dates create an axis and that converting a date field to continuous causes the field to turn green. Tableau's general field-role documentation separately confirms that continuous fields create axes while discrete fields create headers.
質問 # 42
A Tableau associate has a continuous numeric measure named Sales.
Which chart type is created when the associate double-clicks the Sales measure?
正解：B
解説：
Double-clicking a continuous numeric measure such as Sales in an empty Tableau worksheet automatically produces a bar chart, making C correct. Tableau uses intelligent defaults when fields are double-clicked in the Data pane. With a single measure and no dimensions already defining the view, Tableau aggregates the measure-normally using its default aggregation such as SUM-and adds it to the visualization. The Automatic mark type resolves to a bar, producing a single bar representing the aggregated Sales value.
Tableau's official documentation provides this exact construction using the Profit measure. It states that double-clicking Profit automatically adds the field to the view in an intelligent way, and the documented resulting visualization is a bar chart of Profit. Sales is the same type of field-a quantitative measure-so the same automatic-view rule applies. A line chart ordinarily requires an ordered dimension such as a date field to provide a sequence across which values can be connected. A pie chart requires additional categorical structure for meaningful segmentation. A text table is generally created when a dimension is double-clicked first and subsequent fields refine that cross-tabular structure. Therefore, a lone continuous numeric measure double- clicked in a new worksheet produces the automatic single-measure bar visualization.
質問 # 43
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