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Exam 70-767: Implementing a Data Warehouse using SQL Practice Test

Exam 70-767: Implementing a Data Warehouse using SQL concerns the design and implementation of SQL Server data warehouse solutions. The subject area includes organizing dimensional data, preparing and moving source data through ETL workflows, and structuring warehouse information so reporting queries can use it effectively.

Exam overview

Exam providerMicrosoft
Exam code70-767
CategoryIT Certifications

About the Exam 70-767: Implementing a Data Warehouse using SQL Exam

Warehouse implementation calls for decisions about how facts and dimensions represent business events. Relevant design concepts include grain, surrogate keys, conformed dimensions, degenerate dimensions, and approaches for retaining historical attribute values. SQL Server Integration Services (SSIS) work involves controlling package execution, transforming and validating rows, and directing errors so problematic records can be handled rather than silently loaded. Staging and warehouse tables also need structures suited to common query patterns, including appropriate indexing. Reporting tasks may involve exposing data through views or a semantic layer, with attention to how users access measures and dimensions across fact tables. These skills connect source-data handling to consistent analytical results: a sound design supports repeatable loads, preserves useful history, and makes warehouse data easier to query and interpret.

Sample Practice Questions

Review a selection from this exam's published US Practice Tests practice bank.

Question 1

In SQL Server Integration Services, what is the main use of a Sequence Container in an ETL package?

  1. ATo enforce foreign key constraints during loads
  2. BTo group tasks so they can be managed and executed as a logical unit
  3. CTo convert character data into numeric data types
  4. DTo automatically generate clustered indexes
Show answerCorrect answer: B

A Sequence Container is used to organize related tasks and treat them as one logical unit, often to simplify control flow, apply precedence constraints, or share transaction scope.

Question 2

What is the main purpose of a surrogate date key in a warehouse date dimension?

  1. ATo store the original source system transaction identifier
  2. BTo provide a compact, consistent key for joining fact rows to the date dimension
  3. CTo replace all calendar attributes such as month and quarter
  4. DTo ensure the date dimension never needs maintenance
Show answerCorrect answer: B

A surrogate date key provides a consistent internal join key between facts and the date dimension. It simplifies joins and keeps the warehouse independent from source system date formats.

Question 3

What is the main benefit of using a SQL Server view over a complex warehouse join for report users?

  1. AIt can present a simpler, reusable logical interface to the data
  2. BIt permanently deletes duplicate records
  3. CIt replaces the need for permissions
  4. DIt stores data more efficiently than base tables in all cases
Show answerCorrect answer: A

Views can hide complexity and provide a consistent query surface for reporting, while still referencing the underlying warehouse tables.

Question 4

Which indexing choice is often helpful on a large staging or warehouse table when queries commonly filter by a date column?

  1. AAn index beginning with the date column
  2. BA unique index on every column
  3. CNo indexes at all in any warehouse table
  4. DA full-text index on the date column
Show answerCorrect answer: A

If queries frequently filter by a date, leading with that column in an index can improve seek performance and reduce scanning.

Question 5

Which approach is typically used to preserve historical changes for a dimension attribute such as customer city?

  1. ADrop and recreate the dimension each night
  2. BUse a slowly changing dimension pattern
  3. CStore the value only in the fact table
  4. DConvert the attribute to a computed column in the report
Show answerCorrect answer: B

Slowly changing dimension patterns are used to manage attribute changes over time. Depending on the business need, a type 1 or type 2 approach may be used to overwrite values or preserve history.

Question 6

What is the main reason to use error output handling in a data flow when loading warehouse data?

  1. ATo automatically shrink the source database
  2. BTo route rows that fail validation or conversion to a separate path for review
  3. CTo guarantee that every row loads successfully
  4. DTo force all transformations to run in parallel
Show answerCorrect answer: B

Error outputs let you separate problematic rows from valid rows. This supports data quality review, logging, and reprocessing without stopping the entire load.

Question 7

Which design is most appropriate for storing a transaction count in a fact table?

  1. AA numeric measure that can be aggregated across the chosen grain
  2. BA text attribute describing the customer
  3. CA date format string stored as the key measure
  4. DA separate dimension row for each transaction count
Show answerCorrect answer: A

Counts are analytical measures that belong in fact tables, where they can be summed, averaged, or otherwise aggregated at the fact grain.

How to use this practice test

The free practice bank contains 50 published questions. Each session presents up to 20 questions selected at random and displays results after completion. Use the questions to check your understanding of warehouse modeling, SQL Server ETL concepts, and reporting structures, then revisit the specific design decisions behind any missed answers.

Preparation tips

  • Sketch a fact table’s grain before choosing its measures, then distinguish additive transaction counts from descriptive dimension attributes.
  • Compare surrogate and natural keys, and work through how a customer attribute change would be represented when history must be preserved.
  • Trace an SSIS data flow from source to destination; note where validation, error outputs, and package control containers affect loading.
  • For a date-filtered warehouse query, consider the table’s row volume and query pattern before selecting an indexing approach.
  • Explain when a view or semantic layer can simplify report access, and how conformed dimensions support analysis across multiple fact tables.
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