Introduction to dbt (1–10)
1. What is dbt primarily used for?
2. dbt projects are commonly written in which language?
3. Which dbt command compiles models without running them?
4. Which concept best describes dbt’s approach to data transformations?
5. dbt is most similar in concept to:
6. dbt fits into the modern data stack as:
7. When running dbt, transformations are executed:
8. dbt’s philosophy is often summarized as:
9. dbt models are typically organized in a:
10. dbt encourages a version-control workflow that primarily uses:
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11. Which command runs all models in a dbt project?
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12. Where are target profiles typically defined?
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13. The dbt debug command is used to:
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14. To specify different environments like dev or prod, you can:
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15. If your dbt command isn’t connecting to the warehouse, the first step is often to:
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16. dbt commands typically run from:
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17. A dbt_project.yml file defines:
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18. To run a subset of models, dbt uses:
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19. To run models for a specific folder only:
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20. dbt logs by default are found in:
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21. A ref() function in dbt is used to:
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22. Sources in dbt:
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23. Seeds in dbt are:
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24. Materializations define:
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25. A model configured as 'view' materialization:
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26. Ephemeral models:
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27. Incremental models are beneficial for:
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28. The var() function in dbt:
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29. Staging models typically:
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30. A snapshot in dbt:
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31. dbt tests are written in:
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32. Running dbt test does what?
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33. Documentation in dbt is generated by:
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34. The dbt docs serve command:
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35. Schema tests check:
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36. Custom tests are defined by:
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37. dbt docs generate outputs:
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38. The --select test_type:singular argument:
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39. Descriptions for models and columns are usually stored in:
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40. Tests can help ensure data quality by:
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41. Jinja in dbt is used for:
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42. A macro in dbt is:
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43. To reference a macro, you typically use:
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44. Macros help:
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45. A common macro file location is:
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46. Jinja variables are enclosed in:
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47. If you want a macro to do conditional logic, you’d use:
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48. Macros can also be used to:
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49. To debug a macro, you can:
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50. Macros can be made available to other dbt projects by:
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51. dbt packages are defined in:
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52. To install packages, you run:
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53. The dbt Package Hub hosts:
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54. Packages help:
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55. To specify a package version:
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56. A private Git repository can be referenced in packages.yml using:
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57. After updating packages.yml, you must:
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58. Package conflicts are resolved by:
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59. Packages often contain:
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60. Using packages can:
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61. Master Data Management (MDM) focuses on:
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62. Data lineage describes:
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63. Metadata in data modeling is:
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64. A data catalog helps users by:
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65. Data stewardship is responsible for:
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66. Data quality rules ensure:
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67. Reference data management involves:
68. Business Glossaries are important because:
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69. Data governance programs typically aim to:
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70. A common challenge in MDM is:
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71. Temporal data modeling deals with:
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72. Event modeling focuses on:
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73. Domain-driven design (DDD) in data modeling emphasizes:
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74. Data modeling for machine learning often involves:
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75. Ontologies and taxonomies in semantic data modeling:
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76. Data mesh architecture suggests:
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77. Streaming data modeling focuses on:
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78. Multi-model databases support:
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79. Data virtualization is about:
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80. Metadata-driven modeling leverages:
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81. In graph modeling, u201clabelsu201d are used to:
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82. RDF (Resource Description Framework) is used to:
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83. Modeling data for data lakes often requires:
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84. Data wrangling (data preparation) techniques help by:
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85. Agile data modeling promotes:
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86. Data modeling for cloud-native systems often involves:
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87. Geospatial data modeling involves:
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88. Hybrid transaction/analytical processing (HTAP) systems require models that:
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89. JSON schema validation in document databases is used to:
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90. Microservices architectures influence data modeling by:
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91. Knowledge graphs combine:
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92. Data modeling for AI-driven data governance tools helps by:
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93. Event sourcing in data modeling involves:
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94. Data modeling for IoT (Internet of Things) requires:
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95. Using graph embeddings in data modeling refers to:
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96. Data Fabric architectures rely on modeling to:
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97. Vertical partitioning in physical modeling:
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98. Data anonymization techniques in data modeling:
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99. Applying graph constraints (like uniqueness of node labels) ensures:
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