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MongoDB C100DBA Exam Syllabus Topics:

SectionObjectives
Topic 1: Application Administration- Security and Access Control
  • 1. Role-Based Access Control
  • 2. User Management
  • 3. Authorization
  • 4. Authentication
Topic 2: Sharding- Cluster Scaling
  • 1. Query Routing
  • 2. Balancer Operations
  • 3. Chunk Distribution
  • 4. Shard Keys
Topic 3: Indexes- Index Management
  • 1. Index Selection
  • 2. Compound Indexes
  • 3. Query Performance Optimization
  • 4. Single Field Indexes
Topic 4: CRUD Operations- Data Manipulation
  • 1. Update Operations
  • 2. Delete Operations
  • 3. Insert Operations
  • 4. Read Queries
Topic 5: Philosophy and Features- MongoDB Architecture
  • 1. High Availability Concepts
  • 2. Document Model
  • 3. Scalability Concepts
  • 4. Collections and Databases
Topic 6: Replication- Replica Sets
  • 1. Write Concerns
  • 2. Primary and Secondary Nodes
  • 3. Elections
  • 4. Read Preferences
Topic 7: Server Administration- Database Operations
  • 1. Configuration Management
  • 2. Monitoring
  • 3. Backup and Restore
  • 4. Storage Management

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MongoDB Certified DBA Associate Exam (Based on MongoDB 4.4) Sample Questions (Q46-Q51):

NEW QUESTION # 46
Consider a contacts collection that contains these documents:
{ " _id " : 1, " name " : " Poppy " , " birthday " : " 03-09-1984 " }
{ " _id " : 2, " name " : " Ashley " , " birthday " : " 08-12-1990 " }
{ " _id " : 3, " name " : " Cleo " , " birthday " : " 11-02-1976 " }
What query returns the document where the field name is " Poppy " ?

Answer: C

Explanation:
MongoDB equality filters use the form { field: value }. To return the document whose name field contains the exact string " Poppy " , the query therefore uses { name: " Poppy " }, as shown in option D. This shorthand equality syntax is equivalent to explicitly using $eq. Option B uses the correct field but the value is lowercase
" poppy " . Normal MongoDB string equality is case-sensitive unless a suitable collation or another case- insensitive technique is deliberately configured, so it does not match the displayed " Poppy " value. Option A uses name.Poppy as a dot-notation path. Dot notation is for accessing a nested field inside an embedded document, but here name stores a scalar string and has no nested Poppy field. Option C searches for a field literally named Poppy, which the sample documents do not contain. This question tests the basic field/value predicate syntax used by find(). The correct command is db.contacts.find({ " name " : " Poppy " }), making option D correct.
Study Guide reference/topic:MongoDB Architecture and Core Operations - find(), equality predicates, string matching, and dot notation.


NEW QUESTION # 47
A collection named scores includes:
[
{ _id: 1, home_score: 6, away_score: [ 5, 7 ] },
{ _id: 2, home_score: [ 8, 6 ], away_score: 9 },
{ _id: 3, home_score: [ 5, 8 ], away_score: [ 9, 6 ] },
{ _id: 4, home_score: 5, away_score: 10 }
]
Given the following index:
db.scores.createIndex( { home_score: 1, away_score: 1 } )
What document will cause the index to fail?

Answer: C

Explanation:
The proposed index is a compound index across home_score and away_score. MongoDB supports compound multikey indexes, but it imposes an important restriction: for any one indexed document, no more than one field covered by the compound index may contain an array. Document 1 is valid because only away_score is an array. Document 2 is also valid because only home_score is an array. Document 4 contains scalar values for both fields. Document 3 contains arrays in both indexed fields: home_score: [5,8] and away_score: [9,6] .
Indexing that document would require a compound multikey index to expand two independent array dimensions, which MongoDB prohibits. Therefore document 3 prevents the compound index from being valid. The restriction is important because a single multikey field already creates multiple index keys per document; allowing multiple independently array-valued keys in the same compound index would create ambiguous and potentially combinatorial key combinations. DBAs designing indexes over document schemas with arrays must understand these multikey constraints before building compound indexes. The failing document is _id:3, making option D correct.
Study Guide reference/topic:Indexing and Query Optimization - multikey indexes, compound multikey restrictions, and array-valued fields.


NEW QUESTION # 48
Consider that you have a collection called population which has fields state and city. Which of the following query will calculate the population grouped by state and city?

Answer: C


NEW QUESTION # 49
What is the use of mongodump and mongorestore tools?

Answer: B


NEW QUESTION # 50
What operation updates the value of the unitsInStock field to 25 for a document with a " laptop " product, and inserts a new document if it does not exist?

Answer: D

Explanation:
An upsert combines update behavior with conditional insertion. updateOne() first searches for a document matching { product: " laptop " }. If one exists, $set changes unitsInStock to 25. If none exists and the options document contains { upsert: true }, MongoDB inserts a new document based on the equality portions of the filter together with the update result. Option A uses exactly this pattern. Options B and C incorrectly use insertOne(), which inserts a supplied document and does not accept an update-operator document plus upsert semantics in this form. Option D uses the correct method and update structure but specifies True with a capital T. Mongosh uses JavaScript Boolean syntax, so the literal must be lowercase true. Upserts are useful when an application needs to ensure that a logical entity exists after an operation, but DBAs should consider a unique index on the identifying field when duplicate creation by concurrent requests must be prevented.
Given the choices, updateOne(..., {$set:...}, {upsert:true}) is the valid operation, so A is correct.
Study Guide reference/topic:MongoDB Architecture and Core Operations - updateOne(), $set, upserts, and Boolean syntax.


NEW QUESTION # 51
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