Q91
Q91 How do MongoDB drivers handle BSON data types?
They automatically convert BSON to native language data types
They store BSON data types as strings
They require manual conversion of BSON to native data types
They do not support BSON data types
Q92
Q92 What is the purpose of the ObjectID in MongoDB?
To identify users
To encrypt documents
To uniquely identify documents within a collection
To store configuration settings
Q93
Q93 How do MongoDB drivers handle write concerns?
They ignore write concerns
They allow specification of write concerns at the database level
They automatically set write concerns to the highest level
Write concerns must be set manually on each query
Q94
Q94 In a MongoDB driver, how do you typically establish a connection to a database?
Using a connection string
By specifying the IP address and port of the server
Through a graphical user interface
By sending a request to the MongoDB API
Q95
Q95 How can you perform a transaction across multiple documents using MongoDB drivers?
By using the db.transaction() method
By enabling transaction mode on the database
By using the startTransaction method on a session
Transactions are not supported in MongoDB
Q96
Q96 In MongoDB's Node.js driver, how do you handle streaming large amounts of data from a collection?
Using the .stream() method on a cursor
Using the .pipe() method on a cursor
Using the .largeData() method on a collection
Streaming is not supported in MongoDB
Q97
Q97 What might cause a timeout error when connecting to MongoDB through a driver?
Incorrect connection string
Network issues
Outdated driver version
Database service unavailability
Q98
Q98 If an application using a MongoDB driver suddenly starts experiencing slow queries, what could be a primary reason?
Changes in the database schema
Increased load on the database server
Inefficient indexes
Suboptimal query patterns
Q99
Q99 How does MongoDB handle large-scale data analytics primarily?
By using MapReduce functions
Through automatic data sharding
By integrating with external big data tools
Optimizing query execution
Q100
Q100 What is the advantage of MongoDB's flexible schema in the context of big data?
Simplifies data migration
Allows for varied data types and structures
Enhances data security
Improves query speed
Q101
Q101 How do MongoDB's aggregation pipeline and sharding capabilities benefit big data processing?
They increase data processing speed and efficiency
They reduce the need for data normalization
They automate data backup processes
They enhance data encryption
Q102
Q102 In what way does MongoDB's distributed architecture impact big data applications?
It limits the size of data that can be processed
It enhances data processing speed and scalability
It increases data redundancy
It complicates data analysis
Q103
Q103 How can you efficiently query large datasets in MongoDB?
Using indexed fields in queries
Performing full collection scans
Increasing the server's memory capacity
Querying only during off-peak hours
Q104
Q104 What is the best practice for performing complex aggregations on large datasets in MongoDB?
Using a single complex aggregation query
Breaking down the query into multiple stages in an aggregation pipeline
Increasing read preference settings
Replicating the data across more servers
Q105
Q105 What is a common reason for slow query performance in MongoDB when working with big data?
Unindexed queries
Hardware limitations
Network bottlenecks
Data fragmentation
Q106
Q106 In a MongoDB big data application, what is a frequent cause of inconsistent data aggregation results?
Improper sharding keys
Out-of-date read replicas
Incorrect aggregation pipeline stages
Mismatched data formats
Q107
Q107 What is the primary purpose of taking backups in MongoDB?
To improve database performance
To increase storage space
To protect against data loss
To enhance query speeds
Q108
Q108 What is a snapshot backup in MongoDB?
A backup of only the changes made since the last backup
A real-time backup of the database
A full copy of the database at a given point in time
An encrypted backup of the database
Q109
Q109 How does MongoDB ensure data consistency during backups?
By locking the database during backup
By using journaling
By creating read-only replicas
By stopping all write operations during backup
Q110
Q110 What is the difference between logical and physical backups in MongoDB?
Logical backups contain data and indexes; physical backups include data, indexes, and journal files
Logical backups are uncompressed; physical backups are compressed
Logical backups are for small data sets; physical backups are for large data sets
Logical backups are manual; physical backups are automatic
Q111
Q111 What is a critical consideration for choosing a backup strategy in MongoDB?
Data size and type
Frequency of data updates
Storage limitations
Cost of backup solutions
Q112
Q112 How do you create a backup of a MongoDB database using mongodump?
mongodump --db database_name
mongodb-backup --database database_name
backup-mongodb --db database_name
dump-mongo --database database_name
Q113
Q113 How can you restore a MongoDB database from a backup created with mongodump?
Using the mongorestore command with the backup file as an argument
Using the mongodb-restore command
By copying the backup files to the database directory
By importing the backup file using the MongoDB shell
Q114
Q114 If a mongorestore operation fails, what is a common cause?
Incorrect file permissions on the backup files
Network connectivity issues
Corrupt backup files
All of the above
Q115
Q115 What might be the reason for a backup not capturing recent data changes in MongoDB?
The backup process was not completed successfully
Recent data changes occurred after the backup was initiated
Journaling is disabled
The backup is from a secondary replica
Q116
Q116 What is the primary purpose of MongoDB's Performance Advisor?
To automatically fix performance issues
To provide real-time monitoring
To recommend indexes based on query patterns
To manage database configurations
Q117
Q117 How does the WiredTiger storage engine in MongoDB enhance performance?
By compressing data
By using a document-level locking mechanism
By automatically sharding data
By replicating data across multiple nodes
Q118
Q118 What role do MongoDB's read and write concerns play in performance tuning?
They determine how data is distributed across clusters
They control the level of data consistency and durability
They manage the indexing strategy
They optimize query execution time
Q119
Q119 In what way does sharding impact MongoDB's performance?
It increases data redundancy
It enhances data processing speed and scalability
It simplifies data management
It reduces storage requirements
Q120
Q120 How do you enable slow query logging in MongoDB for queries taking longer than a specified threshold?
Set the slowOpThresholdMs parameter in the MongoDB configuration
Use the db.setProfilingLevel() method
Modify the query execution plan
Enable detailed logging for all operations
