With reference to the Spark Structured Streaming programming model, as illustrated in the figure below, the "Output" is defined as what gets written out to the external storage. The output can be defined in three different modes. 

Which of the following is true with reference to the aforementioned modes?

i) In Complete Mode, the entire updated Result Table will be written to the external storage. 

How should you avoid mutable, growing arrays in the following schema?

{
   name: "O'Reilly Media",
   founded: 1980,
   location: "CA",
   books: [12346789, 234567890, ...]
}
{
    _id: 123456789,
    title: "MongoDB: The Definitive Guide",
    author: [ "Kristina Chodorow", "Mike Dirolf" ],
    published_date: ISODate("2010-09-24"),
    pages: 216,
    language: "English"
}
{
   _id: 234567890,
   title: "50 Tips and Tricks for MongoDB Developer",
   author: "Kristina Chodorow",
   published_date: ISODate("2011-05-06"),
   pages: 68,
   language: "English"
} 

Given a customer collection that includes fields for gender and city, which aggregate pipeline shows the number of female customers in each city?

If the address data is frequently retrieved with the name information, how will you modify the following schema representing one to one relationship with referencing?

{
   _id: "joe",
   name: "Joe Bookreader"
}
{
   patron_id: "joe",
   street: "123 Fake Street",
   city: "Faketon",
   state: "MA",
   zip: "12345"
}

Following is the structure of the restaurant collection:

{
  "address": {
     "building": "1007",
     "coord": [ 144.946457, -37.840935 
],
     "street": "Monash University Bakery",
     "zipcode": "3800
"
  },
  "suburb": "Clayton",
  "cuisine": "Bakery",
  "grades": [
     { "date": { "$date": 1393804800000 }, "grade": "A", "score": 2 },
     { "date": { "$date": 1378857600000 }, "grade": "A", "score": 6 },
     { "date": { "$date": 1358985600000 }, "grade": "A", "score": 10 },
     { "date": { "$date": 1322006400000 }, "grade": "A", "score": 9 },
     { "date": { "$date": 1299715200000 }, "grade": "B", "score": 14 }
  ],
  "name": "Monash Bakery Shop",
  "_id": "317455"
}
Write a MongoDB query to display the fields -- 'name', 'suburb' and 'zipcode', but exclude the field '_id' for all the documents in the collection.

Following is the structure of the restaurant collection:

{
  "address": {
     "building": "1007",
     "coord": [ 144.946457, -37.840935 ],
     "street": "Monash University Bakery",
     "zipcode": "3800"
  },
  "suburb": "Clayton",
  "cuisine": "Bakery",
  "grades": [
     { "date": { "$date": 1241804700000 }, "grade": "C", "score": 1 },
     { "date": { "$date": 1224857600000 }, "grade": "A", "score": 5 },
     { "date": { "$date": 1261985600000 }, "grade": "B", "score": 9 },
     { "date": { "$date": 1201003200000 }, "grade": "A", "score": 8 },
     { "date": { "$date": 1137705200000 }, "grade": "B", "score": 13 }
  ],
  "name": "Monash Bakery Shop",
  "restaurant_id": "317455"
}
Write a MongoDB query to display the first 5 restaurant which is in the suburb Clayton.

Which of the following operations cannot be directly implemented in MongoDB using standard operations?

MongoDB stores data in which format, both internally and over the network?

Which of the following can not be used to reduce the query execution time in MongoDB ?

You have 100 documents in events collection, each containing an array attendees of 50 elements. 20 of those documents have at least one attendee with "VIP": true. Consider two approaches to count total "VIP" attendees across all events: