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The C100DBA exam is an online, proctored exam that consists of 60 multiple-choice questions. Candidates have 90 minutes to complete the exam and must achieve a score of at least 65% to pass. C100DBA Exam is available in English, Japanese, Simplified Chinese, and Traditional Chinese.
NEW QUESTION # 149
Which of the following aggregate commands in MongoDB uses a pipeline approach with the goals of improving the aggregation performance?
Answer: B
NEW QUESTION # 150
Consider a clothing collection that contains these documents:
{ " _id " : 1, " item " : " sweater " , " sizes " : [ " XS " , " S " ] }
{ " _id " : 2, " item " : " t-shirt " , " sizes " : [ " S " , " M " , " XL " ] }
{ " _id " : 3, " item " : " vest " , " sizes " : [ " M " , " L " , " XL " , " XXL " ] } What does the following query return?
db.clothing.find({ " sizes " : " M " })
Answer: B
Explanation:
MongoDB supports direct equality matching against elements contained in an array. When the predicate is { sizes: " M " } and sizes is an array field, a document matches when at least one element of the array equals the scalar value " M " . Document 1 contains only " XS " and " S " , so it does not match. Document 2 contains " M " in [ ' S ' , ' M ' , ' XL ' ], and document 3 contains " M " in [ ' M ' , ' L ' , ' XL ' , ' XXL ' ]; both therefore match. No $elemMatch expression is required for this simple scalar membership test. $elemMatch becomes important when multiple conditions must be satisfied by the same array element, particularly for arrays of embedded documents or compound conditions. This array equality behavior is a core part of MongoDB ' s document query model and allows concise filtering of array-valued fields. The correct result is consequently the t-shirt and vest documents, which corresponds to option D. This same matching behavior applies consistently whenever a scalar predicate targets values stored inside a BSON array field.
Study Guide reference/topic:MongoDB Architecture and Core Operations - querying array fields and equality predicates.
NEW QUESTION # 151
What does the output x of the following MongoDB aggregation query result into; db.posts.aggregate( [ { $group: { _id; "$author", x: { $sum: $likes } } } ] )
Answer: A
NEW QUESTION # 152
What are TWO functions of sharding?
Answer: B,E
Explanation:
Sharding is MongoDB ' s horizontal-scaling mechanism: it partitions a collection ' s data across multiple shards so a deployment can scale storage capacity and distribute query or write workload beyond the limits of one server. That directly supports deployments with very large datasets and high-throughput operations, making C correct. MongoDB also supports zone sharding, which associates ranges of shard-key values with selected shards. Zones can be used to keep particular data near a geographical region, satisfy locality requirements, or map data ranges to hardware tiers, so A is also correct. Option B overstates what sharding itself provides; cross-region consistency behavior depends on replication topology, read/write concerns, latency, and application design rather than simply enabling sharding. Option D describes a deployment that already fits comfortably in memory and meets performance goals, so sharding may add unnecessary operational complexity. Option E primarily describes replication: replica sets maintain redundant copies, provide elections, and improve availability. Production shards are themselves normally replica sets, but redundancy is a replication function rather than the defining purpose of sharding. Therefore the two functions represented here are geographic zone distribution and horizontal scale: A and C.
Study Guide reference/topic:Sharding Strategies and Scalable Deployments - horizontal scaling, large datasets, throughput, shard keys, and zone sharding.
NEW QUESTION # 153
You are comparing values of different BSON types in mongodb. You want to compare from lowest to highest.
Which comparison order is used?
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