Microsoft DP-600 Implementing Analytics Solutions Using Microsoft Fabric Exam Dumps and Practice Test Questions Set 9 Q121-135
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Question121:
You are designing a Cosmos DB solution for a global online retail platform. Each customer’s order history, shopping cart, and wishlists must be isolated, and queries will primarily filter by customer ID and order ID. Which partitioning strategy should you implement?
A) Partition by customer ID (high-cardinality key)
B) Partition by product category (low-cardinality key)
C) Single logical partition for all customers
D) Partition by order date
Answer:
A) Partition by customer ID (high-cardinality key)
Explanation:
For a global online retail platform, the correct partitioning strategy is critical to ensure high performance, scalability, and operational efficiency. Option A, partitioning by customer ID, uses a high-cardinality key to logically isolate each customer’s order history, shopping cart, and wishlist into separate logical partitions. High-cardinality partitioning ensures even workload distribution across physical partitions, preventing hotspots and ensuring predictable query performance. Queries filtered by customer ID and order ID are efficiently routed to a single logical partition, minimizing cross-partition scans, reducing request unit (RU) consumption, and improving latency. This approach enables real-time order processing, inventory management, personalized recommendations, and analytics.
Option B, partitioning by product category, is low-cardinality because multiple customers purchase items from the same category. Low-cardinality partitioning can create hotspots, uneven workload distribution, and inefficient query execution. Queries filtered by customer ID would require scanning multiple partitions, increasing latency, RU usage, and operational complexity.
Option C, a single logical partition for all customers, consolidates all operations into one partition, creating bottlenecks for reads and writes. High-concurrency situations, such as flash sales or peak shopping periods, would result in latency spikes, timeouts, and degraded user experience.
Option D, partitioning by order date, is low-cardinality because multiple orders may share the same date. Queries filtered by customer ID or order ID would require cross-partition scans, increasing latency, RU consumption, and operational overhead.
Partitioning by customer ID ensures balanced workload distribution, predictable performance, and operational scalability. Coupled with selective indexing on order ID, product IDs, and timestamps, this strategy supports real-time dashboards, analytics, high-throughput operations, and operational monitoring. This aligns with best practices for globally distributed e-commerce platforms requiring low-latency, high-concurrency, and reliable operations.
Question122:
You are designing a Cosmos DB solution for a global video streaming platform. Each user’s watch history, favorites, and playlist progress must remain consistent across multiple regions in real-time. Which replication and consistency strategy should you implement?
A) Single-region write with eventual consistency
B) Multi-region write with strong consistency
C) Single-region write with bounded staleness
D) Multi-region write with session consistency
Answer:
B) Multi-region write with strong consistency
Explanation:
For a global video streaming platform, maintaining real-time consistency of watch history, favorites, and playlist progress is essential for user experience, personalization, and operational reliability. Option B, multi-region write with strong consistency, guarantees linearizability across all regions. Every read reflects the most recent committed write globally, ensuring that users in any region see accurate watch history, favorites, and playlist progress. Strong consistency prevents discrepancies, missing updates, or conflicting states, which is critical during high-concurrency scenarios such as popular show releases or live streaming events.
Option A, single-region write with eventual consistency, allows temporary discrepancies between regions. Users may see outdated watch history, incomplete favorites, or inconsistent playlist progress, resulting in poor personalization and user experience. Eventual consistency improves throughput but cannot guarantee real-time correctness for critical user data.
Option C, single-region write with bounded staleness, restricts replication lag to a predictable interval. Even minimal delays can cause inconsistencies in watch history or playlist progress, which negatively affects operational reliability and user satisfaction. Bounded staleness does not ensure instantaneous global correctness, making it unsuitable for high-concurrency, real-time streaming workloads.
Option D, multi-region write with session consistency, guarantees correctness only within a single session. Users in different sessions may encounter inconsistent data, potentially causing operational errors, incorrect recommendations, and degraded user experience. Session consistency is suitable for session-specific operations but inadequate for globally distributed streaming systems requiring consistent user state.
Strong consistency across multiple write regions ensures operational reliability, accurate tracking of user activity, and predictable system behavior. Although slightly higher write latency and coordination overhead exist, this strategy guarantees correctness, high-concurrency support, and operational integrity, making it the optimal solution for globally distributed video streaming services.
Question123:
You are designing a Cosmos DB solution for a global online learning platform. Each student’s course enrollments, assignments, and grades must be isolated, and queries will primarily filter by student ID and course ID. Which partitioning strategy should you implement?
A) Partition by student ID (high-cardinality key)
B) Partition by course ID (low-cardinality key)
C) Single logical partition for all students
D) Partition by enrollment date
Answer:
A) Partition by student ID (high-cardinality key)
Explanation:
For a global online learning platform, selecting an appropriate partitioning strategy is essential to maintain performance, scalability, and operational efficiency. Option A, partitioning by student ID, leverages a high-cardinality key to isolate each student’s course enrollments, assignments, and grades into separate logical partitions. High-cardinality partitioning ensures even distribution of workload across physical partitions, preventing hotspots and ensuring predictable query performance. Queries filtered by student ID and course ID are routed to a single logical partition, minimizing cross-partition scans, reducing RU consumption, and improving latency for real-time analytics, dashboards, and personalized learning recommendations.
Option B, partitioning by course ID, is low-cardinality because multiple students enroll in the same course. Low-cardinality partitioning can cause hotspots, uneven workload distribution, and inefficient query execution. Queries filtered by student ID would require scanning multiple partitions, increasing latency, RU usage, and operational complexity.
Option C, a single logical partition for all students, consolidates all operations into one partition, creating bottlenecks for reads and writes. High-concurrency situations, such as simultaneous assignment submissions or quiz attempts, would result in latency spikes, timeouts, and degraded system performance.
Option D, partitioning by enrollment date, is low-cardinality because multiple students may enroll on the same date. Queries filtered by student ID or course ID would require scanning multiple partitions, increasing latency, RU consumption, and operational overhead.
Partitioning by student ID ensures balanced workload distribution, predictable performance, and operational scalability. Combined with selective indexing on course ID, assignment IDs, and timestamps, this strategy supports real-time dashboards, analytics, high-throughput operations, and operational monitoring. This aligns with best practices for globally distributed online learning platforms requiring low-latency, high-concurrency, and reliable operations.
Question124:
You are designing a Cosmos DB solution for a global social networking platform. Each post, comment, and reaction must be isolated per post, and queries will primarily filter by post ID and timestamp. Which partitioning strategy should you implement?
A) Partition by post ID (high-cardinality key)
B) Partition by content type (low-cardinality key)
C) Single logical partition for all posts
D) Partition by creation date (low-cardinality key)
Answer:
A) Partition by post ID (high-cardinality key)
Explanation:
For a globally distributed social networking platform, choosing the correct partitioning strategy is vital for performance, scalability, and operational efficiency. Option A, partitioning by post ID, uses a high-cardinality key to logically isolate each post’s comments, reactions, and metadata into separate logical partitions. High-cardinality partitioning ensures even distribution across physical partitions, preventing hotspots, minimizing latency, and optimizing RU consumption. Queries filtered by post ID and timestamp are routed to a single logical partition, reducing cross-partition scans, operational overhead, and ensuring responsive real-time interactions, notifications, analytics, and content moderation.
Option B, partitioning by content type, is low-cardinality because multiple posts share the same type, such as text, image, or video. Low-cardinality partitioning may lead to uneven workload distribution, hotspots, and inefficient query performance. Queries filtered by post ID require scanning multiple partitions, increasing latency and RU consumption.
Option C, a single logical partition for all posts, consolidates all operations into one partition, creating bottlenecks for reads and writes. High-concurrency scenarios, such as viral posts with thousands of reactions or comments, would result in latency spikes, timeouts, and degraded user experience.
Option D, partitioning by creation date, is low-cardinality because multiple posts may share the same timestamp. Queries filtered by post ID would require cross-partition scans, increasing latency, RU usage, and operational complexity.
Partitioning by post ID ensures balanced workload distribution, predictable performance, and operational scalability. Coupled with selective indexing on timestamps, reactions, and engagement metrics, this approach supports real-time notifications, analytics, and high-concurrency operations. This aligns with best practices for globally distributed social networking platforms requiring low-latency, high-throughput, and reliable operations.
Question125:
You are designing a Cosmos DB solution for a global online multiplayer game. Each player’s progress, inventory, and achievements must remain consistent across regions in real-time. Which replication and consistency strategy should you implement?
A) Single-region write with eventual consistency
B) Multi-region write with strong consistency
C) Single-region write with bounded staleness
D) Multi-region write with session consistency
Answer:
B) Multi-region write with strong consistency
Explanation:
For a globally distributed online multiplayer game, ensuring real-time consistency of player progress, inventory, and achievements is critical for operational reliability, fairness, and user experience. Option B, multi-region write with strong consistency, guarantees linearizability across all regions. Every read reflects the most recent committed write globally, ensuring players in any region see accurate game states simultaneously. Strong consistency prevents conflicts, lost rewards, or duplicated achievements, which is vital during high-concurrency events, competitions, or collaborative gameplay sessions.
Option A, single-region write with eventual consistency, allows temporary discrepancies between regions. Players may see outdated progress, missing inventory items, or inconsistent achievements, leading to frustration, operational errors, and degraded user experience.
Option C, single-region write with bounded staleness, restricts replication lag to a predictable interval. Even minor delays can cause inconsistencies in player progress or inventory, compromising operational reliability. Bounded staleness cannot guarantee instantaneous global correctness, making it unsuitable for real-time, high-concurrency gaming workloads.
Option D, multi-region write with session consistency, guarantees correctness only within a single session. Players in separate sessions may encounter inconsistent progress, inventory, or achievements, resulting in operational errors, disputes, and negative gameplay experiences. Session consistency is suitable for session-specific operations but insufficient for globally distributed, real-time gaming systems.
Strong consistency across multiple write regions ensures operational reliability, accurate tracking of progress and inventory, and predictable system behavior. While it introduces slightly higher write latency and coordination overhead, this approach guarantees correctness, high-concurrency support, and system integrity, making it the optimal strategy for globally distributed online multiplayer games.
Question126:
You are designing a Cosmos DB solution for a global online grocery delivery platform. Each customer’s shopping cart, order history, and delivery preferences must be isolated, and queries will primarily filter by customer ID and order ID. Which partitioning strategy should you implement?
A) Partition by customer ID (high-cardinality key)
B) Partition by product category (low-cardinality key)
C) Single logical partition for all customers
D) Partition by order date
Answer:
A) Partition by customer ID (high-cardinality key)
Explanation:
For a globally distributed online grocery delivery platform, selecting the correct partitioning strategy is crucial to maintain high performance, scalability, and operational reliability. Option A, partitioning by customer ID, leverages a high-cardinality key to isolate each customer’s shopping cart, order history, and delivery preferences into separate logical partitions. High-cardinality partitioning ensures even distribution of data across physical partitions, preventing hotspots and ensuring predictable query performance. Queries filtered by customer ID and order ID are efficiently routed to a single logical partition, minimizing cross-partition scans, reducing request unit (RU) consumption, and improving latency.
Option B, partitioning by product category, is low-cardinality because multiple customers purchase items in the same category. Low-cardinality partitioning can create hotspots, uneven workload distribution, and inefficient queries. Queries filtered by customer ID would require scanning multiple partitions, increasing latency and RU usage.
Option C, a single logical partition for all customers, consolidates all operations into one partition, creating bottlenecks for reads and writes. High-concurrency situations, such as peak shopping hours or holiday sales, would result in latency spikes, timeouts, and degraded user experience.
Option D, partitioning by order date, is low-cardinality because multiple orders may share the same timestamp. Queries filtered by customer ID or order ID would require cross-partition scans, increasing latency, RU consumption, and operational complexity.
Partitioning by customer ID ensures balanced workload distribution, predictable performance, and operational scalability. Combined with selective indexing on order ID, product IDs, and delivery timestamps, this strategy supports real-time dashboards, analytics, high-throughput operations, and operational monitoring. This aligns with best practices for globally distributed grocery delivery platforms requiring low-latency, high-concurrency, and reliable operations.
Question127:
You are designing a Cosmos DB solution for a global e-learning platform. Each student’s course progress, assessments, and grades must remain consistent across regions in real-time. Which replication and consistency strategy should you implement?
A) Single-region write with eventual consistency
B) Multi-region write with strong consistency
C) Single-region write with bounded staleness
D) Multi-region write with session consistency
Answer:
B) Multi-region write with strong consistency
Explanation:
For a globally distributed e-learning platform, ensuring real-time consistency of student course progress, assessments, and grades is critical to maintain operational reliability, academic integrity, and user experience. Option B, multi-region write with strong consistency, guarantees linearizability across all regions. Every read reflects the most recent committed write globally, ensuring that students in any region see the same course progress, assessment results, and grades simultaneously. Strong consistency prevents inconsistencies, lost submissions, or conflicting grade updates, which is essential during high-concurrency scenarios such as quizzes, assignments, or live interactive classes.
Option A, single-region write with eventual consistency, allows temporary inconsistencies between regions. Students in other regions may observe outdated grades, progress, or course content, negatively affecting learning outcomes and operational reliability. Eventual consistency improves throughput but cannot guarantee correctness for critical transactional data.
Option C, single-region write with bounded staleness, restricts replication lag to a predictable interval. Even minor delays can cause discrepancies in progress tracking or grade submissions. Bounded staleness does not ensure instantaneous global correctness, making it insufficient for high-concurrency e-learning environments.
Option D, multi-region write with session consistency, guarantees correctness only within a single session. Students in different sessions may encounter inconsistent course data, grades, or progress metrics, leading to operational errors, confusion, and degraded learning experience. Session consistency is suitable for session-specific operations but inadequate for globally distributed real-time e-learning systems.
Strong consistency across multiple write regions ensures operational reliability, accurate tracking of progress and grades, and predictable system behavior. While it introduces slightly higher write latency and coordination overhead, this strategy guarantees correctness, high-concurrency support, and operational integrity, making it optimal for globally distributed e-learning platforms.
Question128:
You are designing a Cosmos DB solution for a global social commerce platform. Each user’s posts, comments, and purchase interactions must be isolated per user, and queries will primarily filter by user ID and post ID. Which partitioning strategy should you implement?
A) Partition by user ID (high-cardinality key)
B) Partition by post category (low-cardinality key)
C) Single logical partition for all users
D) Partition by post creation date
Answer:
A) Partition by user ID (high-cardinality key)
Explanation:
For a globally distributed social commerce platform, selecting the correct partitioning strategy is critical for performance, scalability, and operational efficiency. Option A, partitioning by user ID, uses a high-cardinality key to isolate each user’s posts, comments, and purchase interactions into separate logical partitions. High-cardinality partitioning ensures even distribution of data across physical partitions, preventing hotspots and ensuring predictable query performance. Queries filtered by user ID and post ID are efficiently routed to a single logical partition, minimizing cross-partition scans, reducing request unit (RU) consumption, and improving latency. This enables real-time analytics, content personalization, and operational efficiency even under high-concurrency workloads.
Option B, partitioning by post category, is low-cardinality because multiple users’ posts may share the same category. Low-cardinality partitioning can lead to hotspots, uneven workload distribution, and inefficient queries. Queries filtered by user ID require scanning multiple partitions, increasing latency and operational complexity.
Option C, a single logical partition for all users, consolidates all operations into one partition, creating bottlenecks for reads and writes. High-concurrency scenarios, such as simultaneous posting or commenting, would result in latency spikes, timeouts, and degraded user experience.
Option D, partitioning by post creation date, is low-cardinality because multiple posts may share the same timestamp. Queries filtered by user ID or post ID would require cross-partition scans, increasing latency, RU consumption, and operational complexity.
Partitioning by user ID ensures balanced workload distribution, predictable performance, and operational scalability. Coupled with selective indexing on post ID, engagement metrics, and timestamps, this approach supports real-time dashboards, analytics, and high-concurrency operations. This aligns with best practices for globally distributed social commerce platforms requiring low-latency, high-throughput, and reliable operations.
Question129:
You are designing a Cosmos DB solution for a global online event management platform. Each event’s ticket inventory, attendee registrations, and payment transactions must remain consistent across regions in real-time. Which replication and consistency strategy should you implement?
A) Single-region write with eventual consistency
B) Multi-region write with strong consistency
C) Single-region write with bounded staleness
D) Multi-region write with session consistency
Answer:
B) Multi-region write with strong consistency
Explanation:
For a globally distributed online event management platform, ensuring real-time consistency of event ticket inventory, attendee registrations, and payment transactions is critical to maintain operational integrity, user trust, and financial accuracy. Option B, multi-region write with strong consistency, guarantees linearizability across all regions. Every read reflects the most recent committed write globally, ensuring that users in any region see accurate ticket availability, registrations, and payment statuses simultaneously. Strong consistency prevents double bookings, conflicting registrations, or payment discrepancies, which is vital during high-concurrency scenarios such as popular concerts, sports events, or live conferences.
Option A, single-region write with eventual consistency, allows temporary discrepancies between regions. Users in other regions may observe outdated inventory, leading to overbooking, failed payments, and operational conflicts. Eventual consistency improves throughput but cannot guarantee real-time correctness for critical transactional data.
Option C, single-region write with bounded staleness, restricts replication lag to a predictable interval. Even minimal delays can cause inconsistencies in ticket allocation or registration updates. Bounded staleness cannot ensure instantaneous global correctness, making it unsuitable for high-concurrency ticketing operations.
Option D, multi-region write with session consistency, guarantees correctness only within a single session. Users in separate sessions may experience conflicting ticket availability, inconsistent registrations, or partial payment updates, resulting in operational errors, financial loss, and degraded user trust. Session consistency is suitable for session-specific operations but inadequate for globally distributed, real-time transactional systems.
Strong consistency across multiple write regions ensures operational reliability, accurate inventory and registration management, and predictable system behavior. While slightly increasing write latency and coordination overhead, this strategy guarantees correctness, high-concurrency support, and operational integrity, making it optimal for globally distributed online event management platforms.
Question130:
You are designing a Cosmos DB solution for a global online multiplayer gaming platform. Each player’s progress, in-game inventory, and achievements must remain consistent across regions in real-time. Which replication and consistency strategy should you implement?
A) Single-region write with eventual consistency
B) Multi-region write with strong consistency
C) Single-region write with bounded staleness
D) Multi-region write with session consistency
Answer:
B) Multi-region write with strong consistency
Explanation:
For a globally distributed online multiplayer gaming platform, maintaining real-time consistency of player progress, in-game inventory, and achievements is critical for operational reliability, fairness, and user experience. Option B, multi-region write with strong consistency, guarantees linearizability across all regions. Every read reflects the most recent committed write globally, ensuring players in any region see accurate game states simultaneously. Strong consistency prevents conflicts, lost rewards, or duplicated achievements, which is essential during high-concurrency events, tournaments, or collaborative gameplay sessions.
Option A, single-region write with eventual consistency, allows temporary discrepancies between regions. Players may encounter outdated progress, missing inventory items, or inconsistent achievements, which can degrade user experience and operational reliability.
Option C, single-region write with bounded staleness, restricts replication lag to a predictable interval. Even minor delays can cause inconsistencies in player progress or inventory, impacting gameplay fairness and operational accuracy. Bounded staleness cannot guarantee instantaneous global correctness, making it unsuitable for real-time, high-concurrency gaming environments.
Option D, multi-region write with session consistency, guarantees correctness only within a single session. Players in separate sessions may see inconsistent progress, inventory, or achievements, leading to operational errors, disputes, and reduced engagement. Session consistency is suitable for session-specific data but inadequate for globally distributed multiplayer gaming platforms.
Strong consistency across multiple write regions ensures operational reliability, accurate tracking of progress and inventory, and predictable system behavior. Although it introduces slightly higher write latency and coordination overhead, this strategy guarantees correctness, high-concurrency support, and system integrity, making it the optimal choice for globally distributed online multiplayer games.
Question131
A global logistics company is building a Cosmos DB–based solution to manage driver delivery routes, real-time vehicle tracking, delivery confirmations, and customer notifications. The system must ensure predictable read latency for drivers regardless of location, while guaranteeing that each driver always reads their own most recent updates even during high-volume delivery windows. Which consistency level should you implement?
A) Strong consistency
B) Bounded staleness consistency
C) Session consistency
D) Eventual consistency
Answer:
C) Session consistency
Explanation:
Session consistency is the most appropriate choice when designing a globally distributed logistics platform where each driver or delivery agent must always read their own latest updates but global absolute ordering is not required. The platform described must ensure predictable read performance across all regions, including during peak delivery windows when data generation is extremely high, and needs to avoid the high latency overhead associated with the strictest forms of replication. Session consistency ensures that any client operating under a session token always sees reads that reflect the latest writes performed within the same session. This means a driver updating their delivery route, scanning a package, marking a delivery complete, or confirming location updates will always see the latest version of their own data immediately, which is essential for operational accuracy and real-time workflow reliability.
Option A, strong consistency, enforces global linearizability and requires that all regions agree on the exact order of writes before any region can perform a read. Although this guarantees absolute correctness, it introduces higher latency, especially across geographically distributed read regions. Given the scale at which logistics companies operate and the constant movement of delivery agents across regions, strong consistency is likely too restrictive and costly. It may degrade performance significantly during peak hours when large amounts of write activity occur from drivers performing thousands of updates simultaneously. Strong consistency also reduces throughput by requiring coordination across multiple replicas, increasing RU consumption and write latency.
Option B, bounded staleness, allows reads to lag behind writes by a fixed number of versions or a fixed time interval. While this may provide predictable windows of stated delays, it is not acceptable when drivers must always see their latest updates. A driver marking a delivery complete and then seeing the old status moments later can cause operational confusion, duplicated actions, incorrect routing decisions, and inaccuracies in customer notifications. Bounded staleness reduces strictness compared to strong consistency but still risks presenting outdated state to the same actor who performed the update.
Option D, eventual consistency, is the most relaxed model and provides no guarantee on how quickly a write becomes visible to readers. This might be acceptable for customer dashboards or long-term analytics, but it is unsuitable for delivery agents who rely on real-time updates to operate efficiently. If a driver completes a delivery and the system still shows it as pending because replication is delayed, customer notifications may be wrong, dispatchers may assume tasks are incomplete, and routing calculations may become inaccurate. Eventual consistency is ideal for cases where freshness is not mandatory, but not for time-critical operational logistics workflows.
Session consistency ensures that each driver always reads their own latest updates while maintaining high throughput, predictable latency, and low replication delays. This model is optimized for user-centric applications where each actor interacts with their own data in a real-time environment and global ordering is not essential. This makes it the best choice for a large-scale logistics system where operational accuracy and real-time responsiveness must coexist with global performance and scalability.
Question132
A retail chain is designing a Cosmos DB solution to store customer purchase histories, product recommendations, and loyalty reward transactions. The company wants to reduce RU consumption while ensuring that only frequently queried properties are indexed and all other properties remain unindexed. What indexing strategy should the company implement?
A) Use a custom indexing policy that includes only required paths
B) Use automatic indexing for all properties
C) Disable indexing entirely
D) Use spatial indexing for all data
Answer:
A) Use a custom indexing policy that includes only required paths
Explanation:
In a retail environment where customer purchase histories, recommendations, and loyalty reward transactions are continuously ingested, indexing strategy plays a major role in determining RU consumption and query performance. A custom indexing policy that includes only required paths provides the optimal balance between cost efficiency and performance. By indexing only the properties that are frequently used in the query workload—such as customer ID, transaction timestamps, product identifiers, reward point totals, and recommendation metadata—the system minimizes the indexing overhead while still supporting efficient lookups.
Option B, automatic indexing for all properties, increases RU consumption significantly because Cosmos DB indexes every property automatically. For large retail datasets, this can be extremely costly, especially when numerous fields are rarely queried, such as embedded analytics metadata, rarely used attributes, internal fraud scoring values, or historical tags. While automatic indexing provides maximum query flexibility, it unnecessarily increases storage overhead and write costs in scenarios where only a subset of fields is relevant to most queries.
Option C, disabling indexing entirely, drastically reduces RU consumption for writes but breaks almost all query capabilities except point reads based on the item ID. Retail systems often rely heavily on queries filtering by customer ID, purchase date, product ID, loyalty status, and reward-related metrics. Without indexing, even simple queries become expensive because they trigger full scans across partitions. This creates bottlenecks, increases RU costs for reads, and lowers performance, making it an impractical approach for customer-driven retail data.
Option D, spatial indexing for all data, is irrelevant to most retail use cases unless location-based searches are a primary workload. Applying spatial indexing where it is not required wastes storage and computational overhead. Retail purchase histories and loyalty records do not typically require geospatial lookups such as proximity searches or polygon matching. Therefore, enabling spatial indexing for all data introduces cost without value.
Custom indexing empowers the retail chain to optimize for the fields that matter most to query performance while excluding rarely used properties. This strategy supports scalable real-time analytics, customer personalization, and loyalty reward tracking efficiently. It preserves operational throughput and minimizes RU cost, making it the ideal choice for retail environments where daily ingestions are large and query workloads are predictable.
Question133
A large finance corporation uses Cosmos DB to store transaction records, fraud scoring results, and customer activity logs. They must guarantee that no operations exceed the allocated RU budget during peak hours. Which configuration should be applied to ensure that the account never exceeds a predefined RU limit?
A) Use autoscale with a maximum RU limit
B) Use manual throughput
C) Use burst capacity
D) Use multi-region writes without limits
Answer:
A) Use autoscale with a maximum RU limit
Explanation:
Autoscale with a maximum RU limit provides precise control over the system’s maximum possible RU consumption. In financial environments where cost control, predictable billing, and operational reliability are critical, autoscale ensures that throughput dynamically adjusts based on the workload while never exceeding the maximum RU ceiling defined by the organization. This is vital for finance companies that experience unpredictable or spiking workloads during market fluctuations, fraud detection cycles, and high-volume transaction periods. Autoscale prevents budget overruns and ensures predictable operational expense.
Option B, manual throughput, requires fixed allocation of RU regardless of workload variation. Although it ensures cost predictability, it cannot scale automatically during spikes. If traffic exceeds manually allocated throughput, requests are throttled. For finance workloads where transaction ingestion and fraud scoring must be real-time, throttling can cause delays, data loss risk, and operational bottlenecks. Manual throughput is too rigid for dynamic financial workloads.
Option C, burst capacity, allows temporary RU increases beyond provisioned levels, which directly contradicts the requirement of never exceeding a predefined RU limit. Burst capacity is useful for handling brief spikes in low-intensity environments but does not guarantee cost ceilings. Finance corporations cannot risk unexpected billing increases due to burst operations.
Option D, multi-region writes without limits, increases throughput and global performance but does not address RU budget constraints. Multi-region writes can significantly increase RU consumption during concurrent global operations. Without setting a maximum cap, costs can escalate sharply during high-volume trading or when fraud scoring algorithms operate on large transaction sets.
Autoscale with a maximum RU limit ensures that the finance corporation receives the performance needed to support critical workloads while guaranteeing that operational cost remains predictable under all conditions. It prevents unexpected charges and protects against throttling by scaling appropriately.
Question134
A global travel booking platform stores itinerary documents containing flights, hotels, payment statuses, and customer preferences. Queries frequently filter by booking ID and customer email. The company wants to optimize query performance and RU consumption while supporting millions of active users. Which partition key strategy should be selected?
A) Partition by customer email
B) Partition by booking ID
C) Partition by travel destination
D) Partition by departure date
Answer:
A) Partition by customer email
Explanation:
Partitioning by customer email provides a high-cardinality, evenly distributed partitioning scheme suitable for large-scale travel booking platforms. Each customer may have multiple itineraries, but the distribution across emails is wide enough that workloads remain balanced. Queries filtering by booking ID and customer email become efficiently routed to the correct logical partition, minimizing cross-partition scans and reducing RU consumption. Travel platforms require low-latency responses for itinerary lookups, modifications, payment updates, and customer notifications, and partitioning by email ensures consistent routing and optimized performance.
Option B, partitioning by booking ID, works initially but is problematic when a customer has multiple bookings. Each booking would be isolated in separate partitions, but queries filtering by customer email would require cross-partition scans. Since customer email is more frequently used for queries than booking ID, this approach leads to higher RU usage.
Option C, partitioning by travel destination, is low-cardinality and creates hotspots during peak seasons when many customers book popular locations. Heavy workloads may concentrate in single partitions, causing throttling, degraded performance, and uneven distribution.
Option D, partitioning by departure date, also creates hotspots because many users travel during peak seasons or holidays. Multiple bookings would share the same departure date, resulting in uneven partition distribution, poor scalability, and high RU consumption for queries filtered by customer email or booking ID.
Partitioning by customer email ensures scalability, efficiency, and predictable query performance for a globally distributed travel booking platform.
Question135
A global sports analytics company uses Cosmos DB to store athlete performance data, historical statistics, event metadata, and team information. Analysts frequently run queries combining athlete ID, event ID, and time-based filters. The company needs fast analytical queries while keeping storage cost low. What indexing strategy should be implemented?
A) Index only athlete ID and event ID while excluding time-based fields
B) Index all fields automatically
C) Use a custom indexing policy that includes athlete ID, event ID, and time-based fields
D) Disable indexing for historical records
Answer:
C) Use a custom indexing policy that includes athlete ID, event ID, and time-based fields
Explanation:
A custom indexing policy including athlete ID, event ID, and time-based fields optimizes analytical queries while minimizing storage cost. Analysts querying performance data often filter by these three major dimensions. Including them ensures fast seeks, reduced RU consumption, and predictable results. Excluding irrelevant fields further lowers indexing overhead and reduces storage cost.
Option A excludes time-based fields, which are essential for sports analytics. Time-series queries would become expensive and slow, requiring cross-partition scans.
Option B indexes all fields automatically, which drastically increases storage cost without improving performance proportional to cost. Unneeded fields like embedded metadata or extended attributes increase RU consumption for writes.
Option D disables indexing for historical records, making analytical queries extremely expensive because historical data is often needed for comparison, trend analysis, and performance modeling. Without indexing, large-scale scans severely degrade performance.
Custom indexing limited to athlete ID, event ID, and time filters offers the best balance between analytical speed and cost efficiency.
A custom indexing policy that selectively includes athlete ID, event ID, and time-based fields provides an optimized balance of analytical speed, predictable query performance, and storage efficiency. In large-scale sports analytics systems, data volumes can grow extremely quickly because each athlete generates numerous performance records across multiple events, locations, seasons, and training cycles. Not every attribute contributes equally to analytical insights. Indexing all fields is not only unnecessary but actively harmful when operating at scale. The goal is to ensure that the most common query paths—those involving athlete identity, event classification, and time dimensions—execute with minimal latency and cost. By explicitly choosing these fields for inclusion, the system ensures that RU consumption remains stable even as the dataset grows.
Sports performance analytics heavily relies on time-series data. Coaches, performance scientists, and analysts typically evaluate trends over time such as improvement rates, seasonal variation, fatigue patterns, recovery efficiency, and trajectory toward competition readiness. Time-based fields therefore serve as a foundational axis for filtering and sorting. Excluding such fields from indexing forces the database to perform large cross-partition scans whenever a query filters by a date range, training period, or season window. These scans not only raise RU consumption dramatically but can produce unpredictable spikes in latency that affect dashboards, automated scoring systems, or real-time analytics pipelines. A custom indexing policy including time information ensures that the system can quickly access specific slices of data without scanning entire athlete histories.
Athlete ID is another field that must be indexed because nearly all queries begin with identifying the athlete. Queries like retrieving performance summaries, examining splits, comparing times across competitions, and evaluating year-over-year progress are all anchored on the athlete identifier. An unindexed athlete ID field would create severe inefficiencies, requiring the database engine to scan large partitions of unrelated data before finding relevant records. Indexing athlete ID ensures direct path access, reducing request units and improving responsiveness for both interactive and bulk analytical workflows.
Event ID is equally critical because sports analytics often involve comparing an athlete’s performance across similar events. For example, analysts frequently evaluate consistency in certain distances, match types, weight classes, or specialized skill-based events. Indexing event ID allows queries to efficiently isolate performance data for the event type under analysis. Without this index, retrieving event-specific performance patterns would require repeatedly scanning disparate data ranges, making comparisons computationally expensive and slower as the dataset expands.
Compared with these essential fields, many additional attributes—such as venue metadata, environmental recordings, extended biometrics, sensor details, or auxiliary context fields—are not used in every query. These fields might be relevant for specialized deeper studies but do not benefit from indexing in between routine analytics workloads. Automatically indexing all fields adds unnecessary load because every write operation must compute and maintain index entries for each of those attributes. This increases RU consumption for inserts, updates, and replaces. In large systems with millions of daily writes, this unnecessary indexing results in significantly higher storage and operational cost. A custom indexing policy enables selective inclusion only of fields that meaningfully contribute to common query patterns.
Disabling indexing for historical records may initially sound appealing from a cost perspective, but it severely limits the ability to perform any retrospective or longitudinal analysis—two cornerstones of sports analytics. Historical data is indispensable for evaluating trends, modeling future outcomes, establishing benchmarks, and understanding peak performance windows. Without indexing, any query relying on older data becomes drastically more expensive, requiring full scans regardless of query filters. This undermines the fundamental role of historical insights in sports decision-making. It also makes automated analytics pipelines unpredictable because query performance becomes tightly coupled with dataset growth rather than indexed efficiency.
A custom indexing approach ensures that data retrieval remains consistent, predictable, and scalable even as the system ingests massive volumes of new performance records. This is especially important for real-world systems where analytics dashboards must remain responsive for coaches and analysts, often under time constraints or competitive environments. It also helps in optimizing cloud resource consumption. Indexing only the fields needed minimizes index size, thereby lowering stored index overhead across all partitions. This not only reduces storage costs but also reduces the computational burden of maintaining indexes during write operations.