AWS Certified Big Data - Specialty (BDS-C00)
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Free AWS Certified Big Data - Specialty (BDS-C00) Questions
1.
Your organization needs to analyze time-series sensor data identifying trends and seasonal patterns. What analytical approach reveals temporal patterns?
- Static aggregations only
- Ignore temporal patterns
- Manual pattern identification
- Time-series decomposition and forecasting with QuickSight ML or SageMaker
Explanation
Explanation
Time-series data often contains trends, seasonal effects, and cyclical patterns that cannot be fully captured by static aggregations. Analytical approaches like time-series decomposition and forecasting, using tools such as Amazon QuickSight ML Insights or Amazon SageMaker, allow organizations to automatically identify these temporal components, detect anomalies, and make predictions. This enables a deeper understanding of sensor data over time and supports data-driven decision-making.
Correct Answer Is:
Time-series decomposition and forecasting with QuickSight ML or SageMaker
Time-series data often contains trends, seasonal effects, and cyclical patterns that cannot be fully captured by static aggregations. Analytical approaches like time-series decomposition and forecasting, using tools such as Amazon QuickSight ML Insights or Amazon SageMaker, allow organizations to automatically identify these temporal components, detect anomalies, and make predictions. This enables a deeper understanding of sensor data over time and supports data-driven decision-making.
Correct Answer Is:
Time-series decomposition and forecasting with QuickSight ML or SageMaker
2.
Your data lake needs to prevent unauthorized data access while enabling analytics teams to discover available datasets. What governance framework balances security and accessibility?
- A) No access controls
- B) Lake Formation with tag-based access control and data catalog permissions
- C) Complete data lockdown
- D) Manual access requests only
Explanation
Explanation
AWS Lake Formation provides comprehensive data lake governance combining security with discoverability. Implement: (1) tag-based access control (LF-Tags) defining permissions based on data classification (e.g., PII, Department, Confidentiality), (2) fine-grained permissions (database, table, column, row-level), (3) Glue Data Catalog for metadata and discovery, (4) cross-account sharing with governed access, (5) audit logging via CloudTrail. Users discover datasets via Data Catalog but access only authorized data when querying. No controls risk data breaches. Complete lockdown prevents legitimate analytics. Manual requests create bottlenecks. Lake Formation enables: data stewards define permissions using business-relevant tags, analysts discover data self-service, queries automatically enforce permissions, compliance through audit trails. Essential for regulated industries requiring data democratization with governance, enabling analytics while maintaining security and compliance controls at scale across organizational data lakes.
Correct Answer Is:
C) Complete data lockdown
AWS Lake Formation provides comprehensive data lake governance combining security with discoverability. Implement: (1) tag-based access control (LF-Tags) defining permissions based on data classification (e.g., PII, Department, Confidentiality), (2) fine-grained permissions (database, table, column, row-level), (3) Glue Data Catalog for metadata and discovery, (4) cross-account sharing with governed access, (5) audit logging via CloudTrail. Users discover datasets via Data Catalog but access only authorized data when querying. No controls risk data breaches. Complete lockdown prevents legitimate analytics. Manual requests create bottlenecks. Lake Formation enables: data stewards define permissions using business-relevant tags, analysts discover data self-service, queries automatically enforce permissions, compliance through audit trails. Essential for regulated industries requiring data democratization with governance, enabling analytics while maintaining security and compliance controls at scale across organizational data lakes.
Correct Answer Is:
C) Complete data lockdown
3.
A data warehouse needs to serve both interactive queries and complex analytical workloads efficiently. What architecture balances these requirements?
- A) Single cluster optimized for one workload type
- B) Separate Redshift clusters or serverless workgroups for interactive vs analytical
- C) Run all workloads serially
- D) No workload separation
Explanation
Explanation
Separate workloads: (1) interactive cluster/workgroup: smaller, optimized for low latency, short queries, higher concurrency, (2) analytical cluster/workgroup: larger, optimized for complex queries, higher memory/CPU, longer query timeouts, (3) data sharing between clusters (producer-consumer) or shared S3 data (Spectrum). Interactive workload characteristics: millisecond-second latency, simple queries, many concurrent users. Analytical: minutes-hours execution, complex aggregations/joins, fewer concurrent users. Single cluster creates contention: complex queries delay interactive responses, interactive queries slow during analytical processing. WLM provides some isolation but separate clusters/workgroups provide stronger guarantees. Essential for mixed analytics environments where business users need responsive dashboards while data scientists run hour-long analytical queries, workload separation ensures consistent performance for both through dedicated resources optimized for each pattern.
Correct Answer Is:
B) Separate Redshift clusters or serverless workgroups for interactive vs analytical
Separate workloads: (1) interactive cluster/workgroup: smaller, optimized for low latency, short queries, higher concurrency, (2) analytical cluster/workgroup: larger, optimized for complex queries, higher memory/CPU, longer query timeouts, (3) data sharing between clusters (producer-consumer) or shared S3 data (Spectrum). Interactive workload characteristics: millisecond-second latency, simple queries, many concurrent users. Analytical: minutes-hours execution, complex aggregations/joins, fewer concurrent users. Single cluster creates contention: complex queries delay interactive responses, interactive queries slow during analytical processing. WLM provides some isolation but separate clusters/workgroups provide stronger guarantees. Essential for mixed analytics environments where business users need responsive dashboards while data scientists run hour-long analytical queries, workload separation ensures consistent performance for both through dedicated resources optimized for each pattern.
Correct Answer Is:
B) Separate Redshift clusters or serverless workgroups for interactive vs analytical
4.
A data warehouse needs to share query results with external partners securely without granting database access. What sharing approach maintains security?
- A) Grant database credentials to partners
- B) Public S3 bucket
- C) Email query results
- D) Athena query results in S3 with pre-signed URLs or S3 access points
Explanation
Explanation
Secure result sharing:
(1) Athena queries write results to S3 bucket,
(2) generate pre-signed URLs with expiration for time-limited access,
(3) S3 Access Points with partner-specific access policies,
(4) AWS DataExchange for formalized data sharing,
(5) encryption at rest (S3 SSE) and in transit (HTTPS).
Pre-signed URL: temporary URL granting access to specific object without AWS credentials, expiration configurable (hours to days).
S3 Access Points: separate access points per partner with different policies.
Alternative: trigger Lambda generating and emailing secure links on query completion.
Database credentials grant excessive access.
Email risks exposure.
Public buckets violate security.
Monitor: access logs (CloudTrail, S3 access logs) tracking who accessed what.
Essential for B2B data sharing where partners need query results without database access, secure sharing mechanisms provide time-limited, audited access to specific result sets without exposing underlying data warehouse or credentials.
Correct Answer
D) Athena query results in S3 with pre-signed URLs or S3 access points
Secure result sharing:
(1) Athena queries write results to S3 bucket,
(2) generate pre-signed URLs with expiration for time-limited access,
(3) S3 Access Points with partner-specific access policies,
(4) AWS DataExchange for formalized data sharing,
(5) encryption at rest (S3 SSE) and in transit (HTTPS).
Pre-signed URL: temporary URL granting access to specific object without AWS credentials, expiration configurable (hours to days).
S3 Access Points: separate access points per partner with different policies.
Alternative: trigger Lambda generating and emailing secure links on query completion.
Database credentials grant excessive access.
Email risks exposure.
Public buckets violate security.
Monitor: access logs (CloudTrail, S3 access logs) tracking who accessed what.
Essential for B2B data sharing where partners need query results without database access, secure sharing mechanisms provide time-limited, audited access to specific result sets without exposing underlying data warehouse or credentials.
Correct Answer
D) Athena query results in S3 with pre-signed URLs or S3 access points
5.
An organization needs multi-region disaster recovery for data warehouse with RTO <1 hour. What Redshift DR strategy meets this requirement?
- A) Manual cluster recreation in DR region
- B) Daily backups only
- C) No DR plan
- D) Cross-region snapshot copy with automated cluster restoration
Explanation
Explanation
Configure Redshift automated cross-region snapshot copy: enable automated snapshots (every 8 hours or configurable), configure cross-region copy to DR region, snapshots automatically replicate asynchronously. For disaster: restore cluster from latest snapshot in DR region (typically 30-45 minutes), update DNS/application endpoints, resume operations. Automate with Lambda monitoring primary region health and triggering restoration. Manual recreation exceeds RTO. No DR risks extended outage. Daily backups may exceed RPO (recovery point objective). Enhanced: maintain warm standby (running cluster in DR region with continuous data replication via Kinesis/Glue replicating incremental changes), failover in minutes but higher cost. For critical workloads: RA3 clusters with data sharing enabling rapid read-only access to production data from DR cluster. Essential for business-critical analytics where data warehouse downtime impacts operations requiring automated DR with tested recovery procedures meeting RTO/RPO SLAs.
Correct Answer Is:
D) Cross-region snapshot copy with automated cluster restoration
Configure Redshift automated cross-region snapshot copy: enable automated snapshots (every 8 hours or configurable), configure cross-region copy to DR region, snapshots automatically replicate asynchronously. For disaster: restore cluster from latest snapshot in DR region (typically 30-45 minutes), update DNS/application endpoints, resume operations. Automate with Lambda monitoring primary region health and triggering restoration. Manual recreation exceeds RTO. No DR risks extended outage. Daily backups may exceed RPO (recovery point objective). Enhanced: maintain warm standby (running cluster in DR region with continuous data replication via Kinesis/Glue replicating incremental changes), failover in minutes but higher cost. For critical workloads: RA3 clusters with data sharing enabling rapid read-only access to production data from DR cluster. Essential for business-critical analytics where data warehouse downtime impacts operations requiring automated DR with tested recovery procedures meeting RTO/RPO SLAs.
Correct Answer Is:
D) Cross-region snapshot copy with automated cluster restoration
6.
A streaming application needs to handle bursts of traffic exceeding normal capacity without data loss. What scaling approach handles traffic spikes?
- Kinesis Data Streams auto-scaling or on-demand mode
- Fixed capacity insufficient for bursts
- Drop data during bursts
- Manual capacity increases
Explanation
Explanation
To reliably absorb sudden spikes in streaming traffic, Kinesis Data Streams offers on-demand mode or automatic scaling mechanisms that dynamically increase capacity based on throughput needs. This ensures that the stream can handle bursty workloads without throttling or losing data. Unlike fixed or manually adjusted capacity, on-demand or auto-scaling seamlessly adapts to fluctuating volumes, making it ideal for unpredictable or highly variable traffic patterns.
Correct Answer Is:
Kinesis Data Streams auto-scaling or on-demand mode
To reliably absorb sudden spikes in streaming traffic, Kinesis Data Streams offers on-demand mode or automatic scaling mechanisms that dynamically increase capacity based on throughput needs. This ensures that the stream can handle bursty workloads without throttling or losing data. Unlike fixed or manually adjusted capacity, on-demand or auto-scaling seamlessly adapts to fluctuating volumes, making it ideal for unpredictable or highly variable traffic patterns.
Correct Answer Is:
Kinesis Data Streams auto-scaling or on-demand mode
7.
Your organization needs to enable data scientists to experiment with production data subsets without impacting production systems. What approach provides safe experimentation?
- A) Data lake zones (raw, dev, prod) with sampling or snapshots for experimentation
- B) Experiment directly in production
- C) No experimentation environment
- D) Copy entire production dataset
Explanation
Explanation
Enable safe experimentation: (1) multi-zone data lake architecture (raw, development, production zones) with access controls per zone, (2) provide development zone with data samples (10% random sample) or recent subsets for experimentation, (3) clone production datasets using snapshots or Redshift data sharing for analysis, (4) use separate compute resources (EMR clusters, Athena workgroups) for experimentation preventing production impact. Development zone: relaxed permissions, same schema as production, sample data reducing costs. Production experimentation risks: performance impact, accidental data modification, security exposure. Data sampling: Athena queries with TABLESAMPLE or systematic sampling (WHERE MOD(hash(id), 10) = 0). Essential for data science workflows requiring production-like data for model development where isolated experimentation environments protect production while providing realistic datasets enabling effective feature engineering and model development without production risks or copying entire petabyte datasets.
Correct Answer Is:
A) Data lake zones (raw, dev, prod) with sampling or snapshots for experimentation
Enable safe experimentation: (1) multi-zone data lake architecture (raw, development, production zones) with access controls per zone, (2) provide development zone with data samples (10% random sample) or recent subsets for experimentation, (3) clone production datasets using snapshots or Redshift data sharing for analysis, (4) use separate compute resources (EMR clusters, Athena workgroups) for experimentation preventing production impact. Development zone: relaxed permissions, same schema as production, sample data reducing costs. Production experimentation risks: performance impact, accidental data modification, security exposure. Data sampling: Athena queries with TABLESAMPLE or systematic sampling (WHERE MOD(hash(id), 10) = 0). Essential for data science workflows requiring production-like data for model development where isolated experimentation environments protect production while providing realistic datasets enabling effective feature engineering and model development without production risks or copying entire petabyte datasets.
Correct Answer Is:
A) Data lake zones (raw, dev, prod) with sampling or snapshots for experimentation
8.
Your organization needs to provide data analysts SQL access to multiple data sources without data movement. What query federation approach enables this?
- Amazon Athena Federated Query accessing multiple sources with connectors
- Copy all data to central database
- Separate queries per source
- Data federation not possible
Explanation
Explanation
Amazon Athena Federated Query allows analysts to run SQL queries across multiple data sources—such as relational databases, SaaS applications, and S3—without moving or copying the data. It uses data source connectors to reach external systems and returns results through a single SQL interface. This enables true query federation, reducing data duplication, simplifying architecture, and providing analysts with seamless access to distributed datasets.
Correct Answer Is:
Amazon Athena Federated Query accessing multiple sources with connectors
Amazon Athena Federated Query allows analysts to run SQL queries across multiple data sources—such as relational databases, SaaS applications, and S3—without moving or copying the data. It uses data source connectors to reach external systems and returns results through a single SQL interface. This enables true query federation, reducing data duplication, simplifying architecture, and providing analysts with seamless access to distributed datasets.
Correct Answer Is:
Amazon Athena Federated Query accessing multiple sources with connectors
9.
Your data pipeline needs to process incremental changes from transactional databases without full table scans. What pattern enables incremental extraction?
- A) Full table extract every run
- B) Change Data Capture (CDC) with DMS or timestamp-based incremental extraction
- C) No incremental support
- D) Manual change identification
Explanation
Explanation
Incremental extraction strategies: (1) CDC using AWS DMS capturing INSERT/UPDATE/DELETE from transaction logs streaming changes, (2) timestamp-based: query WHERE last_modified > max_timestamp_from_previous_run tracking high-water mark, (3) sequence-based: query WHERE id > max_id_from_previous_run for append-only tables. CDC advantages: captures all changes including deletes, low database load, near-real-time. Timestamp approach: requires last_modified column, doesn't capture deletes (soft delete workaround). Full table scans inefficient and expensive with large tables. Store high-water marks in DynamoDB or S3. Use Glue job bookmarks for automatic high-water mark management. Essential for ETL from operational databases where incremental extraction minimizes database impact and processing time, enabling frequent updates (hourly vs daily) with CDC providing highest fidelity change tracking for maintaining synchronized analytical replicas.
Correct Answer Is:
B) Change Data Capture (CDC) with DMS or timestamp-based incremental extraction
Incremental extraction strategies: (1) CDC using AWS DMS capturing INSERT/UPDATE/DELETE from transaction logs streaming changes, (2) timestamp-based: query WHERE last_modified > max_timestamp_from_previous_run tracking high-water mark, (3) sequence-based: query WHERE id > max_id_from_previous_run for append-only tables. CDC advantages: captures all changes including deletes, low database load, near-real-time. Timestamp approach: requires last_modified column, doesn't capture deletes (soft delete workaround). Full table scans inefficient and expensive with large tables. Store high-water marks in DynamoDB or S3. Use Glue job bookmarks for automatic high-water mark management. Essential for ETL from operational databases where incremental extraction minimizes database impact and processing time, enabling frequent updates (hourly vs daily) with CDC providing highest fidelity change tracking for maintaining synchronized analytical replicas.
Correct Answer Is:
B) Change Data Capture (CDC) with DMS or timestamp-based incremental extraction
10.
A company needs to analyze petabytes of data with complex queries requiring distributed processing. What query engine provides the best performance for complex analytics?
- A) Single-node database
- B) Amazon EMR with Apache Spark for distributed processing
- C) Athena for simple queries only
- D) RDS for analytics
Explanation
Explanation
Amazon EMR with Apache Spark provides distributed processing framework ideal for complex analytics on petabyte-scale data. Spark advantages: in-memory processing (10-100x faster than disk-based), distributed computing across hundreds of nodes, advanced analytics (MLlib for ML, GraphX for graphs, Spark SQL for structured data), support for batch and streaming. Single-node databases don't scale to petabytes. Athena works well for ad-hoc queries but EMR Spark better for complex, iterative algorithms or ML workloads. RDS isn't designed for big data analytics. Use EMR Spark for: machine learning at scale, graph analysis, complex ETL with multiple transformations, iterative algorithms, joins across massive datasets. EMR features: multiple deployment options (EC2, EKS, Serverless), integration with S3 for data storage, support for Hive, Presto, HBase. Essential for data science teams requiring computational power beyond query engines, needing custom algorithms or complex analytics pipelines.
Correct Answer Is:
C) Athena for simple queries only
Amazon EMR with Apache Spark provides distributed processing framework ideal for complex analytics on petabyte-scale data. Spark advantages: in-memory processing (10-100x faster than disk-based), distributed computing across hundreds of nodes, advanced analytics (MLlib for ML, GraphX for graphs, Spark SQL for structured data), support for batch and streaming. Single-node databases don't scale to petabytes. Athena works well for ad-hoc queries but EMR Spark better for complex, iterative algorithms or ML workloads. RDS isn't designed for big data analytics. Use EMR Spark for: machine learning at scale, graph analysis, complex ETL with multiple transformations, iterative algorithms, joins across massive datasets. EMR features: multiple deployment options (EC2, EKS, Serverless), integration with S3 for data storage, support for Hive, Presto, HBase. Essential for data science teams requiring computational power beyond query engines, needing custom algorithms or complex analytics pipelines.
Correct Answer Is:
C) Athena for simple queries only
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