Amazon BDS-C00 AWS Certified Big Data – Specialty (BDS-C00) Free Practice Test — 30 Questions
This practice bank exercises foundational knowledge and decision-making for the AWS Big Data – Specialty exam. Questions cover AWS Glue job scheduling, GDPR/HIPAA compliance, CloudTrail log analysis, S3 event-driven Lambda processing, SageMaker ML workflows, data cleaning strategies, Big Data characteristics (volume, velocity, variety, veracity), encryption best practices, data lifecycle retention policies, and storage selection (S3 vs RDS vs DynamoDB). You will also encounter streaming analytics with Kafka, Kinesis shard provisioning, EMR auto-scaling, and performance optimization via data partitioning. Security topics include IAM roles, CloudTrail auditing, and principle of least privilege. The set reinforces both conceptual understanding and practical configuration decisions for big data solutions on AWS.
What this BDS-C00 practice set measures
This is an analysis of the practice bank, not a claim about the vendor's live exam blueprint. Use it to identify the knowledge, judgment, and recall patterns exercised here, then verify your coverage against the current official exam guide.
Data Processing and Analytics
This section covers core AWS services and frameworks for big data processing. Questions explore AWS Glue job concurrency, where the key constraint is job duration relative to scheduling interval. Apache Spark is highlighted for its unified batch/stream processing via DataFrames, with caching crucial for performance. AWS EMR auto-scaling is emphasized for handling peak loads, and Athena query order of operations (WHERE before GROUP BY) can cause unexpected averages. SageMaker built-in algorithms with feature selection and hyperparameter tuning are essential for robust ML models. Normalization and deduplication are vital for data quality in ETL.
- AWS Glue job scheduling: only 2 concurrent jobs possible if job duration is 5 min and interval is 10 min.
- Spark DataFrame API with caching outperforms RDDs for multi-step transformations on large datasets.
- EMR auto-scaling effectively reduces latency during high-load periods by adding resources dynamically.
- Athena SQL evaluates WHERE clause before GROUP BY; filtering after aggregation is incorrect.
- SageMaker requires both feature selection and hyperparameter tuning for model effectiveness.
Security and Compliance
Security and compliance questions focus on protecting sensitive data under GDPR and HIPAA. The practice bank emphasizes encryption at rest (AES-256) and in transit (TLS 1.2) as top priorities. Access controls must follow the principle of least privilege via IAM roles with fine-grained permissions, combined with CloudTrail logging for auditing. Data encryption and access audits are critical for regulatory adherence. Consent mechanisms under GDPR require explicit opt-in, while HIPAA mandates encryption, access controls, and audit logs for ePHI. CloudTrail's S3 bucket should have a restrictive policy and lifecycle rules to transition logs to Glacier.
- Encrypt data at rest with AES-256 and in transit with TLS 1.2 to meet GDPR and PCI DSS.
- Implement IAM roles with fine-grained permissions and enable CloudTrail for auditing API calls.
- For GDPR, use a clear opt-in consent mechanism; pre-ticked boxes are invalid.
- HIPAA requires encryption, access controls, and audit logs for electronic protected health information (ePHI).
- CloudTrail logs should be stored in a restricted S3 bucket with lifecycle rules to Glacier after 90 days.
Data Storage and Lifecycle
Storage decisions revolve around scalability, cost, and data type. Amazon S3 is best for unstructured user activity logs due to its scalability and cost-effectiveness. Data lifecycle management requires retaining critical data for 7 years, sensitive for 5, and archiving non-sensitive after 1 year; at the 5-year mark, 30 TB of non-sensitive data may be archived. S3 versioning and cross-region replication (CRR) create two versions in the primary region (original and modified) and replicate the modified version to the secondary region. For large datasets, data partitioning improves performance via parallel processing across nodes, though overhead must be considered.
- S3 is the most appropriate storage for unstructured logs due to scalability and cost.
- Data retention: critical 7 yr, sensitive 5 yr, non-sensitive archived after 1 yr; at 5 yr, 30 TB non-sensitive can be archived.
- S3 versioning + CRR: two versions in primary; modified version replicated as new object in secondary.
- Data partitioning enables parallel processing and reduces ingestion/processing time, with speedup limited by number of partitions and nodes.
- Normalization standardizes formats (e.g., dates) and deduplication removes duplicate records.
Real-Time and Streaming Analytics
Streaming data requires low latency and fault tolerance. Apache Flink is the best technology for high-throughput, low-latency complex event processing with stateful computations. Apache Kafka is for ingestion, not processing. A combination of Kafka and Lambda provides a scalable, serverless real-time pipeline. Event-driven architecture with a message queue (e.g., SQS) ensures resilience and dynamic scaling for IoT data. Kinesis Data Streams shard provisioning must accommodate peak load: 5,000 transactions/sec requires 5 shards (1 shard = 1,000 records/sec). Batch processing (e.g., hourly) introduces 15-minute delay vs 5 seconds for real-time.
- Apache Flink is most suitable for high-throughput, low-latency stream processing with complex event processing.
- Kafka + Lambda enables real-time data ingestion and serverless processing, automatically scaling with load.
- Event-driven architecture with a message queue handles variable IoT data loads and provides fault tolerance.
- Kinesis: at peak 5,000 transactions/sec, provision 5 shards (each supports 1,000 records/sec).
- Real-time processing detects fraud in 5 seconds vs 15 minutes for batch, significantly reducing potential loss.
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A data engineering team is tasked with processing large volumes of streaming data from IoT devices in real-time. They need to schedule jobs that will aggregate this data every 10 minutes and store the results in an Amazon S3 bucket. The team is considering using AWS Glue for this purpose. Given that the data ingestion rate is approximately 1,000 records per second, and each aggregation job takes about 5 minutes to complete, what is the maximum number of concurrent jobs that can be scheduled without causing delays in data processing?
Study workflow
Turn one BDS-C00 attempt into a study plan
- 1
Design a Scalable Streaming Pipeline
Start with Apache Kafka or Amazon Kinesis to ingest high-velocity data. Use AWS Lambda for serverless processing, scaling automatically with load. Implement an event-driven architecture with a message queue (like SQS) to decouple producers and consumers. Provision Kinesis shards based on peak throughput (1 shard per 1,000 records/sec). For stateful processing, consider Apache Flink. Monitor with CloudWatch and set up dead-letter queues for error handling.
- 2
Implement Data Encryption and Access Controls
Enable AES-256 encryption for data at rest (S3, EBS, RDS) and TLS 1.2 for data in transit. Use IAM roles with fine-grained policies to grant least privilege. Turn on AWS CloudTrail to log all API calls and store logs in a restricted S3 bucket with lifecycle rules. For sensitive data, apply additional controls like S3 bucket policies and VPC endpoints. Regularly audit access using CloudTrail logs and IAM Access Analyzer.
- 3
Optimize Spark Jobs on EMR
Use DataFrame API instead of RDDs for better optimization. Cache intermediate results when reused across multiple actions. Enable auto-scaling for EMR clusters to handle peak loads. Partition input data (e.g., by date) to allow parallelism. Tune Spark configurations like spark.sql.shuffle.partitions based on cluster size. Monitor job performance with Spark UI and EMR metrics, and adjust instance types as needed.
- 4
Manage Data Lifecycle and Compliance
Classify data into categories (critical, sensitive, non-sensitive) with retention periods (7 yr, 5 yr, 1 yr). Use S3 lifecycle policies to transition older data to S3 Glacier for cost savings. Implement versioning to track changes. For compliance, enforce encryption and access controls, and enable CloudTrail for auditing. Use AWS Config rules to check for non-compliant resource configurations. Set up automated deletion or archival per policies.
- 5
Build a Real-Time Analytics Dashboard
Ingest streaming data using Amazon Kinesis or Kafka. Process with AWS Lambda or Kinesis Data Analytics. Store results in Amazon S3 or DynamoDB. Connect Amazon QuickSight using direct query or SPICE (for static datasets). Set up scheduled refreshes if using SPICE, or use direct query for always-current data. Optimize performance by partitioning data and using appropriate visualization types. Monitor costs and adjust refresh frequency.
FAQ
Questions about this BDS-C00 practice page
Clear boundaries on what the bank covers, how to use it, and where official vendor information still matters.
How do I determine the number of shards for Amazon Kinesis Data Streams?+
Each shard supports up to 1,000 records per second. Calculate based on peak throughput. For example, 5,000 records/sec peak requires 5 shards. Consider average record size (max 1 MB per record) and ensure total throughput within shard limits. You can manually add or use auto-scaling with CloudWatch metrics.
What is the difference between Apache Spark and Apache Flink for streaming?+
Spark Streaming uses micro-batches, introducing latency (seconds to minutes). Flink is true real-time with lower latency (milliseconds). Flink also supports stateful computations and complex event processing natively. Spark provides a unified API for batch and stream, while Flink is designed primarily for stream processing.
How can I ensure data integrity when using S3 versioning and cross-region replication?+
Enable versioning on both source and destination buckets. Configure CRR to replicate all object versions. Verify that IAM roles have permissions for both read and write. Use S3 Object Lock to prevent deletions. Monitor replication metrics in CloudWatch. Test by uploading and modifying objects to confirm correct version counts.
What are the key compliance requirements for storing ePHI on AWS?+
HIPAA requires encryption at rest and in transit, strict access controls (least privilege), and audit logging (CloudTrail). Must have a Business Associate Agreement (BAA) with AWS. Enable S3 bucket policies to restrict access, use IAM roles, and implement data lifecycle policies. Regularly audit logs for unauthorized access.
How does data veracity impact big data analytics?+
Veracity refers to the trustworthiness and quality of data. Poor veracity leads to inaccurate insights. To address it, implement data validation rules during ETL, standardize formats, and remove duplicates. Use tools like AWS Glue DataBrew for profiling. Without veracity, even large volumes and fast velocity can produce misleading results.
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