Amazon DAS-C01 AWS Certified Data Analytics – Specialty (DAS-C01) Free Practice Test — 30 Questions
This practice bank covers core AWS data analytics concepts: streaming data ingestion with Kinesis, batch ETL with AWS Glue, data warehousing with Redshift, serverless querying with Athena, and storage optimization with S3 classes. It also addresses security (encryption, access control), compliance (GDPR), and visualization best practices (Power BI, Tableau, DAX, quick chart selection). Working these questions will reinforce architectural decisions for collection, processing, analysis, and governance of large-scale data pipelines.
What this DAS-C01 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 Collection and Storage
This section covers methods to ingest, buffer, and store data cost‑effectively while meeting durability and availability needs. Kinesis Data Firehose buffering logic (time vs. size) is examined, along with S3 storage class selection for infrequently accessed data (Glacier vs. Standard‑IA). Partitioning in S3 and using columnar formats (Parquet, ORC) are highlighted as key optimizations for query performance in Athena.
- Kinesis Firehose delivers data to S3 when buffer interval (300 s) or buffer size (5 MB) is reached – whichever occurs first.
- For data accessed once a year, S3 Glacier provides the lowest storage cost while maintaining 11 nines durability.
- Partitioning S3 data by dimensions (region, product) and using columnar formats (Parquet/ORC) reduces Athena scan volume and query cost.
Processing and Transformation
This set focuses on ETL pipeline design using AWS Glue and on optimizing data loads into Redshift. Understanding how to infer schemas from nested JSON via Glue Crawlers and the benefits of the COPY command for parallel bulk loading is key. It also examines the role of schema inference, transformation jobs, and the importance of avoiding single‑record INSERTs or manual schema definition for large datasets.
- Glue Crawlers automatically infer schemas from JSON files, creating Data Catalog tables – essential for complex nested structures.
- Using the COPY command with parallelism is far more efficient than individual INSERT statements for loading millions of rows into Redshift.
- Glue jobs perform serverless transformations, scaling automatically with workload; Lambda is not a substitute for full ETL logic.
Analysis and Visualization
The practice bank explores DAX formulas for dynamic percentage calculations in Power BI and calculated fields in Tableau. It emphasizes clear labeling, appropriate chart types (line charts for trends vs. pie charts for parts of a whole), and avoiding overplotting. The content also covers SQL query optimization through indexing, partitioning, and rewriting to reduce subqueries, and highlights the importance of performance testing under varying load.
- In Power BI, use DIVIDE with CALCULATE and ALLEXCEPT to compute percentage of total while preserving region context.
- A line chart with monthly markers is best for showing both trend and seasonal fluctuations over time.
- Indexing WHERE‑clause columns and partitioning large tables can reduce query time significantly, especially under concurrent load.
Security, Governance, and Compliance
These questions test understanding of encryption at rest and in transit (KMS, TLS), immutable logging (CloudTrail + S3 Object Lock), and access control combining RBAC with ABAC. GDPR principles (purpose limitation, data minimization, storage limitation) are examined, along with the need for clear consent and anonymization. The section reinforces that security measures must be systematic, not one‑off, and auditable.
- Enable CloudTrail for all API calls and use S3 Object Lock to make logs immutable for compliance audits.
- Combine RBAC (roles) with ABAC (department, clearance) for granular, context‑sensitive access control.
- GDPR compliance requires transparent privacy policies, purpose‑limited data collection, and regular deletion of unnecessary records.
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Static practice bank
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A data analyst is tasked with creating a Power BI report that visualizes sales data across multiple regions. The analyst needs to calculate the percentage of total sales for each region and display this information in a pie chart. The sales data is structured in a table with columns for Region, SalesAmount, and Date. After creating the pie chart, the analyst wants to add a slicer to filter the data by year. What is the most effective way to ensure that the pie chart updates dynamically based on the year selected in the slicer?
Study workflow
Turn one DAS-C01 attempt into a study plan
- 1
Design a Streaming Data Pipeline
Start by identifying the ingestion rate and buffer constraints. Use Kinesis Data Firehose with a buffer interval of 60–900 seconds and buffer size of 1–128 MB. If real-time processing is needed, use Kinesis Data Streams with sufficient shards. Always plan for downstream storage (S3, Redshift) and transform data with Lambda or Kinesis Data Analytics if necessary.
- 2
Optimize Athena Queries on S3
Choose a columnar format (Parquet or ORC) and partition data by high‑cardinality dimensions such as year, month, region, or product. Use the MSCK REPAIR TABLE command after adding partitions. Avoid SELECT *; query only the columns needed. Store results in a separate S3 bucket to avoid scanning the same data repeatedly.
- 3
Perform a Bulk Load into Redshift
Use the COPY command from S3, specifying the IAM role, data format, and compression. Leverage parallel loading by splitting the input into multiple files. For optimal performance, set appropriate region, enable manifest files for multi‑file loads, and use the COMPUPDATE PRESET option for automatic sort key definitions. Avoid single‑row INSERT for large volumes.
- 4
Create a Dynamic Percentage Measure in Power BI
Write a DAX measure using DIVIDE to divide the current context value by the total filtered by the highest granularity dimension. Use CALCULATE with ALLEXCEPT to keep only the region filter. Ensure the measure is placed on the visual and the slicer is connected to the same table. Test with different slicer selections to confirm dynamic recalculation.
- 5
Implement Immutable Audit Logging
Enable CloudTrail to capture all API calls and deliver logs to an S3 bucket. Configure an S3 bucket policy that enforces Object Lock with a retention period matching compliance requirements. Restrict bucket access via IAM policies and enable encryption (SSE‑S3 or KMS). Regularly review access logs and set up alerts for unauthorized activities.
FAQ
Questions about this DAS-C01 practice page
Clear boundaries on what the bank covers, how to use it, and where official vendor information still matters.
When would I use Kinesis Data Firehose instead of Kinesis Data Streams?+
Use Firehose when you need a fully managed, near‑real‑time delivery to S3, Redshift, or Elasticsearch without writing custom consumers. Data Streams is preferred for custom real‑time processing with multiple consumers, lower latency, and the ability to replay records.
How do I choose between S3 Standard-IA and S3 Glacier for infrequent access?+
S3 Standard-IA is suitable for data accessed less frequently but needing millisecond retrieval. S3 Glacier is cheaper for data accessed once a year or less, with retrieval times from minutes to hours. For critical data, Standard-IA; for archival, Glacier.
What is the role of AWS Glue Crawlers in an ETL pipeline?+
Glue Crawlers automatically scan data sources (e.g., S3, JDBC) to infer schemas and populate the Glue Data Catalog. This catalog is then used by Glue jobs for transformation and by Athena for serverless queries. Crawlers handle nested JSON and schema evolution.
Why is the COPY command preferred over INSERT for loading data into Redshift?+
The COPY command loads data in parallel from S3, DynamoDB, or other sources, achieving much higher throughput than single‑row INSERTs. It automatically performs data compression, validation, and error handling, making it the optimal choice for large‑volume ingestion.
How does S3 Object Lock help with compliance?+
S3 Object Lock enforces a retention period during which objects cannot be deleted or overwritten. Combined with CloudTrail logs, it provides immutable audit records that satisfy regulations like GDPR and HIPAA. It can be set at the bucket or object level with legal hold or retention mode.
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