DEA-C01 Data Store Management Practice Test 17 — 15 Questions

Question 1

A data engineer manages an Amazon S3 data lake where each new object must be encrypted at rest with a customer-provided key that is rotated annually. The company's security policy requires that the key material never be stored in AWS and that all encryption and decryption operations are audited in AWS CloudTrail. The engineer needs to configure default bucket encryption. Which solution meets these requirements?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Configure default encryption with SSE-C and provide an encryption key in each request.. The security policy requires a customer-provided key whose material is never stored in AWS, with annual rotation controlled by the company and CloudTrail auditing of key usage. SSE-C is the only S3 server-side encryption option that accepts a customer-supplied key on each request, so S3 never stores the key material. SSE-S3 and SSE-KMS both store key material in AWS, and client-side encryption does not configure default bucket encryption.

Answer

Configure default encryption with SSE-KMS using a customer managed key in AWS KMS.

Answer

Configure default encryption with client-side encryption using an AWS KMS key before uploading objects.

Answer

Configure default encryption with SSE-S3 using an AWS managed key.

Question 2

A data engineer is building an Amazon S3 data lake. Raw JSON files land in an S3 bucket, and AWS Glue crawlers populate the Data Catalog. Analysts query the raw tables with Amazon Athena. The engineer wants to create a curated zone where only specific columns are exposed, sensitive fields are masked, and the data is stored in Apache Parquet for performance. Which combination of actions should the engineer take to meet these requirements?

Accepted Answer

Create an AWS Glue ETL job that reads the raw JSON, applies a SelectFields transform to drop sensitive columns, writes Parquet to a curated S3 prefix, and registers the new table in the Data Catalog.. A curated zone with restricted columns and masked fields requires transforming the data, not just querying it differently. An AWS Glue ETL job can drop sensitive columns, write Parquet to a new prefix, and register the result in the Data Catalog. This delivers column-level control, masking, and columnar performance for Athena queries.

Answer

Enable S3 Object Lambda to redact sensitive fields on read, and keep the raw JSON files as the source for Athena queries.

Answer

Use an AWS Glue crawler to crawl the raw bucket and create a new table with only the allowed columns, then point Athena at that table.

Answer

Create an Athena view over the raw table that selects only the allowed columns, and grant analysts access to the view instead of the base table.

Question 3

A data engineer is managing an Amazon Redshift cluster that experiences performance degradation during complex analytical queries. The cluster uses a single node type and has no concurrency scaling enabled. The engineer observes that some queries are waiting in a queue due to limited query slots. Which action should the engineer take to improve query throughput without changing the cluster size?

Accepted Answer

Enable concurrency scaling on the Redshift cluster.. Concurrency scaling automatically adds transient clusters to handle increased concurrent query loads, reducing queuing and improving throughput without resizing the main cluster. It is ideal for read-heavy workloads with occasional spikes and is cost-effective because you only pay for the extra capacity when it is used.

Answer

Increase the number of slices by resizing the cluster to a larger node type.

Answer

Implement workload management (WLM) with more query slots allocated to the queue.

Answer

Enable short query acceleration (SQA) to prioritize short-running queries.

Question 4

A data engineering team is building a data lake on Amazon S3 and needs to optimize storage costs for a large volume of JSON logs. The logs are accessed frequently for the first 30 days, then rarely after 90 days, and must be retained for 7 years for compliance. The team wants to minimize costs while ensuring data durability and availability. Which TWO actions should the team take? (Choose two.)

Accepted Answer

Convert the JSON logs to Apache Parquet format using AWS Glue ETL jobs.. Converting logs to Parquet reduces storage footprint and query costs. A lifecycle policy that transitions to Standard-IA after 30 days and Glacier Deep Archive after 90 days aligns storage costs with access patterns while ensuring compliance. Together, these actions minimize storage costs without sacrificing durability or availability.

Answer

Enable S3 Intelligent-Tiering to automatically move objects between access tiers.

Answer

Use S3 Transfer Acceleration to speed up uploads of the logs.

Answer

Enable S3 Versioning to protect against accidental deletions.

Question 5

A company is using Amazon Redshift as its data warehouse. The data engineering team needs to implement a strategy to handle slowly changing dimensions (SCD) for a customer dimension table. The table must maintain a full history of changes, including current and previous values, with effective dates. Which two approaches are valid for implementing SCD Type 2 in Redshift? (Choose two.)

Accepted Answer

Use an INSERT statement with a SELECT from the source table, filtering for changed records, and then update the old records separately.. Implementing SCD Type 2 in Redshift typically involves either a MERGE statement to handle updates and inserts in one operation, or a two-step process of updating existing rows to expire them and then inserting new rows. Both methods preserve historical data with effective dates and are supported in Redshift. The other options either lose history or offload logic incorrectly.

Answer

Use a stored procedure that implements SCD Type 2 logic with temporary tables and transactions.

Answer

Use Redshift Spectrum to query external tables and apply SCD logic in Amazon Athena.

Answer

Use a DELETE followed by an INSERT to replace the entire dimension table.

Question 6

A data engineer is building a data lake in Amazon S3. The raw data arrives continuously and must be immediately queryable with Amazon Athena. The engineer wants to avoid the overhead of running ETL jobs and wants new data to be automatically discovered. Which solution meets these requirements with the LEAST operational effort?

Accepted Answer

Use AWS Glue crawlers to catalog the S3 data and query it directly with Athena.. AWS Glue crawlers automatically discover and catalog data in S3, making it immediately queryable by Athena without ETL. This serverless approach requires minimal setup and no infrastructure management, perfectly matching the need for low operational effort. The other options involve additional services, data movement, or manual schema definition, which increase complexity and delay query availability.

Answer

Create an AWS Glue ETL job to convert the data to Parquet and store it in a new S3 prefix.

Answer

Use Amazon Kinesis Data Firehose to deliver the data to Amazon S3 and query it with Amazon Athena.

Answer

Load the data into Amazon Redshift using COPY and query it with Redshift Spectrum.

Question 7

A data engineer is using AWS Glue to catalog data stored in Amazon S3. The data is in Parquet format and partitioned by year, month, and day. The engineer wants to improve query performance in Amazon Athena by reducing the amount of data scanned. Which action should the engineer take?

Accepted Answer

Use AWS Glue partition projection to avoid loading partitions into the Data Catalog.. Partition projection in AWS Glue and Athena allows the query engine to infer partition locations from a pattern, removing the need to store partition metadata in the Data Catalog. This reduces query planning overhead and can improve performance for highly partitioned data. The other options either degrade performance or do not affect data scanning.

Answer

Enable Amazon S3 Transfer Acceleration on the bucket.

Answer

Increase the number of AWS Glue DPUs for the crawler.

Answer

Convert the data to CSV format to enable faster scans.

Question 8

A data engineer is configuring an Amazon S3 bucket to store processed Parquet files for an analytics workload. The engineer wants to reduce storage costs for objects that are accessed frequently for the first 30 days and rarely after that, while keeping objects immediately retrievable. The engineer also wants to minimize management overhead. Which S3 lifecycle configuration should the engineer use?

Accepted Answer

Transition objects to S3 Standard-Infrequent Access after 30 days.. The workload accesses objects frequently for 30 days and rarely afterward, while requiring immediate retrieval and minimal management. S3 Standard-IA is optimized for exactly this pattern, offering lower storage cost than S3 Standard with no retrieval fee for normal access and immediate availability. Glacier Instant Retrieval, One Zone-IA, and Intelligent-Tiering either impose longer minimum durations, reduce durability, or add monitoring overhead.

Answer

Transition objects to S3 One Zone-IA after 30 days.

Answer

Transition objects to S3 Glacier Instant Retrieval after 30 days.

Answer

Transition objects to S3 Intelligent-Tiering after 30 days.

Question 9

A financial services company stores transactional data in an Amazon S3 bucket. The data must be retained for seven years for compliance. After the first year, the data is rarely accessed but must be retrievable within 12 hours if needed. The company wants to minimize storage costs. Which S3 storage class should the data engineer choose for data older than one year?

Accepted Answer

S3 Glacier Flexible Retrieval. S3 Glacier Flexible Retrieval provides a balance of low storage cost and flexible retrieval options. Standard retrieval typically completes within 3–5 hours, and bulk retrieval within 5–12 hours, satisfying the 12-hour retrieval requirement. It is more cost-effective than S3 Glacier Instant Retrieval and S3 Standard-IA for long-term archival. S3 Glacier Deep Archive, while cheaper, has a standard retrieval time of 12 hours and may not consistently meet the requirement.

Answer

S3 Standard-Infrequent Access (S3 Standard-IA)

Answer

S3 Glacier Instant Retrieval

Answer

S3 Glacier Deep Archive

Question 10

A data engineer needs to store large volumes of semi-structured JSON data in Amazon S3 for later analysis with Amazon Athena. The data will be queried frequently, and the engineer wants to minimize query costs and improve performance. Which S3 storage class and file format combination is most cost-effective and performant for this use case?

Accepted Answer

S3 Intelligent-Tiering with Parquet files compressed using Snappy.. Parquet with Snappy compression is a columnar, splittable format that minimizes data scanned by Athena, reducing query costs and improving performance. S3 Intelligent-Tiering automatically optimizes storage costs for frequently accessed data without retrieval fees, making it the best combination for this scenario.

Answer

S3 Glacier Deep Archive with ORC files.

Answer

S3 Standard-Infrequent Access (S3 Standard-IA) with CSV files.

Answer

S3 Standard with JSON files compressed using GZIP.

Question 11

A data engineer is designing a real-time analytics pipeline that ingests JSON events from Amazon Kinesis Data Streams and writes them to Amazon S3 in Parquet format for querying with Amazon Athena. The engineer needs the data to be partitioned by year, month, and day based on the event timestamp, and wants AWS Glue to automatically discover the schema and partitions. The engineer configures an AWS Glue ETL job to read from Kinesis and write to S3. Which combination of actions will ensure that Athena can query the data efficiently with partition pruning?

Accepted Answer

Write Parquet files to S3 with a prefix structure like year=YYYY/month=MM/day=DD/, and run an AWS Glue crawler to populate the AWS Glue Data Catalog with partition metadata.. To achieve efficient Athena queries with partition pruning, the data must be stored in a partitioned directory structure and the Data Catalog must contain the partition metadata. Writing Parquet files with Hive-style prefixes and running a Glue crawler accomplishes both: the crawler automatically discovers the schema and partitions, populating the Data Catalog. This minimizes manual effort and ensures Athena can prune partitions based on query filters.

Answer

Write Parquet files to S3 partitioned by year, month, and day, and configure the Glue ETL job to update the Data Catalog table with partition information using the enableUpdateCatalog option.

Answer

Write Parquet files to S3 with a flat prefix structure and create an Athena view that extracts year, month, and day from the event timestamp using date functions.

Answer

Write Parquet files to S3 partitioned by year, month, and day, and manually add each partition to the AWS Glue Data Catalog using AWS Glue APIs after every micro-batch.

Question 12

A financial services company uses Amazon DynamoDB to store transaction records. The table has a partition key `AccountId` and a sort key `TransactionDate`. The workload includes frequent queries for transactions by `AccountId` within a specific date range, and occasional queries for transactions by `TransactionDate` across all accounts. The table is currently provisioned with 1000 RCUs and 1000 WCUs, but the company wants to optimize costs and performance. The engineer needs to design the table to support these access patterns efficiently. Which combination of actions should the engineer take?

Accepted Answer

Keep the base table as is, and create a global secondary index (GSI) with `TransactionDate` as the partition key and `AccountId` as the sort key, and enable DynamoDB auto scaling on the table and the GSI.. The base table's key schema already supports the primary access pattern of querying by `AccountId` and date range. For the occasional cross-account date queries, a GSI with `TransactionDate` as partition key and `AccountId` as sort key is needed. Enabling auto scaling on the table and GSI ensures capacity matches demand, optimizing cost and performance. Other options either use an inappropriate index type or do not address capacity management effectively.

Answer

Create a global secondary index (GSI) with `TransactionDate` as the partition key and `AccountId` as the sort key, and use on-demand capacity mode for the table and the GSI.

Answer

Keep the base table as is, and create a local secondary index (LSI) with `TransactionDate` as the sort key to support queries by date across all accounts.

Answer

Create a global secondary index (GSI) with `TransactionDate` as the partition key and `AccountId` as the sort key, and enable DynamoDB auto scaling on the table and the GSI.

Question 13

A data engineer is designing a data lake on Amazon S3 and needs to optimize query performance for Parquet files while minimizing storage costs. The data is partitioned by date and accessed frequently for the last 30 days, but older data is rarely queried. Which combination of S3 features and configurations should the engineer use?

Accepted Answer

Use S3 Standard for recent data and transition older data to S3 Glacier Deep Archive using S3 Lifecycle policies.. Using S3 Lifecycle policies to transition older data to a lower-cost storage class like S3 Glacier Deep Archive optimizes storage costs for infrequently accessed data. Recent data remains in S3 Standard for low-latency access. Parquet format and partitioning by date improve query performance and reduce data scanned by analytics services. This combination balances performance and cost effectively.

Answer

Store all data in S3 Standard and use S3 Select to query Parquet files.

Answer

Use S3 Intelligent-Tiering for all data and enable S3 Object Lock.

Answer

Enable S3 Transfer Acceleration and use S3 Analytics to optimize storage.

Question 14

A data engineer is designing a data lake on Amazon S3. The raw data is stored in its original format, and transformed data is stored in Parquet. The engineer needs to ensure that the transformed data is automatically cataloged and available for querying in Amazon Athena as new data arrives. Which AWS service should be used to automate this process?

Accepted Answer

AWS Glue crawler triggered by an AWS Lambda function on S3 event notifications.. Using an AWS Glue crawler triggered by S3 events via Lambda automatically discovers and catalogs new transformed data in the AWS Glue Data Catalog. This event-driven approach ensures that Athena can query the data as soon as it arrives, with minimal operational effort. The other options either involve unnecessary complexity or do not directly update the catalog.

Answer

Amazon EventBridge rule that invokes an AWS Glue ETL job to catalog the data.

Answer

AWS Step Functions state machine that runs an AWS Glue crawler on a schedule.

Answer

Amazon S3 Inventory report configured to update the AWS Glue Data Catalog.

Question 15

A data engineer is designing a data lake on Amazon S3 and needs to ensure that data is encrypted at rest with keys managed by AWS. The company requires automatic key rotation and centralized audit logging of key usage. Which solution meets these requirements?

Accepted Answer

Use server-side encryption with AWS KMS keys (SSE-KMS).. SSE-KMS uses AWS Key Management Service to manage encryption keys. Customer managed keys support automatic annual rotation, and all key usage is logged in AWS CloudTrail, providing centralized audit logging. This satisfies the requirements for AWS-managed keys with automatic rotation and auditability. SSE-S3 does not offer these features, SSE-C requires customer-managed keys, and client-side encryption shifts key management to the customer.

Answer

Use client-side encryption with an AWS KMS key before uploading to S3.

Answer

Use server-side encryption with customer-provided keys (SSE-C).

Answer

Use server-side encryption with Amazon S3 managed keys (SSE-S3).