DEA-C01 Data Store Management Practice Test 10 — 15 Questions

Question 1

A data engineer is designing a data lake on Amazon S3. The data is accessed frequently for the first 30 days, then rarely accessed after 90 days, and must be archived after 1 year. Which S3 lifecycle policy configuration meets these requirements with the lowest cost?

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

Accepted Answer

Transition to S3 Standard-IA after 30 days, then to S3 Glacier Deep Archive after 365 days.. It transitions data to S3 Standard-IA after 30 days (when frequent access drops), then to S3 Glacier Deep Archive after 365 days for archival, minimizing cost while meeting all access patterns. Standard-IA offers lower storage cost than Standard for infrequent access, and Glacier Deep Archive is the cheapest storage class for long-term archival.

Answer

Transition to S3 One Zone-IA after 30 days, then to S3 Glacier Deep Archive after 365 days.

Answer

Transition to S3 Glacier Flexible Retrieval after 90 days.

Answer

Transition to S3 Glacier Instant Retrieval after 30 days, then expire after 365 days.

Question 2

A company uses Amazon Redshift for its data warehouse. The data engineering team notices that queries against a large fact table are slow. The table is distributed using DISTSTYLE EVEN and has multiple sort keys. After analyzing the query plans, they find that most queries filter on a specific column, 'customer_id'. Which change would most likely improve query performance for these filter operations?

Accepted Answer

Change to DISTSTYLE KEY on the 'customer_id' column.. Changing to DISTSTYLE KEY on 'customer_id' ensures that rows with the same customer_id are co-located on the same node slice. This allows the Redshift query engine to perform filter operations on a single slice rather than scanning all slices, dramatically reducing data movement and improving query performance for queries that filter on that column.

Answer

Add a secondary sort key on 'customer_id'.

Answer

Change to DISTSTYLE EVEN with a different sort key.

Answer

Change to DISTSTYLE ALL for the fact table.

Question 3

A company uses Amazon DynamoDB for a gaming application. The table has a partition key of 'user_id' and a sort key of 'game_timestamp'. The application frequently queries by 'user_id' and filters by 'game_timestamp' within a specific date range. The queries are slow. The table has a global secondary index (GSI) on 'game_timestamp'. What is the most likely cause of the slow queries?

Accepted Answer

A hot partition exists due to uneven access pattern on 'user_id'.. The slow queries are most likely caused by a hot partition on the base table. Even though the query uses 'user_id' as the partition key, if a small number of 'user_id' values receive a disproportionate amount of traffic, those specific partitions become overloaded, causing throttling and high latency. The GSI on 'game_timestamp' is not used for these queries because the filter is on the sort key of the base table, and DynamoDB can efficiently query by partition key and filter by sort key without needing a GSI.

Answer

The GSI has insufficient read capacity.

Answer

The GSI is used instead of the base table for queries on 'user_id'.

Answer

The sort key is not used in the query.

Question 4

A data engineer needs to store semi-structured JSON data that is accessed infrequently but requires millisecond retrieval latency. The data is immutable once written. Which AWS service is most cost-effective?

Accepted Answer

Amazon S3 (Standard-IA) with S3 Select. Amazon S3 Standard-IA with S3 Select is the most cost-effective choice because it provides infrequent access storage at low cost while S3 Select enables server-side filtering to retrieve only the required subset of JSON data, achieving millisecond latency for small queries on immutable data without the overhead of a full database.

Answer

Amazon DynamoDB with on-demand capacity

Answer

Amazon ElastiCache for Redis

Answer

Amazon RDS for PostgreSQL with JSONB

Question 5

Which TWO of the following are valid approaches to implement fine-grained access control for Amazon DynamoDB items based on user attributes? (Choose 2.)

Accepted Answer

Use Amazon Cognito identity pools with IAM roles that include conditions based on user attributes.. Amazon Cognito identity pools can be configured to assume IAM roles with fine-grained policies that use condition keys such as `dynamodb:LeadingKeys` or custom attribute-based conditions. This allows access to DynamoDB items to be restricted based on user-specific attributes (e.g., user ID) without hardcoding permissions per user.

Answer

Enable row-level security in DynamoDB using AWS Lake Formation.

Answer

Configure a VPC endpoint with a bucket policy to restrict access to specific items.

Answer

Store user-specific items in separate S3 buckets and use IAM policies to restrict bucket access.

Question 6

Which THREE of the following are best practices for managing data storage costs in Amazon S3? (Choose 3.)

Accepted Answer

Use S3 Lifecycle policies to transition objects to S3 Glacier Deep Archive after a specified period.. Option B is correct because S3 Lifecycle policies can automatically transition objects to lower-cost storage classes such as S3 Glacier Deep Archive after a defined period, reducing storage costs for infrequently accessed data. Option C is correct because S3 Object Lock enforces retention controls that prevent premature deletion, and combining it with a lifecycle policy that expires objects after the retention period ensures data is removed once it is no longer required, avoiding unnecessary ongoing storage charges. Option E is correct because S3 Intelligent-Tiering automatically moves objects between access tiers based on changing or unknown access patterns, optimizing costs without manual intervention. Option A is not a best practice because keeping all data in S3 Standard ignores cheaper archival and infrequent-access classes, increasing costs unnecessarily. Option D is not a cost-management best practice because cross-region replication creates additional copies and incurs extra storage, request, and data transfer charges, which raises rather than lowers storage costs.

Answer

Store all data in S3 Standard for maximum durability.

Answer

Create multiple bucket replicas in different regions to ensure availability.

Question 7

An IAM policy is attached to a role assumed by authenticated users via Amazon Cognito. What does this policy allow?

Accepted Answer

Users can read items in the Orders table only if the partition key matches their Cognito identity ID.. The policy uses a condition key like `dynamodb:LeadingKeys` with a value referencing `${cognito-identity.amazonaws.com:sub}`. This restricts DynamoDB actions to items where the partition key matches the user's Cognito identity ID, ensuring fine-grained access control. Option D correctly states that users can read items only when the partition key equals their identity ID, which is the intended behavior for row-level security.

Answer

Users can read and write items in the Orders table where the partition key matches their Cognito identity ID.

Answer

Users can read any item in the Orders table using GetItem and Query.

Answer

Users can scan the entire Orders table but only if they use a filter expression.

Question 8

A data engineer runs the above DDL statement in Amazon Athena. The query returns an error. What is the most likely cause?

Accepted Answer

The INPUTFORMAT is incorrect for Parquet files.. The DDL statement uses the default INPUTFORMAT 'org.apache.hadoop.mapred.TextInputFormat', which is designed for text-based files like CSV or JSON, not for binary columnar formats like Parquet. Parquet requires 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat' to correctly read the file's metadata and compressed column chunks. Using the wrong INPUTFORMAT causes Athena to fail when attempting to parse the Parquet file, resulting in an error.

Answer

The SerDe is not compatible with Parquet files.

Answer

The S3 bucket location does not exist.

Answer

The table name contains underscores.

Question 9

A data engineering team is designing a data lake on Amazon S3. They need to store raw data in a format that supports schema evolution and is optimized for analytics with Amazon Athena. Which storage format should they use?

Accepted Answer

Parquet. Parquet is the correct choice because it is a columnar storage format that is highly optimized for Amazon Athena, which can read only the necessary columns to minimize data scanned and reduce query costs. Parquet also supports schema evolution through features like nested data structures and the ability to add or remove columns without rewriting the entire dataset, making it ideal for raw data lakes that must accommodate changing schemas over time.

Answer

CSV

Answer

Avro

Answer

JSON

Question 10

A company has an Amazon DynamoDB table with on-demand capacity mode. The table stores session data for a web application. Recently, the application experienced throttling errors during a traffic spike. The team wants to prevent future throttling while optimizing costs. What should they do?

Accepted Answer

Implement a DynamoDB Accelerator (DAX) cluster. A DynamoDB Accelerator (DAX) cluster provides an in-memory cache that absorbs read-heavy traffic spikes, reducing the number of read requests that reach the underlying DynamoDB table. Since the throttling errors occurred during a traffic spike and the table uses on-demand capacity, which already scales automatically for writes and reads, the bottleneck is likely read-heavy traffic overwhelming the table's throughput. DAX offloads reads from the table, preventing throttling without requiring any changes to capacity mode, and it is cost-effective because it reduces read capacity unit consumption.

Answer

Enable DynamoDB auto scaling on the table

Answer

Switch to provisioned capacity with auto scaling

Answer

Increase the read and write capacity of the table

Question 11

A company uses Amazon Redshift for data warehousing. The data team notices that queries are slow due to high disk usage on the cluster. They need to free up space without deleting any data. What should they do?

Accepted Answer

Run a deep copy to re-sort and reclaim space. A deep copy recreates the table with optimal sort order and reclaims unused space by reorganizing data blocks. This process effectively frees up disk space without deleting any data, addressing the high disk usage issue.

Answer

Change the table's sort keys

Answer

Run VACUUM command

Answer

Add more nodes to the cluster

Question 12

A financial services company stores sensitive transaction data in an Amazon S3 bucket. Compliance requires that all objects be encrypted using SSE-KMS and that the bucket be protected from accidental deletion. Which combination of actions meets these requirements? (Select TWO.)

Accepted Answer

Add a bucket policy that denies PutObject if the object is not encrypted with SSE-KMS. A bucket policy with a condition denying PutObject unless the object's encryption status matches SSE-KMS (using the s3:x-amz-server-side-encryption-aws-kms-key-id condition key) enforces encryption at the upload level. This ensures that any object written to the bucket must be encrypted with a KMS key, meeting the compliance requirement.

Answer

Enable MFA Delete on the bucket

Answer

Enable S3 Block Public Access

Answer

Set default encryption to SSE-S3

Question 13

Which TWO actions can improve query performance on an Amazon Redshift cluster? (Choose two.)

Accepted Answer

Define appropriate sort keys. Option A is correct because defining appropriate sort keys on Redshift tables physically orders data on disk by the sort key columns, enabling zone maps to skip large blocks of data during range-filtered scans and dramatically reducing I/O for queries that filter or join on those columns. Option D is correct because columnar compression stores each column separately with encoding schemes (e.g., AZ64, ZSTD, LZO) that shrink data size, so Redshift reads fewer blocks from disk and from Amazon S3 during scans, improving query performance while lowering storage cost. Option B is not the intended answer because adding nodes increases cluster capacity and concurrency but does not by itself optimize the execution of individual queries. Option C is wrong because EVEN distribution is not optimal for all tables; ALL or KEY distribution is often better to avoid redistribution and broadcast steps during joins. Option E is not marked correct because VACUUM is a maintenance operation that reclaims space and re-sorts rows, but the question asks for actions that improve query performance, and the two designated correct answers are sort keys and columnar compression.

Answer

Increase the number of nodes

Answer

Use EVEN distribution style for all tables

Answer

Run VACUUM command regularly

Question 14

Which THREE factors should a data engineer consider when choosing between Amazon S3 and Amazon DynamoDB for storing time-series data? (Choose three.)

Accepted Answer

Required query complexity (simple key lookups vs. range scans). Option A is correct because the required query complexity directly drives the choice: DynamoDB excels at simple primary-key lookups and, with sort keys, efficient range scans on a single partition key, whereas S3 (especially with Athena or S3 Select) is better suited to broader analytical scans and aggregations over large datasets. Option B is correct because latency requirements differ fundamentally: DynamoDB provides single-digit millisecond, predictable latency at any scale, while S3 GET requests typically return in tens to hundreds of milliseconds and are not designed for low-latency transactional reads. Option D is correct because data access patterns determine the fit: DynamoDB is optimized for high-volume random reads/writes keyed by partition and sort key, while S3 is optimized for sequential throughput of large objects and is a poor fit for frequent small random updates. Option C is not a primary differentiator here, since both services offer low-cost storage tiers and per-GB pricing alone rarely decides the architecture compared to access and query characteristics. Option E is also not decisive, because both S3 and DynamoDB scale to virtually unlimited volumes; total data volume matters for cost and partitioning strategy but does not by itself dictate which service to choose.

Answer

Cost per GB of storage

Answer

Total data volume

Question 15

The exhibit shows the output of describe-table for a DynamoDB table. The table is used for a reporting job that queries by 'pk' and filters on 'sk' using a range condition. The job is running slowly. What is the most likely cause?

Accepted Answer

The provisioned read capacity is too low.. The describe-table output shows the table has a primary key composed of 'pk' (partition key) and 'sk' (sort key), which is ideal for the query pattern described (query by 'pk' and filter on 'sk' with a range condition). The job is running slowly, and the most likely cause is insufficient provisioned read capacity, as the reporting job may be consuming more read capacity units than provisioned, leading to throttling and slower performance.

Answer

The table lacks a global secondary index (GSI).

Answer

The table uses provisioned throughput instead of on-demand.

Answer

The table needs a local secondary index (LSI) on 'sk'.