DEA-C01 Practice Questions 2 — 50 Questions

Question 1

A data engineering team is building a data lake on Amazon S3. They need to ingest data from multiple sources: (1) streaming IoT data, (2) daily CSV exports from an on-premises system via SFTP, and (3) change data capture (CDC) from an Amazon Aurora database. Which THREE services should the team use to ingest these data sources?

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

Accepted Answer

Amazon Kinesis Data Streams for IoT data ingestion.. Amazon Kinesis Data Streams (A) is the right choice for streaming IoT data because it is a fully managed, scalable service designed for real-time ingestion of high-volume streaming records into S3 via Kinesis Data Firehose or consumers. AWS Database Migration Service (B) supports ongoing change data capture (CDC) from Amazon Aurora (and other relational databases), replicating inserts, updates, and deletes continuously, which matches the CDC requirement. AWS Transfer Family (C) provides a fully managed SFTP endpoint backed by Amazon S3, so the on-premises system can push daily CSV exports over SFTP directly into the data lake. AWS Glue ETL (D) is a batch/transform service, not a CDC ingestion mechanism, so it does not satisfy the Aurora CDC requirement. Amazon EMR (E) is a big data processing cluster, not a managed ingestion service for daily SFTP CSV files, so it is not the appropriate ingestion tool here.

Answer

AWS Glue ETL for CDC from Aurora.

Answer

Amazon EMR for daily CSV ingestion.

Question 2

A data engineer is designing a data store for a real-time leaderboard application that requires sub-millisecond read and write latency. The leaderboard stores scores for millions of users and needs to be sorted by score. Which AWS service should the engineer use?

Accepted Answer

Amazon ElastiCache for Redis with a sorted set. Amazon ElastiCache for Redis provides a sorted set data structure (ZADD/ZRANGE commands) that maintains elements ordered by a numeric score with O(log N) complexity for both writes and reads, enabling sub-millisecond latency for real-time leaderboard updates and queries. This is the only option purpose-built for in-memory, sorted, real-time leaderboards at scale.

Answer

Amazon RDS for PostgreSQL with an index on score

Answer

Amazon DynamoDB with a global secondary index on score

Answer

Amazon Neptune with a graph model

Question 3

A data engineer needs to store large volumes of semi-structured JSON data in Amazon S3 and query it with Amazon Athena. The engineer wants to minimize query costs and improve performance. Which action should be taken?

Accepted Answer

Convert the data to columnar format like Apache Parquet and partition it.. Using a columnar format like Parquet with partitioning allows Athena to scan only the necessary columns and partitions, drastically reducing the amount of data scanned and thus the cost. This is the recommended best practice for optimizing Athena queries on S3 data.

Answer

Increase the number of partitions to one per day.

Answer

Store the data in a single large JSON file.

Answer

Use gzip compression on each JSON file.

Question 4

A company uses AWS Glue DataBrew to clean and normalize data from an Amazon S3 bucket before loading it into Amazon Redshift. The data contains PII such as social security numbers. A compliance policy requires that PII be masked in the DataBrew output. Which DataBrew transformation should the engineer use to replace the last four digits of each SSN with asterisks?

Accepted Answer

Use the 'Mask value' transformation with a custom pattern to mask the last four characters.. AWS Glue DataBrew provides a 'Mask value' transformation that can apply custom masking patterns to specific characters. By configuring a pattern to mask the last four digits of the SSN with asterisks, the engineer preserves the first five digits for analytics while complying with the PII masking policy. Other transformations either over-mask, encrypt, or remove the data, failing the specific requirement.

Answer

Use the 'Encrypt' transformation with AWS KMS to encrypt the SSN column.

Answer

Use the 'Remove columns' transformation to drop the SSN column entirely.

Answer

Use the 'Replace value or pattern' transformation to replace the entire SSN with a fixed string.

Question 5

A data engineer needs to transform JSON data from Amazon S3 into Parquet format using AWS Glue. The data contains nested fields. Which Glue feature should the engineer use to define the schema and handle the nested structure?

Accepted Answer

Use the 'Relationalize' transform in a Glue ETL script.. The Relationalize transform in AWS Glue is specifically designed to convert nested JSON data into a relational format. It flattens nested structures by creating separate tables for arrays and nested objects, which can then be written to Parquet. This is the appropriate feature to handle nested fields when transforming JSON to Parquet.

Answer

Use the 'FindMatches' transform to identify duplicates.

Answer

Use the 'DropFields' transform to remove nested fields.

Answer

Use the 'Spigot' transform to write sample data.

Question 6

A data engineer is using AWS Glue to catalog data stored in Amazon S3. The engineer needs to run an AWS Glue ETL job that reads from a large dataset in Parquet format, performs transformations, and writes the output to Amazon Redshift. The job must handle data skew and optimize performance. Which AWS Glue feature should the engineer use to address data skew during the join operation?

Accepted Answer

Configure the job to use the `spark.sql.adaptive.enabled` and `spark.sql.adaptive.skewJoin.enabled` properties.. Data skew in joins can be mitigated by enabling Spark's adaptive query execution (AQE) with skew join optimization. This feature dynamically detects and splits skewed partitions, balancing the workload across executors. Other options do not directly address skew: job bookmarks track processed data, Relationalize flattens nested data, and adding DPUs increases capacity without redistributing data.

Answer

Use the AWS Glue DynamicFrame and apply the `Relationalize` transform to flatten nested data.

Answer

Enable job bookmarks to track processed data and avoid reprocessing.

Answer

Increase the number of DPUs allocated to the job to provide more resources for handling skewed data.

Question 7

A company uses AWS Glue to process JSON logs from S3. The logs have a nested structure and the schema evolves over time. The data engineer needs to ensure the Glue job can handle schema changes without failing. Which configuration should be used?

Accepted Answer

Set the job parameter '--enable-glue-datacatalog' and '--mergeDynamicColumns' to true. Setting '--enable-glue-datacatalog' allows the Glue job to use the Data Catalog as the metastore, and '--mergeDynamicColumns' (or the equivalent '--enable-schema-evolution' in newer Glue versions) instructs the job to dynamically merge new columns from the evolving JSON schema into the existing table schema during runtime, preventing job failures due to schema mismatches. This is specifically designed for nested, schema-evolving data like JSON logs, as it automatically reconciles differences between the source data and the catalog definition.

Answer

Manually update the table schema in the Glue Data Catalog before each run

Answer

Use Spark SQL with a static schema definition in the script

Answer

Enable AWS Glue Schema Registry and define a schema version

Question 8

An e-commerce company uses AWS Glue to run ETL jobs that transform clickstream data from Amazon S3. The job reads Parquet files, performs aggregations, and writes the results to Amazon Redshift. The job runs successfully but takes longer than expected. The data volume is increasing. Which design change would MOST improve the job's performance?

Accepted Answer

Increase the number of Glue worker nodes (DPUs) for the job.. Increasing the number of Glue worker nodes (DPUs) directly scales the distributed processing capacity of the ETL job, allowing it to process larger volumes of Parquet data in parallel. This is the most straightforward way to reduce execution time when data volume is growing, as AWS Glue automatically partitions the workload across the additional workers.

Answer

Write the aggregated results to a single large file instead of multiple partitions.

Answer

Convert the Parquet files to CSV to simplify the schema.

Answer

Replace the Redshift target with Amazon Redshift Spectrum.

Question 9

A company is running a Redshift cluster and wants to improve query performance for a frequently used dashboard. Which THREE approaches are recommended?

Accepted Answer

Apply column compression encoding. Option B is correct because applying column compression encoding reduces the amount of data read from disk, which lowers I/O and speeds up scans for dashboard queries. Option C is correct because defining sort keys on columns used in WHERE clauses enables zone maps to skip irrelevant blocks, dramatically reducing the data scanned for filtered queries. Option E is correct because choosing an appropriate distribution key for large tables colocates related rows on the same node slice, minimizing data movement during joins and aggregations common in dashboards. Option A is not among the marked answers, and concurrency scaling mainly handles concurrent workload spikes rather than improving single-query performance. Option D is not marked because simply adding nodes increases capacity and cost but does not by itself optimize query execution without proper sort, distribution, and encoding design.

Answer

Enable concurrency scaling

Answer

Add more nodes to the cluster

Question 10

A company needs to ingest data from a MySQL database into Amazon S3 in near real-time. The database is running on EC2. The data engineer wants to minimize the impact on the source database. Which service should be used?

Accepted Answer

AWS Database Migration Service (DMS) with ongoing replication. AWS DMS with ongoing replication (change data capture) is the correct choice because it can continuously replicate changes from a MySQL source database to Amazon S3 with minimal performance impact. DMS uses a transactional log-based approach (MySQL binlog) to capture changes as they occur, avoiding heavy SELECT queries on the source. This enables near real-time ingestion without adding significant load to the production database.

Answer

AWS Glue ETL job with a JDBC connection

Answer

Amazon RDS for MySQL with read replica

Answer

AWS Schema Conversion Tool (SCT)

Question 11

A company is building a data lake on Amazon S3 and wants to ingest data from multiple AWS services (CloudTrail, VPC Flow Logs, and ALB logs). The data should be stored in a central S3 bucket with a common partitioning scheme. Which service can be used to collect and centralize this data with minimal configuration?

Accepted Answer

Configure each source service to deliver logs directly to the central S3 bucket.. CloudTrail, VPC Flow Logs, and ALB logs can each be configured to deliver logs directly to a specified S3 bucket, including a central bucket, with no intermediary service required. This approach minimizes configuration overhead and avoids data movement costs, as each service writes natively to S3 using its own built-in delivery mechanism.

Answer

Use AWS Data Pipeline to copy logs from each source S3 bucket to the central bucket.

Answer

Use AWS Glue to crawl the logs from each source and write to a central S3 bucket.

Answer

Set up Amazon Kinesis Data Firehose to ingest logs from each service and write to S3.

Question 12

A data engineer needs to store archival data that is rarely accessed but must be retained for 7 years. The data should be retrievable within 12 hours. Which Amazon S3 storage class is MOST cost-effective?

Accepted Answer

S3 Glacier Deep Archive. S3 Glacier Deep Archive is the most cost-effective storage class for archival data that is rarely accessed and requires a 7-year retention period, with retrieval times up to 12 hours. It offers the lowest storage cost among S3 classes, making it ideal for long-term retention of data that does not need immediate access.

Answer

S3 Intelligent-Tiering

Answer

S3 Glacier Flexible Retrieval

Answer

S3 Standard

Question 13

An organization is using AWS Glue to process sensitive data. The data is stored in S3 with server-side encryption using AWS KMS (SSE-KMS). The Glue job fails with an error indicating that it cannot read the data. The IAM role used by Glue has the following policy. What is missing?

Accepted Answer

The kms:Decrypt permission on the KMS key. The Glue job fails because the IAM role lacks the kms:Decrypt permission on the KMS key used for SSE-KMS encryption. When S3 objects are encrypted with SSE-KMS, any principal reading the object must have both s3:GetObject on the object and kms:Decrypt on the KMS key. Without kms:Decrypt, S3 returns an AccessDenied error even though the s3:GetObject permission is present.

Answer

The s3:GetObject permission on the bucket

Answer

The kms:GenerateDataKey permission on the KMS key

Answer

The kms:ReEncrypt permission on the KMS key

Question 14

Refer to the exhibit. A data engineer queries AWS CloudTrail to investigate a PutObject event. What does the exhibit reveal about the object sensitive.csv?

Accepted Answer

The object was uploaded with server-side encryption using AWS KMS.. The CloudTrail event contains `x-amz-server-side-encryption: aws:kms`, which confirms the object was uploaded with server-side encryption using AWS KMS (SSE-KMS). Option A is incorrect because the event shows a successful upload, not a failure. Option C is incorrect because the event indicates encryption was applied. Option D is incorrect because SSE-S3 would show `AES256`, not `aws:kms`.

Answer

The upload failed due to encryption mismatch.

Answer

The object was not encrypted at rest.

Answer

The object was encrypted with SSE-S3.

Question 15

A company needs to ingest data from multiple SaaS applications (Salesforce, Marketo) into Amazon S3 for analytics. The data volume is moderate (~100 GB per day). The pipeline must handle schema changes, deduplicate records, and provide low latency (under 1 hour). Which THREE services should be used? (Choose THREE.)

Accepted Answer

Amazon AppFlow. Amazon AppFlow is the correct choice because it is a fully managed integration service specifically designed to transfer data from SaaS applications like Salesforce and Marketo to AWS services such as Amazon S3. It supports incremental transfers, handles schema changes automatically via its schema evolution feature, and can achieve sub-hour latency for moderate data volumes (~100 GB/day) without custom coding.

Answer

Amazon Kinesis Data Streams

Answer

AWS Database Migration Service (DMS)

Question 16

Which THREE factors should be considered when choosing a partition key for an Amazon DynamoDB table?

Accepted Answer

If the table has a write-heavy workload, the partition key should distribute writes evenly.. Option B is correct because a write-heavy workload requires the partition key to spread write traffic across many partitions; otherwise a hot partition throttles throughput, since each partition supports a limited write capacity (up to 1,000 WCU per partition). Option C is correct because DynamoDB retrieves items by partition key, so aligning the key with the most common query access pattern enables efficient Query operations instead of expensive Scan operations. Option E is correct because high cardinality produces many distinct partition key values, which distributes items and traffic evenly across partitions and avoids hot partitions. Option A is not a valid factor because item size does not determine partition key choice; DynamoDB partitions data by key value, and large items only consume more capacity, not improve partitioning. Option D is not a valid factor because RCU consumption is driven by item size and consistency model, not by selecting a partition key to minimize reads.

Answer

The partition key should be chosen to maximize the size of items in each partition.

Answer

The partition key should be chosen to minimize read capacity unit consumption.

Question 17

Match each AWS database service to its primary use case.

Accepted Answer

Amazon RDS: Relational database for online transaction processing (OLTP) workloads.. Correct matches: RDS -> OLTP relational, DynamoDB -> NoSQL low-latency, Redshift -> data warehousing, ElastiCache -> in-memory caching. Common confusions include swapping RDS and DynamoDB use cases.

Answer

Amazon RDS: NoSQL key-value database for low-latency applications.

Answer

Amazon DynamoDB: Relational database for OLTP workloads.

Question 18

A financial services company uses AWS Glue ETL jobs to process sensitive customer data stored in Amazon S3. The data is encrypted at rest with SSE-KMS using a customer-managed key. Recently, the security team discovered that the Glue job's IAM role has an overly permissive policy that allows the 'kms:Decrypt' action for all KMS keys in the account. The company wants to follow the principle of least privilege. The Glue job runs on a schedule and reads from a specific S3 bucket. The security team needs to update the IAM policy to restrict KMS decryption to only the specific key used for that bucket. What should they do?

Accepted Answer

Update the policy to allow 'kms:Decrypt' only for the specific KMS key ARN used by the S3 bucket containing the customer data.. To follow least privilege, the IAM role for the Glue job should only have access to decrypt using the specific KMS key that encrypts the S3 bucket containing the customer data. This is done by allowing 'kms:Decrypt' with a resource set to the exact ARN of that key, not a wildcard or all keys. Option A is incorrect because using a wildcard in the key ARN (key/*) still grants access to all keys under that key hierarchy, which is overly permissive. Option B is incorrect because allowing 'kms:Decrypt' with resource '*' would grant access to all keys in the account, violating least privilege. Option D is incorrect because removing 'kms:Decrypt' from the IAM policy would prevent the Glue job from decrypting the data; the job's IAM role needs the permission, and relying solely on S3 bucket policies cannot grant decryption permissions cross-account or for IAM roles.

Answer

Update the policy to allow 'kms:Decrypt' with a resource of 'arn:aws:kms:us-east-1:123456789012:key/*' to cover all keys in the account.

Answer

Update the policy to allow 'kms:Decrypt' with a resource of '*' to ensure the job can always decrypt data.

Answer

Remove the 'kms:Decrypt' action from the policy and rely on S3 bucket policies to grant decryption permissions.

Question 19

Match each AWS data analytics service to its primary function.

Accepted Answer

Amazon Athena: Query data in Amazon S3 using standard SQL. The correct matches are: Amazon Athena for serverless SQL querying on S3, Amazon Redshift for data warehousing, Amazon EMR for big data processing, and Amazon Kinesis for real-time streaming. Common confusions include swapping the roles of Athena and Redshift.

Answer

Amazon Athena: Fully managed data warehouse

Answer

Amazon Redshift: Query data in S3 using SQL

Question 20

A company needs to ingest data from multiple SaaS sources (e.g., Salesforce, Marketo) into Amazon S3 for analytics. Which AWS service is designed for this purpose?

Accepted Answer

Amazon AppFlow. Amazon AppFlow is a fully managed integration service specifically designed to securely transfer data between SaaS applications (like Salesforce, Marketo, Slack, and Zendesk) and AWS services such as Amazon S3 and Amazon Redshift. It supports scheduled, event-driven, or on-demand data ingestion with built-in transformations, filtering, and validation, making it the ideal choice for this use case.

Answer

AWS Transfer Family

Answer

AWS Glue

Answer

AWS DataSync