View Full Amazon AWS Certified Data Engineer – Associate DEA-C01 Exam Dumps and Practice Test Dumps.

 

Question 281:

A company needs to process a large dataset using distributed computing and wants to run Apache Spark workloads on AWS. Which AWS service is MOST appropriate?

1. Amazon Athena
2. Amazon EMR
3. Amazon Macie
4. AWS Secrets Manager

Correct Answer: 2

Explanation:

Amazon EMR is a managed cluster platform designed to run big data frameworks such as Apache Spark, Apache Hadoop, Hive, and related technologies. It is appropriate when data engineers need distributed processing for large datasets and require control over cluster configuration and processing frameworks. Athena is a serverless SQL query service primarily used to query data in locations such as Amazon S3. Macie focuses on sensitive-data discovery, while Secrets Manager securely stores credentials. Therefore, Amazon EMR is the most appropriate AWS service for running large-scale Apache Spark workloads that require distributed processing.

Question 282:

A data engineer wants to ensure that an Amazon S3 bucket containing sensitive data cannot be accessed using unencrypted HTTP requests. Which bucket policy condition should be used?

1. aws:SecureTransport
2. aws:username
3. aws:CurrentTime
4. aws:SourceVpc

Correct Answer: 1

Explanation:

The aws:SecureTransport condition key can be used in an Amazon S3 bucket policy to require requests to use secure transport such as HTTPS. A common security pattern is to deny requests when aws:SecureTransport is false. This helps protect data while it is transmitted between clients and AWS. The other condition keys serve different purposes and do not directly enforce HTTPS transport. Using this policy is especially important for sensitive data because it provides an additional layer of protection against insecure network communication. Therefore, aws:SecureTransport is the correct condition for enforcing secure transport.

Question 283:

A company wants to store analytical data in Amazon S3 in a format that supports efficient compression and column pruning. Which format should the data engineer choose?

1. CSV
2. Plain text
3. Parquet
4. XML

Correct Answer: 3

Explanation:

Apache Parquet is a columnar storage format designed for efficient analytical workloads. Because data is stored by column, query engines such as Amazon Athena can read only the columns required by a query, reducing the amount of data scanned. Parquet also supports efficient compression, which can reduce storage requirements and query costs. CSV and plain text are generally row-oriented and do not provide the same level of column pruning. XML is also less efficient for large-scale analytical processing. Therefore, Parquet is the best choice when the requirements include efficient compression and column-level query optimization.

Question 284:

A data engineer needs to create a centralized metadata repository containing table definitions and schemas for datasets stored in Amazon S3. Which AWS service should be used?

1. AWS Glue Data Catalog
2. Amazon CloudWatch
3. Amazon Macie
4. AWS Step Functions

Correct Answer: 1

Explanation:

The AWS Glue Data Catalog is a centralized metadata repository that stores information about datasets, including table definitions, schemas, locations, and other metadata. Services such as Amazon Athena and AWS Glue can use this catalog to understand and query data stored in locations such as Amazon S3. CloudWatch is focused on monitoring and observability, Macie discovers sensitive data, and Step Functions orchestrates workflows. The Glue Data Catalog is therefore particularly useful in data lake architectures where multiple analytics services need a consistent understanding of shared datasets. Hence, AWS Glue Data Catalog is the correct choice.

Question 285:

A data engineer needs to combine customer records with matching order records and wants only customers who have at least one matching order. Which SQL join should be used?

1. FULL OUTER JOIN
2. LEFT JOIN
3. RIGHT JOIN
4. INNER JOIN

Correct Answer: 4

Explanation:

An INNER JOIN returns only rows where the join condition matches in both tables. In this scenario, the requirement is to return customers who have at least one corresponding order. Customers without any matching order should be excluded, making an INNER JOIN the appropriate choice. A LEFT JOIN would retain all customers even if they have no orders, while a RIGHT JOIN would prioritize all records from the orders table. A FULL OUTER JOIN would include unmatched records from both sides. Therefore, an INNER JOIN is the correct SQL operation for returning only customers with matching orders.

Question 286:

A company needs to process millions of records and remove duplicate transactions based on a unique transaction ID before loading the data into an analytical system. Which approach is MOST appropriate?

1. Use a deduplication step based on the transaction ID
2. Randomly delete records
3. Disable data validation
4. Increase the S3 storage capacity

Correct Answer: 1

Explanation:

Deduplication based on a unique transaction ID ensures that only one record for each transaction is retained. An ETL process can group or identify records using the transaction ID and apply a rule to select the appropriate record when duplicates exist. This is important for maintaining accurate analytical results, especially when source systems or streaming pipelines can deliver the same event more than once. Random deletion can remove valid records, disabling validation does not solve duplicate data, and increasing storage capacity has no effect on data quality. Therefore, using the transaction ID as the basis for deduplication is the most appropriate approach.

Question 287:

A data pipeline needs to notify an operations team whenever an AWS Glue job fails. Which combination provides an appropriate event-driven solution?

1. Athena and S3
2. EventBridge and Amazon SNS
3. Macie and DynamoDB
4. Redshift and Route 53

Correct Answer: 2

Explanation:

Amazon EventBridge can detect AWS service events, including events associated with AWS Glue job execution, and route matching events to targets such as Amazon SNS. SNS can then send notifications to email, applications, or other subscribers. This creates a managed event-driven alerting mechanism without requiring custom polling infrastructure. Athena and S3 do not provide the same event-driven notification pattern, while Macie and DynamoDB are unrelated to Glue job failure notifications. Redshift and Route 53 also do not provide the required workflow. Therefore, using EventBridge to detect the failure and SNS to notify the operations team is an appropriate solution.

Question 288:

A company wants to encrypt data stored in Amazon S3 but does not need to manage encryption keys itself. Which encryption option is MOST appropriate?

1. SSE-KMS with a customer managed key
2. Client-side encryption
3. SSE-S3
4. No encryption

Correct Answer: 3

Explanation:

SSE-S3 provides server-side encryption for Amazon S3 objects using keys managed by Amazon S3. It is appropriate when an organization wants encryption at rest without taking responsibility for managing the encryption keys itself. SSE-KMS provides additional key-management capabilities through AWS KMS and is useful when organizations need greater control over keys and permissions. Client-side encryption requires the customer to perform encryption before uploading the data. Leaving data unencrypted does not meet the requirement. Therefore, SSE-S3 is the simplest appropriate option when encryption is required but customer-managed key administration is unnecessary.

Question 289:

A data engineer needs to store frequently accessed application data in memory to reduce the load on a relational database. Which AWS service is BEST suited for this requirement?

1. Amazon S3
2. Amazon ElastiCache
3. Amazon Athena
4. AWS Glue

Correct Answer: 2

Explanation:

Amazon ElastiCache provides managed in-memory caching using technologies such as Redis and Memcached. It can store frequently accessed data in memory, allowing applications to retrieve information much faster than repeatedly querying a relational database. This can reduce database load and improve application response times. Amazon S3 is object storage, Athena is a serverless analytical query service, and AWS Glue provides data integration and ETL capabilities. Therefore, ElastiCache is the most appropriate service when the goal is to cache frequently accessed application data in memory and reduce pressure on the primary database.

Question 290:

A data engineer wants to query only records where a column contains NULL values. Which SQL condition should be used?

1. column = NULL
2. column == NULL
3. column IS NULL
4. column LIKE NULL

Correct Answer: 3

Explanation:

SQL uses IS NULL to test whether a column contains a NULL value. NULL represents the absence or unknown value rather than a normal value that can be compared using the equals operator. Therefore, expressions such as column = NULL do not correctly identify NULL values. The correct syntax is column IS NULL. Similarly, IS NOT NULL can be used to find rows where a value exists. This distinction is important when building data-quality checks, filtering incomplete records, or analyzing missing values. Therefore, column IS NULL is the correct SQL condition.

Question 291:

A company wants to automatically move objects from S3 Standard to S3 Glacier Flexible Retrieval after 90 days. Which feature should be configured?

1. S3 Lifecycle rule
2. S3 Access Point
3. S3 Object Lock
4. S3 Select

Correct Answer: 1

Explanation:

Amazon S3 Lifecycle rules can automatically transition objects between storage classes based on conditions such as object age. A rule can be configured to move objects from S3 Standard to S3 Glacier Flexible Retrieval after 90 days, reducing storage costs for data that becomes less frequently accessed. Access Points provide controlled access to S3 data, Object Lock protects objects against deletion or modification during retention periods, and S3 Select allows applications to retrieve subsets of object data. Therefore, an S3 Lifecycle rule is the correct solution for automatically transitioning objects to a lower-cost archival storage class.

Question 292:

A data engineer needs to calculate the average transaction amount for each customer using SQL. Which SQL construct should be used?

1. ORDER BY only
2. GROUP BY with AVG()
3. DISTINCT only
4. WHERE without aggregation

Correct Answer: 2

Explanation:

The AVG() aggregate function calculates an average value, while GROUP BY divides rows into groups based on one or more columns. To calculate the average transaction amount separately for each customer, the query should group records by customer and apply AVG() to the transaction amount. For example, the logical pattern is SELECT customer_id, AVG(amount) FROM transactions GROUP BY customer_id. ORDER BY sorts results but does not calculate averages, DISTINCT removes duplicates, and WHERE filters records. Therefore, GROUP BY combined with AVG() is the correct SQL approach.

Question 293:

A company wants to retain an exact copy of incoming source data before applying any transformations. Which data lake design approach is BEST?

1. Transform all data immediately and discard the source
2. Store the source data in a raw zone
3. Store only aggregated results
4. Keep data only in the presentation layer

Correct Answer: 2

Explanation:

A raw zone preserves source data in its original or minimally modified form before transformations are applied. Keeping this layer provides an authoritative copy that can be reprocessed when transformation logic changes or when data-quality problems need investigation. It also supports auditing and reproducibility because engineers can trace transformed datasets back to the original source records. Immediately discarding raw data can make recovery and reprocessing difficult. Aggregated and presentation layers are designed for specific analytical needs rather than preserving source information. Therefore, storing incoming data in a raw data lake zone is the best architecture for preserving the original source data.

Question 294:

A data engineer needs to remove leading and trailing spaces from a string column before performing comparisons. Which SQL function should be used?

1. TRIM()
2. COUNT()
3. AVG()
4. GROUP BY

Correct Answer: 1

Explanation:

The SQL TRIM() function removes leading and trailing whitespace from string values. This is useful in data-cleaning operations because source systems may contain accidental spaces around names, identifiers, or other text fields. Removing unnecessary whitespace can help ensure that string comparisons, joins, filters, and deduplication operations work correctly. COUNT() calculates the number of records or values, AVG() calculates an average, and GROUP BY organizes records into groups. Therefore, TRIM() is the correct function when the requirement is to clean leading and trailing spaces from a string column.

Question 295:

A data pipeline should continue processing even if one transient API request fails. The system should retry the request and eventually move the failure to an error-handling path if retries are exhausted. Which approach is BEST?

1. Disable retries
2. Retry indefinitely without handling errors
3. Use retry and catch/error-handling logic
4. Stop all pipeline processing immediately

Correct Answer: 3

Explanation:

Reliable data pipelines should distinguish between transient failures and permanent failures. Retry logic can handle temporary problems such as throttling or short-lived service interruptions. However, retries should have a defined limit so that a permanently failing operation does not block the entire workflow indefinitely. After retries are exhausted, catch or error-handling logic can route the failure to a dead-letter queue, quarantine location, notification system, or other remediation process. Disabling retries reduces resilience, infinite retries can create stuck workflows, and immediately stopping the pipeline may unnecessarily interrupt unrelated processing. Therefore, controlled retries combined with error handling is the best approach.

Question 296:

A company wants to analyze AWS API activity to determine which user deleted an Amazon S3 object. Which service should the data engineer use?

1. Amazon Macie
2. AWS CloudTrail
3. Amazon Athena only
4. AWS Glue Crawler

Correct Answer: 2

Explanation:

AWS CloudTrail records AWS API activity and can provide information about actions performed against AWS resources. When an S3 object deletion is captured in CloudTrail, the event can contain information such as the identity that performed the operation, the time of the request, the API action, and other event details. This makes CloudTrail useful for security investigations and auditing. Macie focuses on sensitive-data discovery, Glue Crawlers discover dataset schemas, and Athena is a query service rather than the primary source of AWS API audit events. Therefore, CloudTrail is the appropriate service for determining who performed an S3 deletion operation.

Question 297:

A data engineer wants to query a very large Amazon S3 dataset efficiently. The dataset is stored in Parquet and partitioned by date. The query needs only three columns and one specific date. Which combination provides the greatest optimization?

1. JSON format and SELECT *
2. Parquet, partition pruning, and column pruning
3. CSV and no filtering
4. XML and full-table scanning

Correct Answer: 2

Explanation:

Using Parquet together with partition pruning and column pruning provides multiple levels of query optimization. Partition pruning allows Athena to skip data outside the requested date partition. Column pruning allows Athena to read only the three required columns rather than all columns in the dataset. Parquet itself is a columnar format that supports efficient storage and compression. In contrast, selecting all columns and scanning all partitions increases the amount of data processed. JSON, CSV, and XML generally provide fewer analytical optimization benefits. Therefore, combining Parquet with partition and column pruning is the most efficient approach.

Question 298:

A company wants to run a serverless SQL query against data stored in Amazon S3 and does not want to provision a data warehouse. Which service should be used?

1. Amazon Athena
2. Amazon Redshift provisioned cluster
3. Amazon EMR cluster
4. Amazon RDS

Correct Answer: 1

Explanation:

Amazon Athena provides serverless SQL querying directly against data stored in Amazon S3. It does not require the data engineer to provision or manage database servers, clusters, or traditional data warehouse infrastructure. Athena is well suited for ad hoc analysis, data lake queries, and interactive exploration of structured and semi-structured data. A provisioned Redshift cluster and EMR cluster require infrastructure configuration, while RDS is designed for transactional relational database workloads. Therefore, Amazon Athena is the best choice when the requirement is to query S3 data using SQL without provisioning a data warehouse or processing cluster.

Question 299:

A data engineer needs to securely retrieve a database password from an application without storing the password directly in application code. Which AWS service should be used?

1. Amazon CloudWatch
2. Amazon Athena
3. AWS Secrets Manager
4. Amazon S3 Glacier

Correct Answer: 3

Explanation:

AWS Secrets Manager securely stores sensitive information such as database passwords, API keys, and other credentials. Applications can retrieve the secret at runtime using IAM permissions rather than embedding the password directly in source code. Secrets Manager also supports secret rotation for supported services, helping reduce the security risk associated with long-lived credentials. CloudWatch is used for monitoring and logging, Athena provides analytical queries, and S3 Glacier is intended for archival storage. Therefore, Secrets Manager is the appropriate service for securely storing and retrieving a database password used by an application.

Question 300:

A data engineer needs to join two datasets while retaining every record from the first dataset, including records that have no match in the second dataset. Which SQL join should be used?

1. INNER JOIN
2. FULL OUTER JOIN
3. RIGHT JOIN
4. LEFT JOIN

Correct Answer: 4

Explanation:

A LEFT JOIN returns every record from the left or first table and matching records from the right table. If a record in the first table has no corresponding match in the second table, the columns from the second table are returned as NULL. This makes LEFT JOIN useful when the complete set of records from the primary dataset must be retained regardless of whether related data exists. INNER JOIN removes unmatched records, RIGHT JOIN prioritizes the right table, and FULL OUTER JOIN retains unmatched records from both tables. Therefore, LEFT JOIN is the correct choice for this requirement.