DEA-C01 Practice Test 48 — 15 Questions

Question 1

A company runs a data lake on Amazon S3 with AWS Glue for ETL. The data is stored in Parquet format and partitioned by date. The data engineer notices that queries using Amazon Athena are scanning large amounts of data even when filtering on the partition column. Which TWO actions would improve query performance? (Choose TWO)

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

Accepted Answer

Ensure that the WHERE clause uses the partition column correctly. Partition pruning requires the WHERE clause to filter on the partition column to reduce data scanned. Option E is correct because enabling predicate pushdown in Athena allows the query engine to push filtering conditions down to the data source, further reducing the amount of data scanned. Option A is incorrect because while Avro is a row-oriented format, it is not more efficient for analytics than Parquet; Parquet is columnar and better for selective queries. Option C is incorrect because CSV is not compressed and would increase data scanned. Option D is incorrect because simply increasing the number of partitions without proper filtering does not improve performance; excessive partitions can even degrade performance due to metadata overhead.

Answer

Use a different file format like Avro

Answer

Convert the data from Parquet to CSV for better compression

Answer

Increase the number of partitions by adding a second partition column

Question 2

A company uses Amazon Redshift for its data warehouse. The data engineer notices that queries are running slower than expected. The system administrator reports that the cluster's disk space is 80% full. Which action should the engineer take to improve query performance?

Accepted Answer

Add more nodes to the cluster to increase storage and compute capacity.. When a Redshift cluster's disk space is 80% full, query performance degrades because Redshift relies on large sequential I/O operations, and high disk utilization forces more random I/O and increases the likelihood of spilling to disk. Adding nodes increases both storage capacity and compute resources, directly alleviating the I/O bottleneck and improving query throughput. This is the recommended scaling action when disk space exceeds 70-80% utilization.

Answer

Redesign the sort keys to optimize query performance.

Answer

Run the VACUUM command to reclaim space.

Answer

Enable concurrency scaling to handle more queries.

Question 3

A data engineer needs to set up a data pipeline that ingests CSV files from an S3 bucket, transforms them using AWS Glue, and loads the results into Amazon Redshift. The pipeline must handle schema evolution and data quality checks. Which combination of services is most appropriate?

Accepted Answer

Use AWS Glue ETL jobs with Glue DataBrew for data quality and write to Redshift. AWS Glue ETL jobs can handle schema evolution through the use of Glue DynamicFrames, and Glue DataBrew provides built-in data quality checks (profiling, validation) that integrate seamlessly. Option A is incorrect because Lambda has timeout and memory limits, making it unsuitable for large-scale transformations. Option B is incorrect because Athena cannot write directly to Redshift; CTAS only writes to S3. Option C is incorrect because Kinesis Data Firehose is designed for streaming data, not batch CSV ingestion from S3.

Answer

Use S3 Events to trigger an AWS Lambda function that writes directly to Redshift

Answer

Use Amazon Athena to query data in S3 and insert results into Redshift via CTAS

Answer

Use Amazon Kinesis Data Firehose to transform and load data into Redshift

Question 4

A company is streaming IoT sensor data from thousands of devices into Amazon Kinesis Data Firehose. The data is then delivered to Amazon S3 for long-term storage. Occasionally, some records fail to be delivered to S3. The company must capture and analyze these failed records. Which TWO actions should be taken? (Choose two.)

Accepted Answer

Configure an AWS Lambda function as a pre-processing step to catch and log failed records.. Configuring an AWS Lambda function as a pre-processing step in Kinesis Data Firehose can catch and log failed records during data transformation. Option D is correct because enabling Amazon CloudWatch Logs for Kinesis Data Firehose captures delivery errors, allowing analysis of failed deliveries. Option B is incorrect because Amazon Kinesis Data Analytics is for real-time analytics, not for handling delivery failures from Firehose. Option C is incorrect because sending failed records to a Kinesis Data Stream would require additional infrastructure and is not the direct way to capture failures; CloudWatch Logs provides the necessary error logging. Option E is incorrect because S3 event notifications are triggered after successful delivery, not for failed records.

Answer

Use Amazon Kinesis Data Analytics to analyze the failed records in real time.

Answer

Send failed records to an Amazon Kinesis Data Stream for reprocessing.

Answer

Set up an S3 event notification to trigger a Lambda function to reprocess failed records.

Question 5

A data engineer needs to set up a new Amazon RDS for MySQL database for a web application. The application experiences variable read traffic and requires low read latency. The engineer needs to minimize downtime during maintenance and provide read scalability. Which configuration meets these requirements?

Accepted Answer

Multi-AZ db.r5.large instance with two Read Replicas. A Multi-AZ deployment provides high availability and automatic failover to minimize downtime during maintenance, while adding two Read Replicas offloads read traffic from the primary instance, reducing read latency and enabling read scalability. The db.r5.large instance size is sufficient for the variable read workload, and Read Replicas can be promoted to standalone instances if needed.

Answer

Multi-AZ db.r5.large instance

Answer

Single-AZ db.r5.large instance

Answer

Single-AZ db.r5.xlarge instance

Question 6

A data engineer is tasked with reducing costs for an Amazon Redshift cluster. The cluster is used for both ETL workloads and BI reporting. The engineer observes that the cluster is over-provisioned during off-peak hours. Which action would be MOST effective in reducing costs while maintaining performance during peak hours?

Accepted Answer

Enable concurrency scaling to automatically add cluster capacity during peak hours.. Concurrency scaling adds additional capacity on demand and is cost-effective for variable workloads. Option A is incorrect because RA3 nodes with managed storage are more about storage scaling and may not directly reduce costs for variable workloads as effectively. Option C is incorrect because Reserved Instances require upfront payment and are best for steady-state, not variable workloads. Option D is incorrect because reducing node count may impact performance during peak hours.

Answer

Switch to RA3 node types for managed storage.

Answer

Purchase Reserved Instances for the cluster.

Answer

Reduce the number of nodes in the cluster.

Question 7

A data engineer notices that an Amazon Kinesis Data Firehose delivery stream is failing to deliver data to an Amazon S3 bucket. The engineer verifies that the S3 bucket exists and that the IAM role attached to the delivery stream has the necessary permissions. What is the MOST likely cause of the failure?

Accepted Answer

No data is being written to the Kinesis Data Firehose delivery stream.. If no data is written to the stream, Firehose has nothing to deliver. Option A is wrong because CloudWatch Logs is for monitoring, not for storing delivery data. Option B is wrong because insufficient permissions would cause an access denied error. Option D is wrong because delivery streams typically use S3 as a destination, not Kinesis Data Streams.

Answer

The delivery stream is configured to deliver to Amazon CloudWatch Logs.

Answer

The IAM role does not have permissions to write to the S3 bucket.

Answer

The delivery stream is configured to deliver to Amazon Kinesis Data Streams instead of S3.

Question 8

An Amazon RDS for PostgreSQL instance is experiencing high CPU utilization and slow query performance. The data engineer suspects that a specific query is causing the problem. The engineer wants to identify the query and analyze its execution plan. Which steps should the engineer take?

Accepted Answer

Enable pg_stat_statements in the PostgreSQL parameter group and query the pg_stat_statements view.. Enabling pg_stat_statements in the PostgreSQL parameter group and querying the pg_stat_statements view provides detailed query execution statistics (e.g., total execution time, calls, rows) and helps identify high‑load queries. The execution plan can then be obtained by running EXPLAIN on the identified query. Option A is incorrect because CloudWatch Logs captures logs but does not provide real‑time query performance details. Option C is incorrect because Enhanced Monitoring shows OS‑level metrics (CPU, memory), not query‑level plans or statistics. Option D is incorrect because RDS Performance Insights identifies top queries by wait events and load but does not directly expose the execution plan; the execution plan is best obtained via EXPLAIN on the specific query identified from pg_stat_statements.

Answer

Enable CloudWatch Logs for the RDS instance and search for slow query logs.

Answer

Enable Enhanced Monitoring and analyze the CPU metrics.

Answer

Use RDS Performance Insights to identify the top queries.

Question 9

A company is using AWS Glue to catalog data stored in Amazon S3. The data is partitioned by year, month, and day. A data analyst reports that new partitions are not automatically discovered by the Glue crawler. The crawler runs on a schedule every hour. What is the MOST likely reason for the missing partitions?

Accepted Answer

The IAM role used by the crawler does not have permission to list the S3 bucket.. The IAM role used by the crawler must have permissions to list the S3 bucket and read its objects. Without s3:ListBucket permission, the crawler cannot discover new partitions in the bucket, even if it runs on schedule. Option B is incorrect because the crawler does not need a Hive metastore connection to discover partitions in S3; it can update the Glue Data Catalog directly. Option C is incorrect because the partition limit is 1,000,000 (not 100,000) per table, and the scenario does not indicate that limit is reached. Option D is incorrect because running the crawler every hour is a reasonable frequency; if permissions are correct, it should discover new partitions at that interval.

Answer

The Glue Data Catalog is not configured to use a Hive metastore.

Answer

The number of partitions exceeds the Glue catalog limit of 100,000.

Answer

The crawler schedule is set to run too frequently.

Question 10

A company uses AWS Glue crawlers to populate the Data Catalog from data in Amazon S3. The crawler fails to update the schema when new columns are added to the CSV files. What is the most likely cause?

Accepted Answer

The crawler uses a custom classifier that defines a fixed schema.. When a crawler uses a custom classifier that defines a fixed schema, it will not recognize new columns added to the CSV files because the custom classifier overrides the schema inference. Option A is incorrect because S3 bucket versioning does not affect crawler schema updates. Option B is incorrect because the crawler configuration to crawl new partitions does not prevent schema updates; it only affects which partitions are crawled. Option C is incorrect because the IAM role would cause permissions errors, not a failure to update schema due to new columns.

Answer

The S3 bucket has versioning enabled.

Answer

The crawler is configured to only crawl new partitions.

Answer

The IAM role for the crawler lacks permissions to read the new columns.

Question 11

A data engineering team is ingesting streaming data from IoT devices into Amazon Kinesis Data Streams. The data is then consumed by an AWS Lambda function that transforms and loads it into Amazon S3. Recently, the team noticed that the Lambda function is failing with throttling errors (HTTP 429) from the Kinesis API. Which configuration change should the team make to resolve this issue?

Accepted Answer

Reduce the batch size and increase the number of shards in the Kinesis stream to increase parallelism.. Reducing the batch size and increasing the number of shards directly addresses the HTTP 429 throttling errors from the Kinesis API. Each shard supports up to 5 read transactions per second and a maximum read rate of 2 MB/s; by increasing shards, you increase the available read throughput, and reducing the batch size lowers the number of records per invocation, preventing the Lambda function from exceeding the per-shard read limits.

Answer

Disable retries on the Lambda function and configure a dead-letter queue for failed records.

Answer

Replace Kinesis Data Streams with Amazon DynamoDB Streams for ingestion.

Answer

Increase the batch size in the Lambda event source mapping to reduce the number of invocations.

Question 12

An IAM policy is attached to a user who tries to upload an object to the S3 bucket example-bucket using the AWS CLI without specifying the --server-side-encryption flag. What will happen?

Accepted Answer

The upload fails with an AccessDenied error.. The IAM policy denies s3:PutObject unless the request includes the `x-amz-server-side-encryption` header with a value of `AES256`. Since the user did not specify `--server-side-encryption` in the AWS CLI command, the request lacks this required header, causing S3 to evaluate the policy and return an AccessDenied error. The upload fails before any default encryption setting on the bucket is applied.

Answer

The upload succeeds and the object is encrypted with SSE-S3 by default.

Answer

The upload fails because the user does not have permission to use KMS.

Answer

The upload succeeds because the policy allows s3:PutObject.

Question 13

A data engineer is building a data ingestion pipeline that uses AWS Lambda to process records from Amazon Kinesis Data Streams. The Lambda function writes the processed data to Amazon DynamoDB. Which TWO factors affect the maximum number of concurrent Lambda executions for this stream? (Choose TWO.)

Accepted Answer

Batch size configured for the Lambda event source mapping. Correct options: D and E. The maximum number of concurrent Lambda executions for processing a Kinesis stream is determined by the number of shards in the stream, because each shard can have at most one concurrent Lambda invocation processing records from that shard. The batch size configured for the Lambda event source mapping also affects concurrency: a smaller batch size can lead to more invocations per shard over time, as each shard processes batches sequentially. Options A (DynamoDB read capacity units) and B (Lambda memory allocation) do not directly limit concurrency from the stream. Option C (stream name) is just an identifier and has no effect on concurrency.

Answer

DynamoDB table's read capacity units

Answer

Lambda function's memory allocation

Answer

Kinesis stream name

Question 14

A data engineer is designing a multi-region disaster recovery plan for an Amazon DynamoDB table. The table stores critical user profile data and must have a Recovery Point Objective (RPO) of less than 1 minute and a Recovery Time Objective (RTO) of less than 5 minutes. Which solution meets these requirements?

Accepted Answer

Use DynamoDB global tables to replicate data to another region.. DynamoDB global tables provide active-active multi-region replication with sub-second latency, ensuring that data written in one region is automatically replicated to other regions within seconds. This meets the RPO of less than 1 minute and RTO of less than 5 minutes because the table is already available in the secondary region for immediate reads and writes, with no manual restore or failover steps required.

Answer

Configure DynamoDB Streams and a Lambda function to replicate data to another region.

Answer

Use DynamoDB on-demand backup and restore to another region.

Answer

Enable point-in-time recovery (PITR) and restore to another region.

Question 15

A data engineer is troubleshooting an issue where an AWS Glue ETL job fails when trying to read data from an S3 bucket encrypted with SSE-KMS. The job has an IAM role that includes `kms:Decrypt` permission. What is the most likely reason for the failure?

Accepted Answer

The KMS key policy does not allow the Glue job to use the key. The most likely reason for the failure, even with kms:Decrypt permission in the IAM role, is that the KMS key policy explicitly denies the Glue job's IAM role access to the key. AWS Glue ETL jobs can read SSE-KMS encrypted S3 objects without additional configuration; the job's IAM role must be allowed by the key policy. Option A is incorrect because the IAM role likely has s3:GetObject permission. Option C is incorrect because cross-region S3 access is permitted with proper permissions. Option D is incorrect because Glue does not require explicit KMS key configuration for reading; the key is identified in the S3 object metadata, and the job's IAM role is trusted to use the key based on key policy.

Answer

The IAM role does not have s3:GetObject permission

Answer

The S3 bucket is in a different AWS region than the Glue job

Answer

The Glue job is not configured to use the KMS key for decryption