DEA-C01 Practice Test 31 — 15 Questions

Question 1

Which THREE are valid considerations when troubleshooting data loss in an AWS Glue ETL job? (Choose three.)

Accepted Answer

Job bookmarks may be skipping new data if not configured properly.. Option A is correct because AWS Glue job bookmarks track previously processed data; if a bookmark is misconfigured, stale, or reset, the job can skip new or changed records, causing apparent data loss. Option D is correct because Glue DynamicFrame transformations and write operations can drop or quarantine rows that fail schema or type validation, so records with errors may silently disappear unless error handling is configured. Option E is correct because an incorrect ApplyMapping or column mapping can write source columns into the wrong target fields or omit them, making data appear lost in the target. Option B is not a valid consideration because S3 server-side encryption protects data at rest and does not cause records to be dropped during ETL processing. Option C is not a valid consideration because a low job timeout causes the job to fail or stop, which is a job failure rather than silent data loss.

Answer

Server-side encryption is disabled on the S3 bucket.

Answer

The job timeout is set too low.

Question 2

A data engineer is designing a data ingestion pipeline for a social media analytics platform. The pipeline must ingest tweets in real-time, perform sentiment analysis, and store results in Amazon S3. The sentiment analysis is compute-intensive and must be done as the data arrives. The estimated throughput is 10,000 tweets per second. Which architecture is most suitable?

Accepted Answer

Amazon Kinesis Data Streams with Amazon Kinesis Data Analytics for sentiment analysis, then Kinesis Data Firehose to S3.. The most suitable because Amazon Kinesis Data Streams can ingest up to 10,000 records per second per shard (with shard-level scaling), and Kinesis Data Analytics provides built-in, low-latency stream processing for compute-intensive sentiment analysis using SQL or Apache Flink. Kinesis Data Firehose then reliably buffers and writes the processed results to Amazon S3 without custom code, ensuring near-real-time delivery.

Answer

Amazon SQS with AWS Lambda pollers to process tweets and store in S3.

Answer

Amazon EMR with Spark Streaming to process tweets and write to S3.

Answer

Amazon API Gateway with AWS Lambda to process each tweet and store in S3.

Question 3

A company uses Amazon Redshift for data warehousing. They notice that queries are running slowly, and the STL_LOAD_ERRORS table shows many 'Parse error' entries. The data is loaded from Amazon S3 using COPY commands. What is the MOST likely cause of the parse errors?

Accepted Answer

The source data files have a different schema or delimiter than what is specified in the COPY command.. COPY loads data by parsing each source record according to the FORMAT, DELIMITER, and column definitions supplied in the command. When the actual file layout (column count, order, or delimiter) does not match those specifications, Redshift cannot map fields to columns and writes a 'Parse error' row into STL_LOAD_ERRORS. This is the classic signature of a schema/delimiter mismatch, not a resource or permission problem.

Answer

The Redshift cluster does not have enough compute nodes to process the data.

Answer

The IAM role used by Redshift does not have permission to decrypt the S3 objects.

Answer

The source data files are compressed using an unsupported compression format.

Question 4

A data engineer needs to monitor the number of records processed by an AWS Glue ETL job and send an alert if the count drops below a threshold. Which AWS service should be used to create this custom metric?

Accepted Answer

Amazon CloudWatch. Amazon CloudWatch is the correct service for creating custom metrics because it allows you to publish your own data points, such as the number of records processed by an AWS Glue ETL job. You can use the CloudWatch PutMetricData API or the AWS Glue job script to emit a custom metric, then set an alarm on that metric to trigger an alert when the count drops below a threshold.

Answer

Amazon S3

Answer

AWS Config

Answer

AWS CloudTrail

Question 5

A company wants to ingest data from SaaS applications (e.g., Salesforce, Marketo) into Amazon S3 for analytics. The data volume is moderate and updates occur frequently. Which AWS service is BEST suited for this task?

Accepted Answer

Amazon AppFlow. Amazon AppFlow is purpose-built for ingesting data from SaaS applications such as Salesforce, Marketo, Slack, and Google Analytics into AWS services like Amazon S3. It provides native connectors, supports scheduled or event-driven flows, and handles authentication and incremental transfers without custom code. For moderate-volume, frequently updated SaaS data landing in S3, AppFlow is the most direct and operationally simple fit.

Answer

Amazon Kinesis Data Streams

Answer

AWS Database Migration Service (DMS)

Answer

AWS Glue

Question 6

A company uses Amazon EMR to run Spark jobs on a transient cluster. The jobs process data from S3 and write results back to S3. The team wants to reduce costs by optimizing the cluster. Which action should the team take?

Accepted Answer

Use Spot Instances for the task nodes.. Using Spot Instances for task nodes in a transient EMR cluster significantly reduces compute costs because Spot Instances are spare AWS EC2 capacity offered at up to 90% discount compared to On-Demand. Since transient clusters are terminated after job completion, the risk of Spot Instance interruptions is mitigated—the job can simply be retried on a new cluster if needed. This directly addresses the cost optimization goal without sacrificing job functionality.

Answer

Increase the number of core nodes and use larger instance types.

Answer

Enable EMRFS consistent view.

Answer

Terminate the cluster after each job and manually restart it for the next job.

Question 7

A company uses AWS Glue to process data in Amazon S3. The Glue job fails with an error indicating that the partition keys in the catalog do not match the actual S3 partition structure. What is the most likely cause?

Accepted Answer

The Glue Data Catalog partition metadata is outdated after the S3 structure changed. The Glue Data Catalog stores partition metadata separately from the actual S3 partition layout. When the S3 partition structure changes (e.g., new partitions are added or existing ones are renamed) without updating the catalog, the Glue job reads stale partition metadata, leading to a mismatch error. The job fails because it expects partitions based on the catalog, not the live S3 structure.

Answer

The IAM role does not have permissions to read the S3 data

Answer

The data files are encrypted with SSE-KMS

Answer

The table name in the catalog is different from the one used in the job

Question 8

A company uses Amazon S3 to store customer documents. The data engineer needs to ensure that all objects uploaded to a specific S3 bucket are automatically encrypted with a customer-managed AWS KMS key. What should the data engineer do?

Accepted Answer

Set default encryption on the bucket to use SSE-KMS with the customer-managed key.. Setting default encryption on the S3 bucket to SSE-KMS with the customer-managed key ensures that all objects uploaded without explicit encryption headers are automatically encrypted using that KMS key. This satisfies the requirement without relying on client-side behavior, as S3 applies the encryption server-side at the time of write.

Answer

Use pre-signed URLs for all uploads that include encryption parameters.

Answer

Create a bucket policy that denies uploads without encryption.

Answer

Enable S3 Versioning on the bucket.

Question 9

A company uses Amazon EMR to process large datasets stored in Amazon S3. The data is in Parquet format and partitioned by date. The EMR cluster uses Spark SQL for transformations. Recently, the job has been slow and some tasks are failing due to 'java.lang.OutOfMemoryError'. The cluster has 10 core nodes of type m5.xlarge. Which configuration change would MOST improve performance and stability?

Accepted Answer

Change the core node instance type to r5.xlarge (memory-optimized).. The error 'java.lang.OutOfMemoryError' indicates that the Spark executors are running out of memory during processing. The m5.xlarge instance type provides 16 GiB of memory, but the workload likely requires more memory per task. Switching to r5.xlarge (32 GiB of memory) doubles the available memory per node, reducing memory pressure and preventing task failures, which directly improves stability and performance for memory-intensive transformations.

Answer

Increase the number of Spark partitions using repartition(), but keep the same nodes.

Answer

Increase the number of executor cores in the Spark configuration.

Answer

Enable Kryo serialization in the Spark configuration.

Question 10

A data engineer notices that a nightly AWS Glue ETL job has been failing for the past three days with the error 'Unable to locate credentials'. The job uses an IAM role for execution. What is the most likely cause of this error?

Accepted Answer

The IAM role's trust policy does not include glue.amazonaws.com as a trusted entity.. The error 'Unable to locate credentials' indicates that the AWS Glue job cannot obtain AWS credentials to authenticate API calls. Since the job uses an IAM role for execution, the most likely cause is that the trust policy of that IAM role does not include 'glue.amazonaws.com' as a trusted entity. Without this trust relationship, AWS Glue cannot assume the role and thus has no credentials to sign requests.

Answer

The IAM role does not have an access key attached.

Answer

The S3 bucket name in the job parameters is misspelled.

Answer

The JDBC connection string contains an incorrect password.

Question 11

A data engineer runs the above SQL commands on an Amazon Redshift cluster. The table 'users' is created with DISTSTYLE EVEN. What is the effect of the DISTSTYLE EVEN on query performance?

Accepted Answer

It ensures data is evenly distributed across all nodes to prevent data skew.. DISTSTYLE EVEN in Amazon Redshift distributes rows across all nodes in a round-robin fashion, ensuring each node holds approximately the same amount of data. This prevents data skew, which can cause some nodes to become bottlenecks, and improves overall query performance for workloads that do not benefit from key-based distribution. It is the correct choice because it directly addresses the goal of balanced data distribution.

Answer

It stores all data on a single node for fast local queries.

Answer

It reduces data movement during queries by co-locating data based on user_id.

Answer

It improves join performance when joining on user_id.

Question 12

A company uses Amazon S3 as a data lake. A data engineer needs to ensure that all objects uploaded to the 'incoming' prefix are automatically encrypted at rest using AWS KMS with a specific customer managed key. What is the simplest way to enforce this?

Accepted Answer

Use a bucket policy that denies PutObject requests without the required encryption header.. A bucket policy that denies s3:PutObject unless the request includes the required encryption header (e.g., x-amz-server-side-encryption: aws:kms and x-amz-server-side-encryption-aws-kms-key-id) enforces encryption at upload time with the specified customer managed key. This is the simplest enforcement mechanism because it operates at the bucket level and rejects non-compliant uploads before the object is written. It also works regardless of which client or SDK performs the upload.

Answer

Enable S3 Transfer Acceleration to force encryption in transit.

Answer

Configure S3 Inventory to report on encryption status and alert on non-compliance.

Answer

Enable default encryption on the bucket with SSE-S3.

Question 13

A company is using Amazon RDS for MySQL and needs to reduce read latency for a global user base. Which AWS feature should be implemented?

Accepted Answer

Read Replicas. Amazon RDS Read Replicas allow you to offload read traffic from the primary DB instance to one or more read-only copies, which can be placed in different AWS Regions to reduce read latency for a global user base. Unlike Multi-AZ, which is designed for high availability, Read Replicas directly address read performance and latency by distributing read queries closer to users. Cross-Region Replication for RDS MySQL is achieved through Read Replicas, making option C the correct choice for reducing read latency globally.

Answer

Multi-AZ deployment

Answer

Aurora Auto Scaling

Answer

Cross-Region Replication

Question 14

A company needs to centralize audit logs from multiple AWS accounts into a single S3 bucket. Which service should be used to aggregate these logs?

Accepted Answer

AWS CloudTrail. AWS CloudTrail can be configured to deliver logs from multiple accounts to a single S3 bucket using a trail in the management account. Option C is correct.

Answer

AWS Config

Answer

Amazon Kinesis Data Firehose

Answer

Amazon CloudWatch Logs

Question 15

A company is running an Amazon EMR cluster with Spark for data processing. The data engineer wants to automatically scale the core and task nodes based on the YARN memory and CPU utilization. Which scaling metric should the engineer use for the EMR managed scaling policy?

Accepted Answer

YARNMemoryAvailablePercentage. EMR managed scaling policies are designed around YARN metrics, and YARNMemoryAvailablePercentage is the primary metric used to determine when to add or remove core and task nodes. When available YARN memory drops below a threshold, EMR scales out; when it rises above a threshold, EMR scales in. This directly reflects the resource pressure that Spark executors place on the cluster.

Answer

CPUUtilization

Answer

DiskIOPS

Answer

HDFSUtilization