DEA-C01 Practice Test 42 — 15 Questions

Question 1

A data engineer is using AWS Glue to read a large dataset from Amazon S3 and write it to Amazon Redshift. The job intermittently fails with 'Communication link failure' errors during the write phase. The dataset is several hundred gigabytes and the Redshift cluster is under heavy query load. Which change is MOST likely to resolve the failures while preserving data integrity?

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

Accepted Answer

Write the transformed data to Amazon S3 in Parquet, then use a Redshift COPY command from S3 instead of a direct JDBC write.. Staging data in S3 and using the Redshift COPY command is the recommended pattern for bulk loads. It minimizes open connections, leverages Redshift's parallel load capability, and provides transactional integrity. Increasing workers, extending timeouts, or switching to Spectrum does not resolve the underlying contention or integrity concerns during direct writes.

Answer

Switch the write operation to use Redshift Spectrum and query the S3 data directly without loading.

Answer

Increase the Glue job's number of workers and enable auto-scaling.

Answer

Configure the Glue connection to use a Redshift JDBC URL with a longer socketTimeout and retry the write.

Question 2

A data engineer is configuring an AWS Glue crawler to catalog CSV files stored in Amazon S3. The files are organized in prefixes by year and month, and the engineer wants the crawler to detect new partitions automatically and avoid re-crawling unchanged partitions. (Choose two.)

Accepted Answer

Configure the crawler to use incremental crawling so it processes only folders added since the last crawl.. Incremental crawling limits each crawler run to newly added folders, so previously cataloged partitions are not rescanned, reducing time and cost. Hive-style key=value prefixes make the year and month directories recognizable as partitions so the crawler can populate them correctly in the Data Catalog. Together they deliver automatic partition detection without reprocessing unchanged data.

Answer

Set the crawler's schema change policy to update the table definition in the Data Catalog.

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Add a path in the crawler configuration that points to each year and month prefix individually.

Answer

Create a partition index on the Data Catalog table to speed up partition filtering.

Question 3

A data engineer is configuring an AWS Glue crawler against an Amazon S3 path that contains CSV files with inconsistent column counts across files. The crawler keeps creating multiple tables for the same data and the engineer wants a single table with a merged schema. Which crawler configuration should the engineer change?

Accepted Answer

Set the crawler's 'Create a single schema for each S3 path' option to true.. The 'Create a single schema for each S3 path' crawler setting merges compatible schemas found under the same S3 path into one table. For CSV files with inconsistent column counts, enabling this option produces a single table with the union of compatible columns, eliminating the multiple tables the crawler was creating.

Answer

Increase the crawler's 'Maximum number of tables' limit in the crawler configuration.

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Enable the crawler's 'Update all new and existing partitions with metadata from the table' option.

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Change the crawler's classifier to a custom classifier that forces all columns to string type.

Question 4

Which TWO statements about Amazon Redshift data distribution are correct? (Choose two.)

Accepted Answer

KEY distribution places rows with the same distribution key on the same slice. In Amazon Redshift, KEY distribution places all rows with the same distribution key value on the same slice (compute node segment). This ensures that join operations on the distribution key are collocated, reducing data movement across the network and improving query performance.

Answer

DISTSTYLE is a distribution style option

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AUTO distribution always chooses EVEN

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ALL distribution distributes data across all slices

Question 5

A data engineer is building an AWS Glue ETL job that reads JSON files from Amazon S3, flattens nested arrays, and writes Parquet to another S3 bucket. The job runs daily and processes about 2 TB. The engineer notices that job runs are failing intermittently with OutOfMemory errors during the shuffle phase. The job uses 10 G.1X workers. Which change should the engineer make to resolve the memory failures while minimizing cost?

Accepted Answer

Switch the worker type to G.2X to provide more memory and disk per worker.. OutOfMemory errors during the shuffle phase of a Glue job indicate insufficient per-worker memory. G.2X workers provide double the memory and disk of G.1X, which is the most direct and cost-effective fix for shuffle-heavy transformations such as flattening nested arrays. Reducing workers or changing the source format does not address the memory bottleneck and can worsen the problem.

Answer

Reduce the number of workers from 10 to 5 to lower concurrency.

Answer

Convert the source JSON files to CSV before running the Glue job.

Answer

Enable AWS Glue job bookmarks to skip previously processed files.

Question 6

A data engineer is configuring an AWS Glue job bookmark on a job that reads partitioned Parquet data from Amazon S3 and writes to another S3 location. The engineer notices that reprocessing keeps occurring and wants the bookmark to correctly skip already-processed data. Which two actions should the engineer take? (Choose two.)

Accepted Answer

Ensure the job reads from a source that supports bookmarks, such as S3 or the Glue Data Catalog, rather than an unsupported source. Reprocessing under a bookmark usually means the feature is not actually tracking the source. The bookmark must be enabled on the job and the source must be a supported type such as S3 or the Glue Data Catalog, with no manual reset in between. Confirming both restores incremental processing so already-consumed partitions are skipped.

Answer

Increase the number of worker nodes so the bookmark commits faster between runs

Answer

Enable the Glue Data Catalog schema evolution setting on the job

Answer

Convert the output to a single unpartitioned file so the bookmark can track it

Question 7

A data engineer is troubleshooting an AWS Glue job that writes data to an Amazon S3 bucket in Parquet format. The job runs successfully but the output files are smaller than the configured 'groupFiles' size. The engineer has set 'groupFiles' to 'inPartition' and 'groupSize' to 1 GB. The input data is 10 GB in a single partition. What is the most likely reason for the small files?

Accepted Answer

The 'groupFiles' parameter only affects the input read phase, not the output write phase.. 'groupFiles' only works when the input data is already small and needs to be coalesced. However, if the input is large and the job writes output, the output file size is determined by the number of Spark partitions, not grouping. The grouping feature only applies to reading input files. Option A is wrong because the setting is correct. Option C is wrong because grouping is a read-time feature, not write-time. Option D is wrong because grouping does not require repartitioning.

Answer

The 'groupFiles' parameter is deprecated in the current Glue version.

Answer

The 'groupFiles' parameter is misspelled or set incorrectly.

Answer

The engineer must also set 'repartition' to 1 to merge output files.

Question 8

A data engineer is configuring an AWS Glue ETL job that reads semi-structured JSON from Amazon S3 and must flatten nested arrays before loading into Amazon Redshift. During testing, the job fails with an AnalysisException stating that a column named 'events' cannot be resolved, even though the AWS Glue Data Catalog table shows the column. The engineer confirms the catalog table was created by a crawler and the S3 data is present. Which action will most directly resolve the schema resolution failure?

Accepted Answer

Call resolveChoice with the 'MatchCatalogSchema' option on the DynamicFrame before referencing the nested column.. The failure occurs because Spark's inferred schema at read time diverges from the Data Catalog definition created by the crawler, so nested fields such as 'events' are not resolvable in the DynamicFrame. Applying resolveChoice with MatchCatalogSchema reconciles the runtime schema with the catalog, restoring the column. Capacity changes and file grouping do not alter schema resolution, and crawlers do not perform format conversion.

Answer

Increase the number of DPUs allocated to the Glue job so the executor can read the full nested JSON payload.

Answer

Enable the 'Group files' option in the crawler and re-crawl the S3 prefix to consolidate the JSON into a single object.

Answer

Convert the source JSON to Parquet with an AWS Glue crawler and then point the job at the Parquet table.

Question 9

A company ingests streaming data into Amazon Kinesis Data Streams. Producers write records using the PutRecords API with explicit partition keys based on customer ID. A data engineer observes that a few shards are consistently at 100 percent write throughput while others are underutilized, causing throttling. Which action should the engineer take to distribute the load more evenly?

Accepted Answer

Change the partition key to include a random suffix or use a higher-cardinality key.. Hot shards occur when partition keys are skewed, causing the hash function to map most traffic to a few shards. Including a random suffix or using a higher-cardinality key distributes records more evenly across all shards. Adding shards or changing the write API does not alter the key-to-shard mapping, and enhanced fan-out only affects consumer reads, not producer write distribution.

Answer

Enable enhanced fan-out on the stream to give consumers dedicated throughput.

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Switch the producers to use the PutRecord API instead of PutRecords.

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Increase the number of shards in the stream to match the number of partition keys.

Question 10

A data engineer is using AWS Glue Studio to build an ETL job that reads semi-structured JSON from Amazon S3. The source files contain nested arrays and inconsistent keys, and the engineer wants the job to automatically infer the schema at runtime without a Data Catalog table. Which transform or configuration should the engineer use to read the data most reliably?

Accepted Answer

Create a DynamicFrame with the from_options method and set the format to "json" with the recurse parameter enabled.. Reading semi-structured JSON from S3 without a Data Catalog entry is best done with DynamicFrame.from_options, specifying the JSON format and enabling recursion so nested arrays and inconsistent keys are handled during inference. This avoids the rigidity of hard-coded schemas and the catalog dependency of crawler-based reads.

Answer

Use a Spark DataFrame read with spark.read.schema() and a hard-coded StructType that matches all nested fields.

Answer

Configure the job to use the ApplyMapping transform with a manually defined mapping for every nested attribute before reading.

Answer

Run an AWS Glue crawler against the S3 prefix and then use the catalog table in the job with a from_catalog read.

Question 11

A data engineer needs to ensure that an Amazon Redshift cluster encrypts data at rest using a customer-managed AWS KMS key. Which configuration step is required?

Accepted Answer

Create a new cluster and specify a customer-managed KMS key for encryption.. To encrypt an Amazon Redshift cluster with a customer-managed KMS key, you must specify that key at cluster creation time; Redshift does not allow you to change the encryption key of an existing cluster after it is created. The customer-managed key gives you control over key rotation, grants, and audit via CloudTrail, which is required when the default AWS-managed key (aws/redshift) does not satisfy compliance.

Answer

Create a new cluster and select the default AWS managed key for encryption.

Answer

Use AWS CloudHSM to generate a key and attach it to the cluster.

Answer

Enable encryption on the existing cluster by modifying the cluster configuration.

Question 12

A data engineer needs to ingest streaming data from thousands of devices sending JSON messages via HTTP POST. The data should be stored in Amazon S3 with minimal latency and also be available for real-time analytics. Which combination of services is MOST appropriate?

Accepted Answer

Amazon API Gateway, Amazon Kinesis Data Streams, and Kinesis Data Firehose.. Amazon API Gateway provides a scalable, managed HTTP endpoint that can receive JSON POST requests from thousands of devices and proxy them directly into Amazon Kinesis Data Streams. Kinesis Data Streams ingests the data with low latency and makes it available for real-time analytics via Kinesis Client Library (KCL) applications, AWS Lambda, or Kinesis Data Analytics. Kinesis Data Firehose then delivers the stream to Amazon S3 with minimal buffering (as low as 60 seconds), satisfying both the real-time and storage requirements.

Answer

Amazon DynamoDB with DynamoDB Streams and Lambda.

Answer

Amazon SQS and AWS Lambda to write to S3.

Answer

AWS Lambda directly writing to S3 via API Gateway.

Question 13

A company needs to ingest data from an on-premises Oracle database into Amazon S3 on a daily basis. The data volume is about 100 GB per day. Which AWS service is BEST suited for this task?

Accepted Answer

Use AWS Database Migration Service (DMS) to replicate data to S3.. AWS Database Migration Service (DMS) can continuously replicate data from Oracle to S3, and it supports full load and change data capture (CDC). Option A (AWS DataSync) is for file-based transfers, not database replication. Option B (Amazon Kinesis Data Firehose) is for streaming data, not database pull. Option D (AWS Glue) is for ETL but does not natively support continuous CDC from Oracle.

Answer

Use AWS DataSync to copy the database files to S3.

Answer

Use Amazon Kinesis Data Firehose with a database connector.

Answer

Use AWS Glue to extract data from Oracle and write to S3.

Question 14

A data engineer is designing a data lake on Amazon S3. The compliance team requires that objects be automatically deleted after 7 years. Additionally, objects must be transitioned to Amazon S3 Glacier Instant Retrieval after 30 days to reduce costs. Which S3 lifecycle policy configuration meets these requirements?

Accepted Answer

Transition to Glacier Instant Retrieval after 30 days, then expire after 2555 days.. The requirement is to transition objects to Glacier Instant Retrieval after 30 days and delete them after 7 years. 7 years equals 2555 days (7 × 365). Option B correctly specifies transition after 30 days and expiration after 2555 days. This meets both the cost optimization and compliance retention requirements.

Answer

Transition to Glacier Instant Retrieval after 30 days, then expire after 90 days.

Answer

Transition to Glacier Deep Archive after 30 days, then expire after 7 years.

Answer

Transition to S3 Standard-IA after 30 days, then expire after 7 years.

Question 15

A data engineer is troubleshooting a DMS task that is replicating data from an on-premises Oracle database to an RDS for MySQL instance. The task is failing with 'ORA-1555: snapshot too old' error. What is the best course of action?

Accepted Answer

Increase the UNDO tablespace size and set UNDO_RETENTION to a higher value.. ORA-1555 'snapshot too old' occurs when Oracle cannot reconstruct a consistent read image because the required undo data has been overwritten. Increasing the UNDO tablespace size and raising UNDO_RETENTION gives Oracle more undo history to retain, allowing the long-running DMS full-load read to complete without losing its snapshot. This directly addresses the root cause of insufficient undo retention on the source.

Answer

Disable full supplemental logging on the source tables.

Answer

Increase the size of the redo logs on the source database.

Answer

Enable batch optimized apply on the DMS task.