DEA-C01 Practice Test 32 — 15 Questions

Question 1

A company runs a data lake on Amazon S3 with AWS Glue and Amazon Athena. The data engineer notices that queries are slow and scanning large amounts of data. Which THREE actions should the engineer take to optimize query performance and reduce costs?

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

Accepted Answer

Compress data files using gzip or snappy.. Option C is correct because compressing data files with gzip or snappy reduces the total bytes stored and scanned, and Athena charges and performs based on data scanned, so smaller files lower both query latency and cost. Option D is correct because partitioning the S3 data by frequently filtered columns such as date or region enables partition pruning, so Athena reads only the relevant prefixes instead of scanning the entire table. Option E is correct because columnar formats like Parquet or ORC let Athena read only the columns referenced in the query and provide better compression and predicate pushdown, dramatically reducing scanned data compared to row-based formats like CSV or JSON. Option A is not appropriate because increasing the Athena query timeout only allows long-running queries to finish; it does not reduce the amount of data scanned or improve performance. Option B is not appropriate because adding DPUs to the Glue job speeds up ETL processing, not Athena query performance or the volume of data scanned at query time.

Answer

Increase the query timeout in Athena.

Answer

Increase the number of DPUs in the Glue job.

Question 2

A data engineer needs to ensure that an Amazon Redshift cluster only accepts encrypted connections. Which parameter should be modified?

Accepted Answer

require_SSL. Setting the `require_SSL` parameter to `true` forces all connections to the Amazon Redshift cluster to use SSL/TLS encryption, ensuring that data in transit is encrypted. This parameter is modified in the cluster's parameter group and applies to both JDBC and ODBC connections, as well as the Redshift Query Editor.

Answer

enable_user_activity_logging

Answer

max_concurrency_scaling_clusters

Answer

wlm_json_configuration

Question 3

A company uses AWS Lake Formation to manage data lakes on Amazon S3. The data engineer needs to grant a data analyst access to query specific columns in a table using Amazon Athena, but deny access to columns containing personally identifiable information (PII). Which Lake Formation feature should be used?

Accepted Answer

Column-level permissions in Lake Formation.. Lake Formation column-level permissions allow a data engineer to grant SELECT on specific columns of a table while excluding others, which is exactly what is needed to hide PII columns from the analyst. When the analyst queries via Athena, Lake Formation enforces the column filter at query time, returning only the permitted columns. This is the native, fine-grained access control mechanism for column-level restrictions in Lake Formation.

Answer

Row-level security filters.

Answer

Tag-based access control with Lake Formation tags.

Answer

Cell-level security with AWS Glue.

Question 4

A company uses Amazon Kinesis Data Firehose to deliver data to Amazon S3. The data is transformed using an AWS Lambda function. Recently, the transformation errors have increased due to Lambda timeouts. The data engineer needs to diagnose and resolve the issue without losing data. What should the engineer do?

Accepted Answer

Increase the Lambda function timeout and ensure that failed records are sent to a backup S3 bucket. Increasing the Lambda function timeout directly addresses the root cause of transformation errors (timeouts), and configuring a backup S3 bucket for failed records ensures no data loss. Kinesis Data Firehose can be configured to send failed records to a separate S3 bucket as a dead-letter queue, which preserves the data for later reprocessing while the primary transformation pipeline is fixed.

Answer

Enable Amazon CloudWatch Logs for the Lambda function to capture errors and store failed records in CloudWatch

Answer

Configure the Lambda function to write failed records to an Amazon SQS queue for later reprocessing

Answer

Modify the Lambda function to store failed records in Amazon S3 before processing

Question 5

A company needs to transform JSON data from an Amazon S3 bucket into Parquet format and load it into an Amazon Redshift cluster. The transformation includes joining with a reference table stored in Amazon RDS. Which AWS service is BEST suited for this task?

Accepted Answer

AWS Glue ETL job. (AWS Glue ETL job) is the best choice because it natively integrates with S3, RDS, and Redshift. Glue can read JSON from S3, connect to RDS via JDBC to join with the reference table, transform the data to Parquet using its built-in converter, and write directly to Redshift. Option A (AWS Data Pipeline) is older and less integrated for this purpose. Option C (Amazon Athena) can query S3 and convert to Parquet but cannot natively join with RDS without additional services. Option D (Amazon EMR with Spark) is possible but requires more setup and maintenance.

Answer

AWS Data Pipeline

Answer

Amazon Athena

Answer

Amazon EMR with Spark

Question 6

A company uses Amazon Kinesis Data Firehose to ingest application logs into an Amazon S3 bucket. The logs are in JSON format. The data engineering team wants to convert the logs from JSON to Parquet format before landing in S3. What is the most cost-effective way to achieve this?

Accepted Answer

Configure the Firehose delivery stream to convert the data to Parquet using a schema from AWS Glue.. Amazon Kinesis Data Firehose supports record format conversion natively, allowing a delivery stream to convert incoming JSON records to Parquet or ORC using a schema stored in the AWS Glue Data Catalog. This is the most cost-effective approach because the conversion happens within Firehose without provisioning or paying for separate compute such as Lambda or Glue ETL jobs.

Answer

Use Amazon Athena to query the JSON data and write results in Parquet format.

Answer

Use an AWS Lambda function to transform each record to Parquet and send to Firehose.

Answer

Use an AWS Glue ETL job to run on a schedule and convert JSON to Parquet in S3.

Question 7

A financial services company stores transaction data in Amazon RDS for PostgreSQL. The company requires that all changes to the database be logged for audit purposes, including before and after images of updated rows. Which feature should the data engineer enable?

Accepted Answer

Set up logical replication using pglogical or native publication/subscription. Logical replication, using either pglogical or native PostgreSQL publication/subscription, captures row-level changes (INSERT, UPDATE, DELETE) and can include both the old and new values of updated rows. This meets the audit requirement for before-and-after images, as logical replication decodes the write-ahead log (WAL) to produce a change stream that includes full row snapshots.

Answer

Enable automated backups and export logs to Amazon S3

Answer

Enable Enhanced Monitoring and publish logs to CloudWatch Logs

Answer

Enable Multi-AZ deployment and read replicas

Question 8

A data engineer is building a data pipeline that ingests data from Amazon S3 into Amazon Redshift. The data is in CSV format and includes a timestamp column. The pipeline should load only new data incrementally. Which approach is most efficient?

Accepted Answer

Use the COPY command with a manifest file that lists only the new S3 objects. Using the COPY command with a manifest file allows you to explicitly list only the new S3 objects to be loaded, enabling incremental loading without scanning or loading the entire bucket. This approach is efficient as it avoids the overhead of deduplication or full-bucket scans, and it leverages Redshift's native high-speed parallel ingestion from S3.

Answer

Use the COPY command to load the entire bucket and rely on Redshift to deduplicate

Answer

Use Amazon Redshift Spectrum to query the S3 data directly without loading

Answer

Use INSERT statements within a loop to load each new file

Question 9

A company wants to securely store database credentials used by a Lambda function. Which AWS service should be used to store and rotate the credentials automatically?

Accepted Answer

AWS Secrets Manager. AWS Secrets Manager is purpose-built for storing, retrieving, and automatically rotating secrets such as database credentials, API keys, and OAuth tokens. It natively integrates with Amazon RDS, Redshift, and DocumentDB to rotate credentials on a schedule using Lambda rotation functions. Lambda functions can retrieve secrets at runtime via the Secrets Manager API or the AWS Parameters and Secrets Lambda Extension, avoiding hardcoded credentials.

Answer

AWS CloudHSM

Answer

AWS Key Management Service (KMS)

Answer

AWS Systems Manager Parameter Store

Question 10

A company is migrating a large Oracle data warehouse to Amazon Redshift. Which THREE considerations are important for optimizing the Redshift cluster?

Accepted Answer

Defining appropriate sort keys for the tables.. Sort keys in Amazon Redshift determine the physical order of data on disk, which directly impacts the efficiency of range-restricted queries and compression. By defining appropriate sort keys (compound or interleaved), the query optimizer can use zone maps to skip large blocks of data that don't match the filter criteria, significantly reducing the number of blocks scanned and improving query performance.

Answer

Purchasing reserved instances for the cluster.

Answer

Using columnar storage format.

Question 11

A data engineer is reviewing an IAM policy that controls access to an S3 bucket. The policy is attached to a user group. The policy includes a condition that explicitly requires server-side encryption with SSE-S3 for all GetObject requests. The engineer notices that users are unable to download objects from the bucket. What is the likely cause?

Accepted Answer

The objects are encrypted using SSE-KMS, not SSE-S3.. The IAM policy includes a condition that allows downloads only if the object is encrypted with SSE-S3. However, the objects in the bucket are encrypted using SSE-KMS, which does not satisfy the condition. As a result, the s3:GetObject request is denied. This is a common scenario where a specific encryption condition in the policy blocks access when the actual encryption method differs. The other options are less likely: attaching the policy to a user group is valid and does not cause download issues; an incorrect bucket ARN would affect all operations, not just downloads; and missing s3:GetObject would be a straightforward policy error that would be easily identified.

Answer

The policy is attached to a user group instead of an IAM role.

Answer

The policy does not specify the correct bucket ARN.

Answer

The policy does not allow the s3:GetObject action.

Question 12

A company wants to enable automatic encryption for all new objects written to an S3 bucket. The bucket has existing objects that are unencrypted. Which solution meets these requirements with the least operational overhead?

Accepted Answer

Enable default encryption on the bucket using SSE-S3. Enabling default encryption on the bucket with SSE-S3 causes all new objects written to the bucket to be encrypted automatically without any client-side changes or bucket policy enforcement. It is a single bucket-level setting, so it has the least operational overhead. Existing unencrypted objects remain unencrypted until rewritten, but the requirement only specifies automatic encryption for new objects.

Answer

Configure a lifecycle policy to transition objects to a new bucket with encryption

Answer

Use S3 server-side encryption with S3 managed keys (SSE-S3) and apply a bucket policy that denies writes without encryption

Answer

Use S3 Batch Operations to copy existing objects with SSE-S3

Question 13

A company uses AWS Glue to run ETL jobs on a schedule. Recently, a job failed with the error: 'AnalysisException: cannot resolve '`column_name`' given input columns: ...'. The job reads from an Amazon S3 source that has a schema defined in the AWS Glue Data Catalog. What is the MOST likely cause?

Accepted Answer

The schema of the source data has changed and is not reflected in the Data Catalog.. The AnalysisException 'cannot resolve column_name given input columns' means the Spark/Glue job's code references a column that does not exist in the DataFrame schema derived from the Data Catalog. The most likely cause is that the source data's schema has evolved (new/renamed/dropped columns) but the Glue Data Catalog table definition was not updated, so the job's schema and the actual data are out of sync.

Answer

The source data file is corrupted and cannot be parsed.

Answer

The IAM role associated with the Glue job does not have permissions to read the S3 bucket.

Answer

The data type of the column in the source does not match the Data Catalog definition.

Question 14

A company is using Amazon S3 to store large amounts of archival data. The data is accessed infrequently but must be immediately retrievable when needed. Which storage class is the most cost-effective choice?

Accepted Answer

S3 Standard-IA. S3 Standard-IA (Infrequent Access) is the most cost-effective choice because it offers lower storage costs than S3 Standard while still providing millisecond first-byte latency for immediate retrieval. The data is accessed infrequently but requires instant availability, which matches the IA use case exactly.

Answer

S3 Standard

Answer

S3 Glacier Deep Archive

Answer

S3 Intelligent-Tiering

Question 15

A data engineer uses Amazon EMR to run a Spark job that reads from S3 and writes to HDFS on the cluster. The job fails with an 'OutOfMemoryError: Java heap space' error in the executors. Which parameter adjustment should be made to resolve this?

Accepted Answer

Increase spark.executor.memory. The OutOfMemoryError: Java heap space in Spark executors indicates that the executor JVM heap is insufficient to hold the data being processed. Increasing spark.executor.memory allocates more heap space to each executor, allowing it to handle larger partitions or aggregations without running out of memory. This directly addresses the root cause of the error.

Answer

Increase spark.default.parallelism

Answer

Increase spark.sql.shuffle.partitions

Answer

Increase spark.driver.memory