MLS-C01 Data Engineering Practice Test 30 — 15 Questions

Question 1

A data engineer is building an AWS Glue ETL job that reads from an Amazon DynamoDB table using a Glue DynamicFrame. The table has a large number of items and the job must avoid consuming all read capacity, which would impact the production application. The engineer wants to control the read throughput. What should the engineer do?

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

Accepted Answer

Configure the Glue job's DynamoDB connection with a read throughput percentage or use the dynamodb.read.throughput.percent connection option.. AWS Glue provides a connection option for DynamoDB sources that limits the percentage of read capacity units used. By setting dynamodb.read.throughput.percent, the engineer can throttle the scan so it does not exhaust the table's provisioned capacity and affect the production application. This is the intended control for read throughput in Glue ETL jobs.

Answer

Enable DynamoDB Streams on the table and have the Glue job read from the stream instead of the table.

Answer

Increase the number of Glue DPUs to parallelize the read and finish faster.

Answer

Set the DynamoDB table's read capacity mode to on-demand before running the Glue job.

Question 2

A machine learning engineer needs to store training data in Amazon S3 for a SageMaker training job. The data is currently in Amazon Redshift and is about 2 TB. The engineer wants to minimize the time to transfer the data and ensure the training job can access it efficiently. Which method should the engineer use to move the data from Amazon Redshift to Amazon S3?

Accepted Answer

Use the UNLOAD command in Amazon Redshift to export the data directly to Amazon S3 in Parquet format.. The UNLOAD command is the native and most efficient way to export large datasets from Amazon Redshift to Amazon S3. It parallelizes the export across slices, supports columnar formats like Parquet, and can compress data, reducing transfer time and storage costs. Other methods like DMS or Glue add overhead and are not optimized for this specific bulk export scenario. Therefore, UNLOAD is the best choice.

Answer

Use the Amazon Redshift COPY command to copy the data from Redshift to Amazon S3.

Answer

Use the AWS Glue crawler to catalog the Redshift data and then use AWS Glue ETL to write to Amazon S3.

Answer

Use AWS Database Migration Service (AWS DMS) to replicate the data from Amazon Redshift to Amazon S3.

Question 3

A machine learning platform team must give a SageMaker training job access to training data in Amazon S3 and to a secret stored in AWS Secrets Manager that holds database credentials. Security policy forbids long-term access keys on the training instances and requires that the permissions be scoped to only the required resources. The team will pass an execution role to the training job. Which two actions should the team take to meet these requirements? (Choose two.)

Accepted Answer

Attach an IAM policy to the SageMaker execution role that grants s3:GetObject on the specific training data prefix and secretsmanager:GetSecretValue on the specific secret ARN.. SageMaker training jobs obtain credentials by assuming an execution role whose trust policy permits the SageMaker service principal. Passing that role ARN at job creation lets the container receive temporary, automatically rotated credentials. Scoping the attached policy to the exact S3 prefix and the specific secret ARN satisfies least privilege, while no static keys are ever placed on the instances.

Answer

Enable network isolation on the training job and retrieve the secret from the container using the AWS SDK default credential chain.

Answer

Grant the training job access by attaching the AdministratorAccess managed policy to the execution role.

Answer

Store an IAM user's access key and secret key as environment variables in the training job definition.

Question 4

A retail company stores 40 TB of historical transaction logs in Amazon S3 in CSV format. A machine learning team needs to run ad hoc SQL queries against this data only a few times per month, and the dataset must remain immediately queryable with no ETL or data movement. Leadership has flagged that storage and query costs must be minimized. Which solution meets these requirements with the LEAST operational overhead?

Accepted Answer

Use Amazon Athena with the CSV data in S3 and define the table in the AWS Glue Data Catalog.. Querying the CSV files in place with Amazon Athena and registering them in the AWS Glue Data Catalog satisfies every constraint: no data movement, immediate queryability, serverless operation, and pay-per-query pricing that suits infrequent access. Provisioned engines such as Redshift and EMR add idle compute cost and management burden, while DynamoDB is the wrong data model for analytical SQL.

Answer

Create an Amazon Redshift cluster, run COPY commands to load the CSV data, and query it with standard SQL.

Answer

Load the CSV files into Amazon DynamoDB and query them with PartiQL from the AWS Management Console.

Answer

Launch an Amazon EMR cluster with Apache Hive, point the external table at the S3 prefix, and query with HiveQL.

Question 5

A company ingests application logs into Amazon Kinesis Data Firehose and delivers them to Amazon S3. Compliance requires that each delivered object be encrypted with a customer-managed AWS KMS key, and that the Firehose service be able to write to the bucket without granting broad permissions. Which two actions must the data engineer take? (Choose two.)

Accepted Answer

Configure the Firehose delivery stream with a KMS key for SSE-KMS server-side encryption of the delivered S3 objects.. Delivering KMS-encrypted objects requires selecting a customer-managed key on the Firehose stream so SSE-KMS is applied at write time, and because Firehose assumes a delivery role, the destination bucket policy must authorize that role to put objects into the target prefix. Together these two controls provide the encryption and the least-privilege write access the compliance rule demands.

Answer

Create a VPC endpoint for Amazon S3 and route all Firehose traffic through it.

Answer

Enable S3 Object Lock in governance mode on the destination bucket.

Answer

Set the Firehose destination to Amazon OpenSearch Service and enable fine-grained access control.

Question 6

A data engineer must give a SageMaker training job read access to a specific S3 bucket containing training data, while ensuring the job cannot access any other bucket. The training job runs with an IAM role. What is the MOST secure way to grant this access?

Accepted Answer

Attach an IAM policy to the training job's execution role that allows s3:GetObject and s3:ListBucket on the specific bucket and its objects.. Scoping an IAM policy to the specific bucket and attaching it to the training job's execution role grants only the required read permissions. SageMaker assumes the role to obtain temporary credentials, so no long-lived keys are needed, and the policy prevents access to other buckets. Broad managed policies, embedded keys, or public buckets all violate the least-privilege and security requirements.

Answer

Attach the AmazonS3ReadOnlyAccess managed policy to the training job's execution role so it can read any bucket in the account.

Answer

Make the S3 bucket public and restrict access using an S3 bucket policy that allows only the training job's IP address range.

Answer

Store long-term AWS access keys in the training container's environment variables and use them to access the bucket.

Question 7

A machine learning engineer needs to create a training dataset from an Amazon Redshift cluster and store it in Amazon S3 for use with Amazon SageMaker. The dataset is the result of a complex join and aggregation query. The engineer wants to minimize the amount of data transferred out of Redshift and avoid running the query multiple times. Which approach should be used?

Accepted Answer

Use the Redshift UNLOAD command to export the query results directly to Amazon S3 in Parquet format.. The UNLOAD command is designed to export query results from Amazon Redshift to Amazon S3 efficiently. It parallelizes the export across compute nodes and can output Parquet, which is columnar and ideal for SageMaker training. It avoids re-running the query for extraction and minimizes data movement, satisfying the engineer's requirements.

Answer

Run the query in Amazon Redshift, save the results to a new table, and then use the CREATE TABLE AS SELECT with an S3 location.

Answer

Create an Amazon Redshift Spectrum external table pointing to the S3 bucket and query it from SageMaker.

Answer

Use an AWS Glue ETL job with a Redshift connection to read the table and write to S3.

Question 8

A data engineering team loads JSON event files into an AWS Glue Data Catalog table backed by Amazon S3. New files arrive continuously and each file contains records with a schema that occasionally gains new optional fields. A nightly AWS Glue ETL job reads the table and writes Parquet output. The job sometimes fails with an error that the schema does not match the table definition. The team wants the job to tolerate the new optional fields without rewriting historical data. What should the team do?

Accepted Answer

Set the job's catalog table update behavior to update the Data Catalog schema from the source, and enable schema evolution on the DynamicFrame reader.. AWS Glue readers can merge schemas observed across input files when schema evolution is enabled, and the job can push the reconciled schema back to the Data Catalog. That combination lets newly added optional fields flow through the job while older Parquet output stays valid, because added columns read as null for historical rows. No source data needs to be reprocessed.

Answer

Run an AWS Glue crawler with the 'Ignore the change and don't update the Data Catalog' option and continue the nightly job unchanged.

Answer

Increase the number of Glue DPUs allocated to the nightly job and enable job bookmarks.

Answer

Convert the source JSON files to Parquet first, then register the Parquet output in the Data Catalog for the nightly job to read.

Question 9

A machine learning team stores 40 TB of Parquet training data in Amazon S3 and wants to query and transform it with Amazon Athena before training. Queries currently scan the entire dataset and cost is rising. The data is partitioned by year, month, and day in the S3 prefix structure, but query performance has not improved. A data engineer confirms the table was created with AWS Glue Data Catalog, but the partition metadata is missing. What should the engineer do to enable partition pruning and reduce scanned data?

Accepted Answer

Run MSCK REPAIR TABLE on the Athena table to load the partition metadata into the AWS Glue Data Catalog.. Registering the existing Hive-style partitions in the Data Catalog is the key. MSCK REPAIR TABLE detects the year/month/day prefix structure and adds partition entries, allowing Athena to prune non-matching partitions and scan only relevant data. Changing formats or enabling transfer features does not address the missing metadata that prevents pruning.

Answer

Convert the Parquet files to CSV and re-crawl them with an AWS Glue crawler to force partition discovery.

Answer

Enable Amazon S3 Transfer Acceleration on the bucket to speed up Athena reads.

Answer

Add a WHERE clause filtering on the S3 object key prefix instead of on partition columns.

Question 10

A media company ingests live video telemetry into Amazon Kinesis Data Streams. A consumer application reads the stream and must process each record exactly once before writing enriched results to Amazon S3. The team observes duplicate records in S3 after consumer restarts. Which two actions should the engineer take to eliminate duplicates? (Choose two.)

Accepted Answer

Make the S3 write idempotent by including a deterministic record identifier and using conditional writes or deduplication before storing.. Eliminating duplicates requires both reliable progress tracking and safe re-writes. Checkpointing with the Kinesis Client Library lets a restarted consumer resume from the last processed sequence number, while idempotent, deduplicated S3 writes absorb any records reprocessed during the window between processing and checkpointing. Together they deliver effectively-once results; scaling shards, enhanced fan-out, or encryption do not affect duplicate semantics.

Answer

Enable server-side encryption with AWS KMS on the stream and the S3 bucket.

Answer

Switch the consumer to use enhanced fan-out with a dedicated throughput of 2 MB per second per shard.

Answer

Increase the number of shards in the stream so each consumer processes fewer records.

Question 11

A data scientist needs to store a large dataset for a machine learning project in Amazon S3. The dataset will be accessed frequently during the training phase, which lasts for two weeks. After training, the data will be archived for compliance and accessed only once a year. The data scientist wants to minimize storage costs. Which S3 storage class should be used for the data during the training phase and after training?

Accepted Answer

Use S3 Standard during training, then transition to S3 Glacier Deep Archive after training.. During the two-week training phase, frequent access requires a storage class with low latency and high throughput, such as S3 Standard. After training, the data is archived and accessed only annually, making S3 Glacier Deep Archive the most cost-effective choice for long-term retention. This lifecycle minimizes storage costs while meeting performance and compliance needs.

Answer

Use S3 Intelligent-Tiering during training, then transition to S3 Glacier Flexible Retrieval after training.

Answer

Use S3 Standard-Infrequent Access (S3 Standard-IA) during training, then transition to S3 One Zone-IA after training.

Answer

Use S3 Glacier Instant Retrieval during training, then transition to S3 Glacier Deep Archive after training.

Question 12

A company ingests streaming sensor data into Amazon Kinesis Data Streams and must store it in Amazon S3 in Parquet for analytics. The data volume varies significantly, with long idle periods overnight and bursts during the day. The solution must minimize cost during idle periods and scale automatically during bursts, with minimal operational management. Which solution meets these requirements?

Accepted Answer

Use Amazon Kinesis Data Firehose to deliver the stream to S3 with a Lambda function for Parquet conversion, and enable dynamic partitioning.. Kinesis Data Firehose is a fully managed delivery service that scales automatically with incoming throughput and bills by data volume, so idle overnight periods incur minimal cost. Lambda provides the Parquet conversion, and dynamic partitioning organizes output into S3 prefixes for efficient querying. This meets the variable-load, cost, and low-management requirements without provisioning capacity.

Answer

Provision a fixed number of Kinesis Data Streams shards sized for peak load and run an AWS Lambda consumer that converts records to Parquet and writes to S3.

Answer

Use AWS Glue streaming ETL jobs with a fixed number of workers to read from Kinesis and write Parquet to S3.

Answer

Run a continuously provisioned Amazon EMR cluster with Spark Streaming that reads from Kinesis and writes Parquet to S3.

Question 13

A data engineer must catalog a growing collection of JSON files in Amazon S3 so that analysts can query them with Amazon Athena. The files arrive continuously, and new partitions appear daily. The engineer wants the table metadata to stay current automatically with minimal operational effort. Which solution meets these requirements?

Accepted Answer

Run an AWS Glue crawler on a schedule against the S3 prefix and let it update the AWS Glue Data Catalog table.. A scheduled AWS Glue crawler is the standard low-code way to keep S3-backed Athena tables current: it samples the data to infer schema, discovers partition prefixes, and updates the Data Catalog that Athena consults. Manual DDL, custom DynamoDB metadata, or relocating data to Redshift all fail to deliver automatic, low-effort catalog maintenance.

Answer

Load the JSON files into Amazon Redshift using COPY and query Redshift instead of Athena.

Answer

Create an AWS Lambda function that scans the prefix and writes partition metadata to a DynamoDB table for Athena to read.

Answer

Define the table manually in Athena with CREATE TABLE and rely on Athena's schema-on-read to detect new partitions.

Question 14

A company runs an Amazon Kinesis Data Firehose delivery stream that buffers incoming records for 300 seconds and 5 MB before delivering them to Amazon S3. Downstream ML feature pipelines read the S3 objects every 15 minutes. Analysts report that the freshest features are sometimes almost 20 minutes old. The team wants to reduce end-to-end latency without changing the downstream consumers or switching to a different ingestion service. What should the data engineer do?

Accepted Answer

Change the Firehose buffer interval to 60 seconds and buffer size to 1 MB, and configure S3 event notifications to invoke the downstream processing as objects arrive.. Two independent delays combine here: the Firehose buffer holds records up to 300 seconds before writing to Amazon S3, and the consumers poll on a fixed 15-minute schedule. Reducing the buffer interval and size shortens the first delay, while S3 event notifications eliminate the second by pushing work as soon as objects land. Together they cut end-to-end feature latency substantially.

Answer

Change the Firehose delivery stream's buffer interval to 60 seconds and buffer size to 1 MB, and enable dynamic partitioning on the destination.

Answer

Enable Amazon S3 Transfer Acceleration on the destination bucket and increase the Firehose delivery stream's shard count.

Answer

Increase the Firehose buffer size to 128 MB and keep the 300-second interval so that each delivery contains a complete batch.

Question 15

A data engineer needs to catalog metadata for a large collection of Parquet files stored in Amazon S3 so that multiple analytics services can discover and query the tables consistently. The engineer wants automatic schema discovery and a shared, Hive-compatible metastore without running any servers. Which service should be used?

Accepted Answer

AWS Glue Data Catalog. The AWS Glue Data Catalog is the central, serverless, Hive-compatible metadata repository for the AWS analytics ecosystem. Crawlers populate it automatically, and services including Athena, Redshift Spectrum, EMR, and Lake Formation reference the same table definitions. Workgroups, Lake Formation permissions, and S3 Inventory serve different purposes and cannot replace the shared catalog for schema discovery and cross-service querying.

Answer

Amazon S3 Inventory reports

Answer

AWS Lake Formation permissions

Answer

Amazon Athena workgroups