MLS-C01 Data Engineering Practice Test 8 — 15 Questions

Question 1

A company uses AWS Lake Formation to manage permissions on a data lake stored in Amazon S3. A data analyst tries to query a table using Amazon Athena but receives an 'Access Denied' error. The analyst has SELECT permission on the table in Lake Formation. What is the most likely cause?

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

Accepted Answer

The S3 bucket is not registered with Lake Formation. When Lake Formation manages permissions on a data lake, it requires that the underlying S3 bucket be registered with Lake Formation. If the bucket is not registered, Lake Formation cannot enforce its fine-grained access controls, and Athena will fail with an 'Access Denied' error even if the analyst has SELECT permission on the table in Lake Formation. Registering the bucket allows Lake Formation to integrate with S3 and apply its permission model.

Answer

The S3 bucket is encrypted with a KMS key that the analyst does not have access to

Answer

The table does not have any partitions defined

Answer

The IAM role used by Athena does not have lakeformation:GetDataAccess permission

Question 2

A company uses Amazon EMR to run Spark jobs on a large dataset stored in Amazon S3. The jobs are failing with 'OutOfMemoryError' in the executors. The data is not skewed. Which configuration change will most likely resolve the issue?

Accepted Answer

Increase the spark.executor.memoryOverhead setting. When Spark executors run out of memory during shuffle operations, the `spark.executor.memoryOverhead` setting is often the culprit. This parameter allocates off-heap memory for JVM overhead, internal metadata, and shuffle buffers. Increasing it provides more room for these operations without reducing the executor heap, directly addressing OutOfMemoryError in non-skewed data scenarios.

Answer

Enable Kryo serialization

Answer

Decrease the number of shuffle partitions

Answer

Increase the number of executor cores

Question 3

A data engineer is designing a data ingestion pipeline that will receive up to 5 GB of data per hour from thousands of IoT devices. The data must be stored in Amazon S3 and analyzed in near real-time. Which TWO services should be used together to meet these requirements? (Choose TWO.)

Accepted Answer

Amazon Kinesis Data Analytics. Amazon Kinesis Data Firehose is the correct service because it can reliably ingest streaming data from thousands of IoT devices at up to 5 GB per hour, automatically buffer, compress, and deliver the data to Amazon S3 with near-real-time latency (typically 60 seconds). Amazon Kinesis Data Analytics is correct because it enables real-time SQL-based analytics on the streaming data before it is stored in S3, allowing the data engineer to derive insights as data arrives without needing to query the S3 bucket after storage.

Answer

AWS Lambda

Answer

Amazon Athena

Answer

Amazon Simple Queue Service (Amazon SQS)

Question 4

A company needs to transfer 10 TB of data from an on-premises data center to Amazon S3. The network bandwidth is limited to 100 Mbps, and the transfer must complete within 5 days. Which TWO options are viable? (Choose TWO.)

Accepted Answer

Use AWS Snowball Edge device to ship the data. AWS Snowball Edge is a physical data transport solution designed for large-scale data transfers over slow or unreliable networks. With 10 TB of data and a 100 Mbps link, the theoretical transfer time is over 9 days, exceeding the 5-day window. Snowball Edge bypasses the network bottleneck entirely by shipping the data via courier. Option E is also correct because AWS Direct Connect provides a dedicated network connection that can offer higher and more consistent bandwidth than the existing internet connection. If provisioned with sufficient capacity (e.g., 1 Gbps), the transfer can complete within the 5-day window. Options A and B are not viable because they cannot overcome the physical bandwidth limitation of 100 Mbps. Option D is not viable because DataSync still relies on the existing internet connection, which is insufficient.

Answer

Use S3 Transfer Acceleration to speed up the transfer

Answer

Use S3 Multipart Upload to upload files in parallel

Answer

Use AWS DataSync over the existing internet connection

Question 5

A company uses Amazon Redshift for data warehousing. The data engineering team notices that query performance has degraded over time. Which THREE actions should the team take to improve performance? (Choose THREE.)

Accepted Answer

Define appropriate sort keys on large tables. Sort keys in Amazon Redshift define the order in which data is stored on disk within each node. By defining appropriate sort keys on large tables, the query engine can use zone maps to skip entire blocks of data that do not match the query's filter predicates, dramatically reducing the amount of data scanned and improving query performance.

Answer

Increase the number of nodes in the Redshift cluster

Answer

Delete old data that is no longer needed

Question 6

A data engineer is building a data pipeline that ingests streaming data from IoT devices. The data must be processed in near real-time and stored in Amazon S3 for further analysis. Which AWS service should be used to capture and process the streaming data before storing it in S3?

Accepted Answer

Use Amazon Kinesis Data Streams to capture the data and Amazon Kinesis Data Firehose to deliver it to S3.. Amazon Kinesis Data Streams is designed for real-time data ingestion and can capture streaming data from IoT devices with low latency. Amazon Kinesis Data Firehose then reliably loads that streaming data into Amazon S3, handling buffering, compression, and partitioning automatically. This combination provides the near real-time processing and durable storage required for the pipeline.

Answer

Use Amazon S3 with S3 Event Notifications to trigger AWS Lambda for processing.

Answer

Use AWS Glue to perform ETL on the streaming data.

Answer

Use Amazon Simple Queue Service (SQS) to buffer the data and then process it with AWS Lambda.

Question 7

A machine learning team needs to preprocess large volumes of clickstream data stored in Amazon S3 before training a model. The preprocessing includes data cleaning, feature engineering, and normalization. The team wants to use a serverless solution that minimizes operational overhead. Which combination of services should the team use?

Accepted Answer

AWS Glue ETL jobs reading from and writing to S3.. AWS Glue ETL jobs are a serverless solution that automatically provisions and scales the underlying compute resources, making them ideal for preprocessing large volumes of clickstream data stored in S3. Glue can read directly from S3, perform data cleaning, feature engineering, and normalization using PySpark or Scala, and write the transformed data back to S3, all without managing any infrastructure. This minimizes operational overhead while handling the required preprocessing tasks at scale.

Answer

Amazon SageMaker Notebooks with custom Python scripts.

Answer

Amazon EMR with Spark clusters.

Answer

Amazon Athena with SQL queries.

Question 8

A company is using Amazon Kinesis Data Analytics for Apache Flink to process real-time data. The data source is a Kinesis data stream, and the output is written to an S3 bucket. Recently, the processing latency has increased significantly. The team suspects that the Flink application is encountering backpressure. Which metric should the team monitor to confirm backpressure?

Accepted Answer

busyTimeMsPerSecond. The correct metric to confirm backpressure in a Flink application is `busyTimeMsPerSecond`. This metric measures the percentage of time a task is actively processing data versus waiting for input. A high `busyTimeMsPerSecond` value (close to 1000ms) indicates that the task is fully utilized and cannot keep up with the incoming data rate, which is the direct symptom of backpressure. Other metrics like `currentLowWatermark` relate to event time progress, not backpressure.

Answer

currentLowWatermark

Answer

numberOfFailedCheckpoints

Answer

numRecordsInPerSecond

Question 9

A data scientist wants to query a dataset stored in Amazon S3 using standard SQL without provisioning any servers. The dataset is in CSV format and is updated daily. Which AWS service should be used?

Accepted Answer

Amazon Athena. Amazon Athena is a serverless interactive query service that allows you to query data directly from Amazon S3 using standard SQL without provisioning any servers. It is ideal for querying CSV datasets that are updated daily because it supports schema-on-read, meaning you can define the table schema at query time without loading or transforming the data beforehand.

Answer

Amazon Redshift

Answer

Amazon RDS

Answer

Amazon DynamoDB

Question 10

A company is building a data pipeline to process sensitive customer data. The pipeline uses AWS Glue for ETL and stores results in Amazon S3. The security team requires that all data be encrypted at rest in S3 using customer-managed AWS KMS keys. Additionally, the Glue job must be able to write encrypted data to S3. What should the data engineer do to meet these requirements?

Accepted Answer

Attach a policy to the Glue job's IAM role that includes kms:GenerateDataKey and kms:Decrypt actions for the KMS key.. AWS Glue jobs use an IAM role to interact with AWS services. To write encrypted data to S3 using a customer-managed AWS KMS key, the IAM role must have permissions for `kms:GenerateDataKey` (to request a data key for encryption) and `kms:Decrypt` (to decrypt the data key when reading or writing). This allows the Glue job to encrypt objects at rest in S3 with the specified KMS key, meeting the security team's requirement.

Answer

Use S3 server-side encryption with customer-provided keys (SSE-C).

Answer

Use S3 server-side encryption with SSE-S3, which is enabled by default.

Answer

Configure an S3 bucket policy to enforce encryption and attach it to the Glue job's IAM role.

Question 11

A large e-commerce company is using Amazon DynamoDB as the source for real-time analytics. The data is streamed to Amazon Kinesis Data Streams using DynamoDB Streams and then processed by an AWS Lambda function. The Lambda function writes the data to an Amazon Elasticsearch Service cluster for search and visualization. Recently, the Lambda function has been failing with throttling errors from the Elasticsearch cluster. What is the MOST effective way to handle this?

Accepted Answer

Configure a Dead Letter Queue (DLQ) on the Lambda function to capture failed records and implement retry logic.. Using a Dead Letter Queue (DLQ) allows the Lambda function to capture records that fail due to Elasticsearch throttling, so they can be retried later without blocking the function's processing of other records. This prevents data loss and handles backpressure effectively. Option A is incorrect because increasing Lambda concurrency would increase the rate of writes to the Elasticsearch cluster, worsening the throttling. Option B is incorrect because increasing Kinesis shards would increase the throughput of data arriving at the Lambda function, again exacerbating the throttling. Option C is incorrect because decreasing the Kinesis retention period does not reduce the data volume; it only changes how long data is stored in the stream, and it would not solve the throttling issue.

Answer

Increase the Lambda function's reserved concurrency to handle more invocations.

Answer

Increase the number of shards in the Kinesis data stream.

Answer

Decrease the Kinesis stream's retention period to reduce the data volume.

Question 12

A company is using Amazon S3 as a data lake. The data engineering team needs to catalog the schema of the data and make it available for querying with Amazon Athena. Which AWS Glue component should be used?

Accepted Answer

AWS Glue Crawlers. AWS Glue Crawlers automatically scan data in Amazon S3, infer the schema, and populate the AWS Glue Data Catalog with metadata tables. This makes the data immediately available for querying with Amazon Athena without manual schema definition.

Answer

AWS Glue Studio

Answer

AWS Glue ETL jobs

Answer

AWS Glue DataBrew

Question 13

A data engineer needs to transfer 50 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The on-premises network has a 100 Mbps connection to AWS. The transfer must be completed within one week. Which approach should the engineer use?

Accepted Answer

Use AWS Snowball Edge device to physically transfer the data.. The on-premises network has a 100 Mbps connection, which yields a theoretical maximum transfer of about 1.08 TB per day (100 Mbps * 86400 seconds / 8 bits per byte / 1024^4 bytes per TB). To transfer 50 TB within 7 days, the required throughput is approximately 7.14 TB per day, far exceeding the available bandwidth. AWS Snowball Edge provides a physical shipping method that bypasses network constraints entirely, making it the only viable option for this volume and timeline.

Answer

Use Amazon S3 Transfer Acceleration.

Answer

Use AWS DataSync to transfer the data over the network.

Answer

Use multiple concurrent AWS CLI copy commands over VPN.

Question 14

A company is using Amazon Redshift for data warehousing. The data engineering team notices that queries are slow and the system is frequently writing to disk due to insufficient memory. Which type of workload management (WLM) configuration change would help reduce disk writes?

Accepted Answer

Increase the memory percentage allocated to the WLM queue.. When queries spill to disk in Amazon Redshift, it indicates that the memory allocated to the WLM queue is insufficient for the workload. Increasing the memory percentage for the queue allows more queries to be processed in memory, reducing the need to write intermediate results to disk and improving query performance.

Answer

Increase the number of query concurrency slots.

Answer

Enable query monitoring rules to abort queries that spill to disk.

Answer

Enable short query acceleration (SQA).

Question 15

Which TWO AWS services can be used to move data from an on-premises database to Amazon S3 on a recurring schedule without writing custom code? (Choose 2.)

Accepted Answer

AWS Glue. AWS Glue is correct because it provides a fully managed ETL service that can run crawlers and jobs on a recurring schedule to extract data from on-premises databases (via JDBC connections) and write it to Amazon S3 without requiring any custom code. AWS DMS is correct because it supports continuous replication or scheduled tasks to migrate data from on-premises databases to S3 as a target, using built-in transformation capabilities and no custom scripting.

Answer

AWS Snowball Edge

Answer

Amazon Athena

Answer

Amazon Kinesis Data Firehose