MLS-C01 Practice Test 46 — 15 Questions

Question 1

A data engineer is building a streaming pipeline using Amazon Kinesis Data Streams and AWS Lambda. The Lambda function processes records and writes results to Amazon S3. The engineer notices that the Lambda function is experiencing throttling and some records are being dropped. Which TWO actions should the engineer take to improve the reliability of the pipeline?

Accepted Answer

Increase the number of shards in the Kinesis data stream.. Increasing the number of shards in the Kinesis stream increases throughput and reduces throttling. Setting the Lambda function's reserved concurrency ensures it has sufficient capacity to handle the incoming records.

Answer

Add a Dead Letter Queue to the Lambda function to capture failed records.

Answer

Decrease the batch size in the Lambda event source mapping.

Answer

Increase the Kinesis stream's retention period to 7 days.

Question 2

A data engineer needs to transform a large dataset stored in Amazon S3 using Apache Spark. The engineer wants to minimize costs and avoid managing infrastructure. Which AWS service should be used?

Accepted Answer

AWS Glue. AWS Glue is a serverless Spark ETL service that can process large datasets stored in S3 without requiring infrastructure management.

Answer

Amazon Athena

Answer

Amazon SageMaker

Answer

Amazon EMR

Question 3

A data engineer needs to transfer 50 TB of historical data from an on-premises Hadoop cluster to Amazon S3. The company has a 1 Gbps internet connection and wants to complete the transfer within 5 days. What is the MOST cost-effective and reliable solution?

Accepted Answer

Use AWS Snowball Edge device to physically ship the data. AWS Snowball Edge is a physical device that can transfer large amounts of data faster than over the internet. Option A is wrong because the internet connection would take about 5 days at full bandwidth, but is unreliable and may incur high costs. Option B is wrong because Direct Connect requires setup time and ongoing costs. Option D is wrong because S3 Transfer Acceleration may help but still relies on internet.

Answer

Use S3 multipart upload over the internet

Answer

Set up AWS Direct Connect and transfer over the dedicated line

Answer

Use S3 Transfer Acceleration to speed up the transfer

Question 4

A data engineering team needs to ingest streaming data from thousands of IoT devices into Amazon S3 for near-real-time analytics. The data arrives in bursts and must be processed with minimal latency. Which AWS service is most appropriate for the ingestion layer?

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is designed for real-time data streaming with low latency and can handle high throughput from many sources. Option A is wrong because SQS is for decoupled messaging, not streaming. Option C is wrong because Kinesis Data Firehose is for loading streaming data into destinations with some latency. Option D is wrong because S3 itself is not a streaming ingestion service.

Answer

Amazon Kinesis Data Firehose

Answer

Amazon SQS

Answer

Amazon S3

Question 5

A company uses Amazon EMR to run Spark jobs on a transient cluster that processes data from S3. The jobs are failing with 'OutOfMemory' errors. The data engineer has already increased the executor memory. Which additional configuration change would MOST likely resolve the issue?

Accepted Answer

Increase the number of partitions in the data. Increasing the number of partitions can reduce the amount of data each executor processes, alleviating memory pressure. Option A is wrong because more executors may increase parallelism but not reduce per-executor data. Option C is wrong because increasing driver memory addresses driver-side issues. Option D is wrong because using fewer nodes may worsen the problem.

Answer

Use fewer, larger instance types for the core nodes

Answer

Increase the driver memory

Answer

Increase the number of executors

Question 6

A team has deployed a SageMaker endpoint for a sentiment analysis model. The model was trained on text data from social media. After deployment, the team notices that the model's accuracy has dropped significantly after 3 months. Which action should the team take to detect and address this issue?

Accepted Answer

Enable SageMaker Model Monitor to detect data drift and trigger a retraining pipeline.. Setting up SageMaker Model Monitor to detect drift and triggering a retraining pipeline (Option D) automates detection and correction. Option A (re-deploy) does not address root cause. Option B (CloudWatch alarm) only monitors latency, not accuracy. Option C (A/B testing) helps compare but does not detect drift automatically.

Answer

Use SageMaker A/B testing to compare with a new model.

Answer

Re-deploy the model using the same training script.

Answer

Create a CloudWatch alarm on invocation errors.

Question 7

A company is building a data pipeline using AWS Glue to transform data from Amazon RDS to Amazon S3. The pipeline runs daily and processes about 500 GB of data. The team notices that the job is taking longer than expected. Which change would MOST improve the job performance?

Accepted Answer

Increase the number of DPUs for the Glue job. Increasing the number of DPUs (Data Processing Units) allocated to the Glue job can improve parallelism and reduce execution time. Option A is wrong because upgrading RDS may not be the bottleneck. Option B is wrong because using a smaller file size could increase overhead. Option D is wrong because disabling job bookmarking is not a performance improvement.

Answer

Disable job bookmarking

Answer

Upgrade the RDS instance to a larger class

Answer

Use smaller file sizes in S3 output

Question 8

A data engineering team needs to orchestrate a complex workflow that involves multiple AWS Glue jobs, Lambda functions, and S3 operations. The workflow must run on a schedule and allow monitoring of each step. Which AWS service should they use?

Accepted Answer

AWS Step Functions. AWS Step Functions is a serverless orchestration service that can coordinate multiple AWS services and visualizes workflows. Option A is wrong because CloudWatch Events can trigger based on events but not orchestrate. Option B is wrong because Data Pipeline is for data-driven workflows but less flexible. Option D is wrong because Simple Workflow Service is a legacy service.

Answer

Amazon Simple Workflow Service (SWF)

Answer

AWS Data Pipeline

Answer

Amazon CloudWatch Events

Question 9

A data engineer is troubleshooting an AWS Glue job that reads from an S3 bucket and writes to another S3 bucket. The job fails with an 'Access Denied' error when trying to write to the output bucket. The IAM policy attached to the Glue service role is shown. What is the MOST likely cause of the failure?

Accepted Answer

The output bucket is not listed in the Resource of the IAM policy. The policy only allows s3:GetObject and s3:PutObject on 'example-bucket', but the job is writing to a different bucket. The Glue job needs permissions on the output bucket as well. Option A is wrong because the job role has permissions for Glue actions. Option B is wrong because S3 permissions are for a specific bucket. Option D is wrong because the job role is separate from the user role.

Answer

The user who runs the job does not have S3 permissions

Answer

The Glue job role does not have permissions to start a job run

Answer

The S3 bucket policy denies access to the Glue service

Question 10

Which THREE are valid considerations when deploying a large deep learning model (10 GB) on a SageMaker endpoint? (Choose 3.)

Accepted Answer

Enable SageMaker Data Compression for network transfer.. Using GPU instances (Option A), enabling data compression (Option C), and using multi-model endpoints (Option D) are valid considerations. Option B (Elastic Inference) is deprecated and not recommended. Option E (serverless inference) has a payload limit and cold starts unsuitable for large models.

Answer

Use SageMaker Serverless Inference to avoid managing instances.

Answer

Attach Elastic Inference accelerators.

Question 11

A data engineer has attached the above IAM policy to an IAM role used by an AWS Glue ETL job. The job reads from and writes to 'my-data-bucket'. The job is failing with an Access Denied error. What is the most likely cause?

Accepted Answer

The condition restricts access to a specific IP range that does not include the AWS Glue service IPs.. The policy restricts access to requests originating from the IP range 10.0.0.0/24. AWS Glue jobs run in a VPC that uses private IPs, but the source IP condition is evaluated based on the IP address of the Glue service principal, which is not within that range. The condition should be removed or modified to allow access from the Glue service.

Answer

The IAM role needs to have s3:ListBucket permission.

Answer

The IAM role does not have permission to list the bucket.

Answer

The resource ARN should include the bucket itself, not just the objects.

Question 12

A machine learning team is preparing a large dataset for training. The dataset consists of 10,000 CSV files, each about 100 MB, stored in Amazon S3. The team wants to transform the data using AWS Glue ETL jobs. The transformation involves filtering rows, adding new columns, and joining with a small reference table (100 KB). The team is concerned about job performance and cost. They currently have a Glue job with 10 DPU (Data Processing Units) and it takes about 2 hours to complete. The team wants to reduce the runtime and cost. Which approach should they take?

Accepted Answer

Convert the CSV files to Parquet format and partition the data by a column.. Using a columnar format like Parquet and partitioning the data on a relevant column (e.g., date) can significantly reduce the amount of data scanned and improve performance. Additionally, optimizing the number of DPUs (e.g., using a larger number of DPUs for a shorter time) can reduce cost if the job is billed by DPU-hour.

Answer

Use Amazon Athena to transform the data.

Answer

Increase the number of DPUs to 100.

Answer

Use Amazon EMR with Spot Instances instead of AWS Glue.

Question 13

A data engineer is tasked with building a data pipeline that moves data from an on-premises database to Amazon S3 for analytics. The database is a MySQL instance that is 2 TB in size. The company has a 1 Gbps dedicated network connection to AWS (AWS Direct Connect). The data must be transferred once daily. The engineer needs to choose the most efficient and reliable service for this task. Which service should they use?

Accepted Answer

AWS Database Migration Service (DMS). AWS Database Migration Service (DMS) is designed for migrating databases to AWS and can continuously replicate data. For a one-time daily transfer, DMS can perform a full load and then ongoing replication if needed. It supports MySQL as a source and S3 as a target.

Answer

AWS DataSync

Answer

AWS Glue

Answer

Amazon S3 Transfer Acceleration

Question 14

A machine learning engineer is building a pipeline to preprocess data and train a model using Amazon SageMaker. The data is stored in Amazon S3 and the preprocessing step is computationally intensive. The engineer wants to minimize costs while ensuring that the preprocessing step does not fail due to instance termination. Which instance type should be used for the preprocessing step?

Accepted Answer

On-demand instances. Option C is correct because using on-demand instances guarantees that the instance will not be terminated during the preprocessing step. Option A is wrong because spot instances can be terminated, causing failures. Option B is wrong because reserved instances require a long-term commitment. Option D is wrong because a larger instance type increases costs unnecessarily.

Answer

Reserved instances

Answer

A larger instance type to speed up processing

Answer

Spot instances

Question 15

Refer to the exhibit. A developer has this IAM policy attached to an IAM role used by SageMaker. When attempting to create an endpoint, the operation fails with an access denied error. What is the MOST likely cause?

Accepted Answer

The policy is missing s3:ListBucket on the model bucket.. The policy grants permissions to create the model, endpoint config, and endpoint, but it does not include sagemaker:InvokeEndpoint (Option A). The error is likely due to missing sagemaker:InvokeEndpoint, but the question asks about creating an endpoint. Actually, creating an endpoint does not require InvokeEndpoint. The correct answer is that the policy is missing s3:ListBucket (Option B) because SageMaker needs to list objects in the bucket when accessing the model artifacts. Option C (ecr:DescribeRepositories) is not needed. Option D (sagemaker:DescribeEndpoint) is not required for creation.

Answer

The policy is missing ecr:DescribeRepositories.

Answer

The policy is missing sagemaker:DescribeEndpoint.

Answer

The policy is missing sagemaker:InvokeEndpoint.