MLS-C01 Practice Test 18 — 15 Questions

Question 1

A data engineering team needs to set up a data pipeline that ingests streaming data from an Apache Kafka cluster running on Amazon EKS into an S3 data lake. The data must be stored in Parquet format, partitioned by date and event type. The team wants a fully managed solution with minimal operational overhead. Which solution should they choose?

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

Accepted Answer

Use Amazon MSK (Managed Streaming for Apache Kafka) and configure an MSK Connect S3 sink connector.. Amazon MSK is a fully managed Apache Kafka service that integrates with MSK Connect, which provides a pre-built S3 sink connector. This connector can directly stream data from Kafka topics to S3 in Parquet format with partitioning by date and event type, requiring no custom code or infrastructure management. This minimizes operational overhead while meeting all requirements for a fully managed solution.

Answer

Set up a Kinesis Data Firehose delivery stream that reads from Kafka and writes to S3.

Answer

Use AWS Glue ETL jobs to pull data from Kafka cluster periodically.

Answer

Create a Kinesis Data Analytics application to read from Kafka and write to S3.

Question 2

A company is deploying a machine learning model using Amazon SageMaker. The model needs to be updated frequently with new data. Which TWO approaches can be used to update the model without downtime? (Choose TWO.)

Accepted Answer

Use SageMaker A/B testing to gradually shift traffic to the new model variant.. Amazon SageMaker supports deploying multiple model variants behind a single endpoint using production variants. By using A/B testing (traffic shifting), you can gradually route a percentage of inference requests to the new model variant while the old variant continues serving the majority of traffic, enabling updates with zero downtime.

Answer

Delete the existing endpoint and create a new one with the updated model.

Answer

Directly update the model artifact in the existing endpoint configuration.

Answer

Stop the endpoint, update the model, and restart the endpoint.

Question 3

A company wants to use Amazon SageMaker to train a model using data that is updated daily. The training data is stored in an S3 bucket, and the team wants to automate the training process whenever new data arrives. Which AWS service should be used to trigger the SageMaker training job?

Accepted Answer

AWS Lambda triggered by S3 event notifications. AWS Lambda can be triggered by S3 event notifications (e.g., object creation). The Lambda function can then start the SageMaker training job using the AWS SDK, automating the process when new data arrives. Option B (Amazon CloudWatch Events) is incorrect because CloudWatch Events can schedule events based on time or AWS API calls but cannot directly react to S3 object creation without additional services. Option C (Amazon SQS) is incorrect because SQS is a message queue service; it does not natively trigger from S3 events without an intermediary like Lambda, which would still be the trigger. Option D (AWS Step Functions with a scheduled trigger) is incorrect because a scheduled trigger is time-based, not event-driven; Step Functions would need an S3 event to start, and that event would typically come via Lambda.

Answer

Amazon CloudWatch Events

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Amazon Simple Queue Service (SQS)

Answer

AWS Step Functions with a scheduled trigger

Question 4

A data scientist is exploring a dataset with many features and suspects that some features are highly correlated. Which TWO methods can the scientist use to detect and handle multicollinearity before building a linear regression model?

Accepted Answer

Compute Variance Inflation Factor (VIF) for each feature and remove features with VIF > 10.. Options C and E are correct. Variance Inflation Factor (VIF) is a standard metric to detect multicollinearity; removing features with VIF > 10 reduces multicollinearity. Ridge regression (L2 regularization) can also handle multicollinearity by shrinking coefficients, which stabilizes estimates even when predictors are correlated. Option A is incorrect because PCA reduces dimensionality but the resulting components are orthogonal, not the original features, and using all components does not address multicollinearity among the original predictors. Option B is incorrect because standardizing features only changes their scale, not their correlations. Option D is incorrect because stepwise selection does not directly detect or mitigate multicollinearity; it can even produce unstable models if collinearity is present.

Answer

Apply Principal Component Analysis (PCA) and use all components.

Answer

Standardize all features to have zero mean and unit variance.

Answer

Use stepwise feature selection.

Question 5

A data scientist is reviewing the training logs from a SageMaker training job. The logs show training and validation loss per epoch. Based on the exhibited logs, which statement is correct?

Accepted Answer

The model is overfitting because training loss decreases while validation loss does not. The training logs show a classic overfitting pattern: training loss consistently decreases across epochs, indicating the model is memorizing the training data, while validation loss does not decrease (or may even increase), indicating poor generalization to unseen data. In SageMaker, monitoring both losses during training is critical to detect overfitting early, often prompting regularization or early stopping.

Answer

The model is not learning because the loss is not decreasing

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The model is underfitting because both losses are high

Answer

The model is performing well because validation loss is stable

Question 6

A data engineer runs the CLI command to download an object from S3. The bucket owner is 123456789012, and the engineer's IAM user has s3:GetObject permission on the bucket. The object was uploaded by a different AWS account. What is the MOST likely reason for the AccessDenied error?

Accepted Answer

The object is owned by a different AWS account, and the bucket owner has not been granted access. When an object is uploaded to S3 by a different AWS account, the object is owned by the uploading account, not the bucket owner. By default, the bucket owner does not have access to objects uploaded by other accounts, even if the bucket owner has a policy granting s3:GetObject to their IAM users. The engineer's IAM user has permission on the bucket, but the object itself is not owned by the bucket owner, so the bucket owner cannot delegate access to it unless the object owner explicitly grants read access via an object ACL or a bucket policy that the object owner accepts.

Answer

The --expected-bucket-owner parameter is incorrect

Answer

The bucket policy denies access to the engineer's IAM user

Answer

The IAM policy does not allow s3:GetObject for that specific key

Question 7

A data engineering team is designing a data lake on AWS for machine learning workloads. The data includes structured, semi-structured, and unstructured data. The team needs to ensure that the data is cataloged, easily discoverable, and can be queried by Amazon Athena and Amazon EMR. The team also wants to enforce fine-grained access control at the column and row level for sensitive data. Which combination of AWS services should the team use? (Select TWO.)

Accepted Answer

AWS Lake Formation. AWS Lake Formation is correct because it provides a centralized service to build, secure, and manage data lakes on AWS. It enables fine-grained access control at the column and row level for sensitive data, which directly meets the requirement for enforcing such controls. Additionally, Lake Formation integrates with Amazon Athena and Amazon EMR for querying and processing the cataloged data.

Answer

AWS Identity and Access Management (IAM)

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Amazon RDS for PostgreSQL

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Amazon DynamoDB

Question 8

Which THREE of the following are best practices for training deep learning models on Amazon SageMaker?

Accepted Answer

Use SageMaker Debugger to profile system bottlenecks. SageMaker Debugger is a best practice because it provides real-time profiling of system bottlenecks such as CPU/GPU utilization, memory I/O, and network throughput during training. This allows you to identify and resolve performance issues early, optimizing training efficiency and cost. It integrates directly with SageMaker's training jobs without requiring code changes.

Answer

Disable automatic scaling to avoid interruptions

Answer

Always use the largest instance type available for faster training

Question 9

A machine learning engineer is training a deep learning model using the SageMaker built-in XGBoost algorithm. The training job is taking longer than expected. The engineer notices that the training data is stored in S3 in CSV format and is 500 GB in size. The instance type is ml.c4.8xlarge with 10 instances. Which change would most likely reduce training time?

Accepted Answer

Use Pipe input mode instead of File input mode.. Pipe input mode streams data directly from S3 to the training instances without first downloading it to the local EBS volume, eliminating the I/O bottleneck of reading a 500 GB CSV file. This reduces the time spent on data loading and allows the XGBoost algorithm to begin training sooner, which is especially beneficial for large datasets.

Answer

Convert the data to Parquet format.

Answer

Increase the number of instances to 20.

Answer

Increase the size of the EBS volume attached to each instance.

Question 10

During EDA, a data scientist finds that a numeric feature has many outliers. The feature will be used in a linear regression model. Which approach should the scientist take to handle the outliers?

Accepted Answer

Cap the feature values at the 1st and 99th percentiles.. Capping (winsorizing) the feature values at the 1st and 99th percentiles limits the influence of extreme outliers while retaining all data points. This is particularly important for linear regression, which is sensitive to outliers. Option A is wrong because removing all rows with outliers can lead to significant data loss and bias. Option B is wrong because a logarithmic transformation reduces skew but does not eliminate the impact of outliers; it only compresses their range. Option C is wrong because Z-score normalization standardizes the data but does not reduce the influence of outliers; extreme values remain extreme relative to the distribution.

Answer

Remove all rows with outlier values.

Answer

Apply a logarithmic transformation to the feature.

Answer

Standardize the feature using Z-score normalization.

Question 11

A data engineer is designing a data lake on Amazon S3 that must support both batch and streaming analytics. The data comes in Parquet format and needs to be queryable by Amazon Athena. Which partitioning strategy will optimize query performance and reduce costs?

Accepted Answer

Partition by date and hour for time-based queries. Partitioning by date and hour is optimal for time-series data in Parquet format queried by Athena because it leverages Hive-style partitioning to prune partitions during query execution, drastically reducing the amount of data scanned. This minimizes Athena's cost (which is based on data scanned) and improves query performance by limiting I/O to only the relevant partitions. Parquet's columnar storage further reduces scan volume when queries select only specific columns, making this combination highly efficient for both batch and streaming ingestion patterns.

Answer

Store data as CSV without partitioning for simplicity

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Partition by device_id for granular access

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Use a single partition for all data to simplify management

Question 12

A data scientist is using Amazon SageMaker to train a model. The training data is stored in Amazon S3 and is approximately 500 GB. The data scientist notices that the training job is taking a long time to start because the data is being copied to the training instance's storage. The data scientist wants to reduce the startup time for subsequent training jobs. Which action should the data scientist take?

Accepted Answer

Use Pipe input mode instead of File input mode for the training job. Using Pipe input mode streams data directly from S3 to the training algorithm without downloading, reducing startup time. Option B is wrong because FSx for Lustre is not needed for simple streaming. Option C is wrong because increasing instance storage does not address the data transfer issue. Option D is wrong because using EBS optimized instances does not change the data loading mechanism.

Answer

Use an EBS-optimized instance type

Answer

Use Amazon FSx for Lustre as a high-performance file system mounted to the training instance

Answer

Increase the size of the training instance's Amazon EBS storage volume

Question 13

A data scientist is training a model and wants to monitor training progress. Which AWS service can be used to track metrics like loss and accuracy in real time?

Accepted Answer

Amazon CloudWatch. Amazon CloudWatch is the correct service because it provides real-time monitoring of metrics such as loss and accuracy during model training. When using SageMaker, training jobs automatically emit metrics to CloudWatch via the CloudWatch agent, allowing you to view logs and set alarms on metric thresholds in near real-time.

Answer

Amazon SageMaker Ground Truth

Answer

Amazon SageMaker Automatic Model Tuning

Answer

AWS Glue

Question 14

A team is deploying a machine learning model to production using Amazon SageMaker. They want to automatically scale the endpoint based on the incoming request volume, and they also need to ensure that the endpoint can handle sudden bursts of traffic without dropping requests. Which scaling policy should they use?

Accepted Answer

Target tracking scaling policy based on the number of invocations. A target tracking scaling policy based on the number of invocations allows the endpoint to automatically adjust the number of instances to maintain a target metric value (e.g., invocations per instance). This policy can proactively scale out to handle sudden bursts by adding instances before the request queue grows, preventing dropped requests. SageMaker's built-in scaling metric, 'SageMakerVariantInvocationsPerInstance', is ideal for this use case as it directly correlates with traffic volume.

Answer

Scheduled scaling policy for peak hours

Answer

Simple scaling policy based on average latency

Answer

Manual scaling by monitoring CloudWatch alarms

Question 15

A company uses Amazon Kinesis Data Streams to collect clickstream data. The data is consumed by a Lambda function that writes to DynamoDB. Occasionally, the Lambda function fails due to throttling from DynamoDB. How can the company resolve this issue without losing data?

Accepted Answer

Decrease the batch size in the Lambda event source mapping.. Decreasing the batch size in the Lambda event source mapping reduces the number of records sent to each Lambda invocation. This lowers the write throughput demand on DynamoDB per invocation, mitigating throttling while still allowing Lambda to retry failed records individually. The Kinesis stream retains data for up to 365 days, so no data is lost as long as the Lambda function eventually processes all records.

Answer

Ignore the throttling errors and let Lambda retry.

Answer

Increase the number of shards in the Kinesis stream.

Answer

Use an Amazon SQS queue as a buffer between Kinesis and Lambda.