MLS-C01 Practice Test 14 — 15 Questions

Question 1

A data scientist is training a neural network using a custom loss function. The training process converges, but the model's performance on the validation set is poor. The data scientist suspects that the model is overfitting. Which action should the data scientist take to diagnose overfitting?

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

Accepted Answer

Plot the training and validation loss over epochs. Plotting the training and validation loss over epochs is the standard diagnostic technique for detecting overfitting. If the training loss continues to decrease while the validation loss plateaus or increases, it indicates that the model is memorizing the training data rather than generalizing. This visual comparison directly confirms overfitting, allowing the data scientist to take corrective action such as regularization or early stopping.

Answer

Add more layers to the network

Answer

Increase the learning rate

Answer

Compute the confusion matrix on the training set

Question 2

A company is using AWS Glue to catalog metadata from various data sources. The crawler is configured to run daily. However, the catalog is not reflecting new partitions added to an S3 bucket during the day. What is the MOST likely cause?

Accepted Answer

The crawler is not scheduled frequently enough to capture changes. The crawler is configured to run daily, but new partitions are being added to the S3 bucket throughout the day. Since the crawler only runs once per day, it will not detect and catalog those new partitions until its next scheduled run. To capture changes more frequently, the crawler schedule should be increased or an event-driven trigger (e.g., using Amazon S3 Events and AWS Lambda) should be implemented.

Answer

The S3 bucket has insufficient permissions for the Glue crawler

Answer

The table schema has changed and the crawler does not update it

Answer

The data format is not supported by AWS Glue

Question 3

A data scientist is using Amazon SageMaker to train a TensorFlow model on a dataset that includes sensitive personal information (PII). The data is stored in Amazon S3 with server-side encryption using AWS KMS (SSE-KMS). The training job fails with an Access Denied error when trying to read from S3. The data scientist has already verified that the SageMaker execution role has s3:GetObject permissions on the S3 bucket. What additional configuration is needed?

Accepted Answer

Add kms:Decrypt permission to the SageMaker execution role.. When S3 objects are encrypted with SSE-KMS, the SageMaker execution role must have the kms:Decrypt permission to decrypt the data during training. Even though the role has s3:GetObject access, the KMS key policy or the role's IAM policy must explicitly allow decryption of the KMS key used for server-side encryption. Without this, SageMaker cannot read the encrypted objects, resulting in an Access Denied error.

Answer

Add kms:Encrypt permission to the SageMaker execution role.

Answer

Add a bucket policy that grants s3:GetObject to the SageMaker role.

Answer

Configure a VPC endpoint for S3 and attach a policy.

Question 4

Which THREE factors should be considered when choosing an instance type for a SageMaker training job?

Accepted Answer

The number of vCPUs needed for parallel processing. The number of vCPUs directly determines the parallel processing capability of the training job. SageMaker training instances with more vCPUs can handle larger batch sizes and more concurrent data loading, which is critical for CPU-bound preprocessing or model training that does not rely on GPUs. Choosing an instance with insufficient vCPUs can lead to underutilization of other resources or excessive training time.

Answer

The endpoint latency requirement

Answer

The AWS region where the instance is launched

Question 5

A data engineer runs the AWS CLI command above to inspect a file in S3. They need to determine if the file was modified after a Glue ETL job processed it. What additional information could they obtain from this command?

Accepted Answer

The object's last modified timestamp.. The AWS CLI command `aws s3api head-object --bucket my-bucket --key my-key` returns metadata about an S3 object without downloading it. The `LastModified` field in the response provides the exact timestamp of the last modification, which can be compared to the Glue ETL job's execution time to determine if the file was modified after processing. This is the only field that directly answers the question about modification timing.

Answer

The object's content type.

Answer

The object's storage class.

Answer

The object's ETag.

Question 6

Refer to the exhibit. An ML engineer runs the above CLI command to inspect files in an S3 bucket. The training data consists of 200 CSV files, each 1 GB. The engineer plans to use Amazon SageMaker to train a model using this data. What should the engineer do to optimize training performance?

Accepted Answer

Convert the CSV files to Parquet format and use Pipe input mode.. Converting CSV files to Parquet format and using Pipe input mode significantly improves training performance. Parquet is a columnar storage format that reduces I/O by reading only relevant columns, and it is compressed. Pipe input mode streams data directly from S3 to the training algorithm without downloading to EBS, reducing startup time and disk usage. Option A is incorrect because simply increasing the number of instances does not address the inefficiency of reading CSV files; it may help parallelization but not per-instance throughput. Option B is incorrect because Amazon Athena is a query service, not a data transformation tool for SageMaker; converting to CSV with headers does not improve performance. Option C is incorrect because using File input mode copies all files to the training instance's EBS volume, which is slow for 200 GB of data and does not leverage streaming benefits.

Answer

Increase the number of training instances to process files in parallel.

Answer

Use Amazon Athena to transform the data into CSV format with headers.

Answer

Use the File input mode and copy all files to the training instance's EBS volume.

Question 7

A company is deploying a machine learning model using SageMaker. The model is a PyTorch model that requires GPU for inference. The company wants to minimize costs while ensuring low latency. Which instance type should be used for the SageMaker endpoint?

Accepted Answer

ml.g4dn.xlarge. Ml.g4dn.xlarge is a GPU instance with an NVIDIA T4, optimized for inference, and is more cost-effective than ml.p3.2xlarge while still providing low latency for PyTorch models. Option A (ml.m5.large) is wrong as it is a CPU instance without GPU support. Option B (ml.p3.2xlarge) is wrong because although it has a GPU, it is more expensive and not necessary for low-latency inference; ml.g4dn.xlarge offers similar performance at lower cost. Option C (ml.c5.2xlarge) is also a CPU instance and unsuitable.

Answer

ml.m5.large

Answer

ml.p3.2xlarge

Answer

ml.c5.2xlarge

Question 8

A data engineer configures an S3 event notification to trigger an AWS Lambda function when a new object is created in 'my-input-bucket'. The Lambda function processes the CSV file and writes results to 'my-output-bucket'. The engineer notices that the Lambda function is not triggered for some objects. Which step should the engineer take to diagnose the issue?

Accepted Answer

Check the Lambda function's resource-based policy to ensure S3 has permission to invoke the function.. The most likely cause of the Lambda function not being triggered for some objects is that S3 lacks the necessary permission to invoke the function. S3 event notifications require a resource-based policy (also known as a Lambda function policy) that explicitly grants the S3 service principal permission to invoke the function. Without this policy, S3 will not be able to trigger the Lambda function, even if the event notification configuration is correct.

Answer

Check the Lambda function's execution role for permissions to write to the output bucket.

Answer

Review the CloudWatch Logs for the Lambda function to see if there are errors.

Answer

Verify that the S3 event notification is configured with the correct prefix and suffix filters.

Question 9

A company uses Amazon Kinesis Data Analytics for real-time anomaly detection on a stream of IoT sensor data. The application is experiencing high latency. The data volume has doubled. Which action would MOST effectively reduce latency?

Accepted Answer

Increase the Parallelism setting of the Kinesis Data Analytics application. Increasing the Parallelism setting of the Kinesis Data Analytics application directly allocates more processing resources (e.g., more Kinesis Processing Units or KPUs) to handle the doubled data volume. This allows the application to process records concurrently, reducing the per-record processing time and overall latency. Parallelism is the primary scaling mechanism for Kinesis Data Analytics to match throughput increases.

Answer

Change the record format from JSON to Avro

Answer

Decrease the retention period of the source stream

Answer

Increase the number of shards in the source Kinesis stream

Question 10

A company needs to perform complex transformations on large datasets stored in Amazon S3 using Apache Spark. They want to minimize operational overhead. Which AWS service should they use?

Accepted Answer

Amazon EMR. Amazon EMR is the correct choice because it is a managed big data platform that natively runs Apache Spark, allowing you to perform complex transformations on large datasets stored in Amazon S3 without provisioning or managing underlying infrastructure. EMR automatically handles cluster lifecycle, scaling, and tuning, minimizing operational overhead while providing full Spark compatibility.

Answer

Amazon EC2 with manually configured Spark

Answer

Amazon Athena

Answer

AWS Glue

Question 11

A company uses AWS Glue crawlers to populate the AWS Glue Data Catalog from Amazon S3. The data is partitioned by year/month/day/hour. The crawler runs every hour and adds new partitions. However, the data engineer notices that the crawler is taking longer to run as the number of partitions grows, and sometimes it misses new partitions. What is the most cost-effective and reliable way to address this?

Accepted Answer

Enable the crawler's partition index feature.. Enabling the crawler's partition index feature allows AWS Glue to quickly find new partitions without re-scanning the entire table, reducing runtime and improving reliability. Option B is incorrect because manually adding partitions is error-prone and does not scale. Option C is incorrect because the MSCK REPAIR TABLE command is a manual step that does not automate the process. Option D is incorrect because increasing the crawler's frequency does not address the underlying issue of scanning overhead and may lead to resource contention.

Answer

Manually add new partitions using ALTER TABLE ADD PARTITION in Athena.

Answer

Use the Athena MSCK REPAIR TABLE command after the crawler runs.

Answer

Increase the crawler's schedule to run every 30 minutes.

Question 12

A data scientist wants to deploy a PyTorch model for real-time inference with low latency. Which AWS service should they use?

Accepted Answer

Amazon SageMaker real-time endpoint. Amazon SageMaker real-time endpoints are specifically designed for low-latency inference on deployed models, including PyTorch models. They provide persistent HTTPS endpoints that autoscale and support custom containers, making them ideal for real-time prediction workloads.

Answer

Amazon Elastic Container Service (ECS)

Answer

Amazon SageMaker batch transform

Answer

AWS Lambda

Question 13

A data scientist is troubleshooting a failed SageMaker training job that uses a custom Docker image. The failure reason shows 'unrecognized arguments: --sagemaker_program'. What is the most likely cause?

Accepted Answer

The custom Docker image does not use the SageMaker training toolkit and thus does not accept SageMaker hyperparameters. The error 'unrecognized arguments: --sagemaker_program' indicates that the custom Docker image does not include the SageMaker Training Toolkit. The SageMaker Training Toolkit is a Python library that provides a default entry point to parse and handle SageMaker-specific hyperparameters (like --sagemaker_program, --sagemaker_submit_directory, etc.). Without this toolkit, the container's entry point does not recognize these arguments, causing the training job to fail.

Answer

The Docker image is tagged incorrectly and cannot be pulled

Answer

The training job is in a different region than the ECR repository

Answer

The input mode is File mode, but the container expects Pipe mode

Question 14

A company is deploying a machine learning model on SageMaker for real-time inference. The model requires GPU for low latency. Which THREE steps are necessary to set up the endpoint?

Accepted Answer

Create a SageMaker model object that points to the S3 bucket containing the model artifacts and the inference container image. To deploy a model for real-time inference on SageMaker, you must first create a SageMaker model object that references the model artifacts stored in S3 and the inference container image (e.g., a GPU-enabled Docker image). This object is the foundational resource that SageMaker uses to launch instances for serving predictions.

Answer

Train the model using a SageMaker training job

Answer

Create a SageMaker batch transform job

Question 15

A company uses Amazon Kinesis Data Firehose to deliver streaming data to Amazon S3. The data contains personally identifiable information (PII) that must be redacted before storage. Which AWS service can be integrated with Kinesis Data Firehose to transform the data in real time?

Accepted Answer

AWS Lambda. AWS Lambda can be integrated as a data transformation function within a Kinesis Data Firehose delivery stream. When Firehose receives incoming records, it can invoke a Lambda function synchronously to process each batch of data, allowing you to redact PII fields in real time before the data is written to Amazon S3.

Answer

Amazon Athena

Answer

Amazon Kinesis Data Analytics

Answer

Amazon EMR