MLS-C01 Machine Learning Implementation and Operations Practice Test 12 — 15 Questions

Question 1

A data scientist is training a model using Amazon SageMaker. The training dataset is 500 GB and is stored in S3. The data scientist wants to use Pipe input mode to stream data directly from S3 to the training container. However, the training job fails with an error indicating that the container cannot read the data. What is the most likely cause?

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

Accepted Answer

The training algorithm does not support Pipe mode. Pipe mode in SageMaker streams data directly from S3 to the training container via a FIFO pipe, but the training algorithm must be specifically designed to read from a pipe (e.g., via stdin or a named pipe). If the algorithm expects random access to files or uses libraries that require seekable file handles (like many image-processing or custom Python scripts using `open()`), it will fail because a pipe is a sequential, non-seekable stream. SageMaker’s built-in algorithms like BlazingText and XGBoost support Pipe mode, but custom algorithms often do not unless explicitly coded for it.

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The training instance does not have enough memory

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The IAM role does not have s3:GetObject permission

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The data is compressed and Pipe mode cannot handle compressed data

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The S3 bucket is in a different Region

Question 2

Refer to the exhibit. A company is using an IAM role with the attached policy to deploy a SageMaker model. The data scientist can create training jobs and models, but when trying to create an endpoint, they receive an access denied error. What is the missing permission?

Accepted Answer

ec2:CreateNetworkInterface. When SageMaker creates an endpoint, it provisions a network interface in the customer's VPC to route traffic to the endpoint instances. The IAM role must have the ec2:CreateNetworkInterface permission to allow SageMaker to create this ENI; without it, the endpoint creation fails with an access denied error.

Answer

cloudwatch:PutMetricData

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iam:PassRole

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sagemaker:InvokeEndpoint

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kms:Decrypt

Question 3

Refer to the exhibit. A data scientist is reviewing CloudWatch logs for a SageMaker real-time endpoint. The log shows that a prediction took 15 ms. The endpoint is configured with an ml.c5.large instance and the model is a small scikit-learn model. The latency requirement is under 10 ms. Which action would most likely reduce the latency?

Accepted Answer

Use a larger instance type. The latency of 15 ms exceeds the 10 ms requirement, indicating that the current ml.c5.large instance lacks sufficient compute resources (CPU) to process predictions quickly enough. Upgrading to a larger instance type (e.g., ml.c5.xlarge or ml.c5.2xlarge) provides more CPU capacity, reducing inference time by allowing the model to compute predictions faster. This directly addresses the bottleneck for a small scikit-learn model, which is CPU-bound and benefits from increased compute power.

Answer

Add more instances to the endpoint

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Change the model to a TensorFlow model

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Enable SageMaker Batch Transform

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Increase the batch size for inference

Question 4

A company is using Amazon SageMaker to train a model. The training data includes sensitive personally identifiable information (PII). The company needs to ensure that the training data is protected and that the trained model does not inadvertently expose PII. Which TWO actions should the company take? (Choose TWO.)

Accepted Answer

Encrypt the training data in S3 using AWS KMS. Encrypting the training data in S3 using AWS KMS ensures that the data is protected at rest with customer-managed keys, providing a strong security control for sensitive PII. This encryption prevents unauthorized access to the raw data stored in S3, which is a fundamental requirement for data protection.

Answer

Use server-side encryption with S3-managed keys

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Enable AWS CloudTrail to log all access to the data

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Grant public read access to the training data for faster access

Question 5

A data scientist is deploying a model on Amazon SageMaker. The model requires inference on images, and the data scientist wants to use a GPU instance for low latency. However, the data scientist is unsure about the instance type to choose for the endpoint. Which TWO factors should the data scientist consider when selecting the instance type? (Choose TWO.)

Accepted Answer

The cost per inference for the instance type. Cost per inference directly impacts operational budget, especially with GPU instances that have higher hourly costs; data scientists must balance low latency with cost efficiency. Option E is correct because GPU memory limits the size of models and batch sizes that can be processed in a single inference call, directly affecting latency and throughput. Both factors are critical when selecting an instance type for real-time inference on SageMaker.

Answer

The time taken to train the model

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The number of vCPUs on the instance

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The AWS Region of the S3 bucket storing the model

Question 6

A company uses Amazon SageMaker to train models. The data scientist wants to automate the retraining process whenever new data arrives in an S3 bucket. Which THREE services can be used together to achieve this? (Choose THREE.)

Accepted Answer

Amazon S3. Amazon S3 is correct because it acts as the event source, emitting notifications (e.g., s3:ObjectCreated:*) when new data arrives. These events can be captured by AWS Lambda, which is correct because it can run a function that invokes Amazon SageMaker to start a retraining job. Amazon SageMaker is correct because it performs the actual model training. Together, S3 triggers the pipeline, Lambda orchestrates the invocation, and SageMaker executes the retraining. Options B (EC2) and E (Glue) are not directly required for this automated retraining workflow; EC2 is a compute service that would add unnecessary complexity, and Glue (data transformation) is not needed for the core trigger-and-train flow.

Answer

Amazon EC2

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AWS Glue

Question 7

A company operates a real-time fraud detection system using an Amazon SageMaker endpoint. The model is a gradient boosting model trained on historical transaction data. The endpoint is deployed on an ml.c5.2xlarge instance with auto-scaling enabled based on average latency. Recently, during a flash sale event, the endpoint started returning HTTP 503 errors. The CloudWatch metrics show that the CPU utilization is at 70%, and the average latency has increased from 50 ms to 200 ms. The auto-scaling policy is configured to add one instance when average latency exceeds 100 ms for 5 consecutive minutes, and remove one instance when latency drops below 50 ms for 5 minutes. The current number of instances is 2. The flash sale lasted 30 minutes. What should the company do to prevent this issue in future flash sales?

Accepted Answer

Pre-warm the endpoint by setting a minimum number of instances that can handle the expected peak load before the flash sale. The auto-scaling policy is reactive and requires a 5-minute evaluation period, which is too slow to handle the rapid traffic spike during a flash sale. Pre-warming the endpoint by setting a minimum number of instances to handle the expected peak load ensures capacity is available immediately. Option A (request throttling) would reject excess requests and cause errors, not prevent them. Option B (changing instance type) may help handle more load per instance but still suffers from the same reactive scaling delay, and it may be more expensive. Option D (simpler model) could reduce latency but may compromise model accuracy and does not address the scaling issue directly.

Answer

Enable request throttling to drop excess requests

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Change the instance type to ml.c5.4xlarge to handle higher load

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Change the model to a simpler model with lower latency

Question 8

A data scientist is using Amazon SageMaker to train a model on a large dataset (10 TB) stored in S3 in Parquet format. The training job uses an ml.p3.16xlarge instance with multiple GPUs. The data scientist notices that the GPU utilization is low (around 30%) and the training is slow. The dataset consists of hundreds of thousands of small Parquet files. The data scientist suspects that the I/O is bottlenecked. What should the data scientist do to improve GPU utilization and training speed?

Accepted Answer

Consolidate the small Parquet files into larger files (e.g., 1 GB each). Consolidating small Parquet files into larger files (e.g., 1 GB each) reduces the overhead of reading many small files from S3, improving I/O throughput and keeping GPUs busy. Option A (increase batch size) may help GPU utilization but does not address the I/O bottleneck. Option C (use a smaller instance) would not improve speed and may worsen the situation. Option D (Pipe input mode) can help with streaming but does not solve the small file issue; the data still comes from many small files.

Answer

Increase the batch size

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Use a smaller instance type to reduce cost

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Use Pipe input mode to stream data directly

Question 9

A company has deployed a machine learning model on a SageMaker endpoint that serves predictions to a web application. The model uses a custom inference container that loads the model artifacts from an ECR repository. After updating the model with new training data, the data scientist creates a new model and updates the endpoint. However, some users report that they still get predictions from the old model. The data scientist confirms that the endpoint configuration points to the new model. What is the most likely cause?

Accepted Answer

DNS caching on the client side is resolving to the old endpoint IP address. DNS caching on the client side can cause the endpoint's DNS name to resolve to an old IP address, especially if the endpoint's underlying instances have not changed. This explains why some users still receive predictions from the old model even though the endpoint configuration points to the new model. Option A is incorrect because incorrect model artifacts would affect all users uniformly. Option B is incorrect because SageMaker endpoints do not have load balancers in the traditional sense; the endpoint itself routes traffic to the instances. Option C is incorrect because SageMaker automatically handles container image updates when a new model is deployed to an endpoint.

Answer

The new model artifacts are not correctly uploaded to S3

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The endpoint is behind a load balancer that is not updated

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The inference container is cached and not pulling the new image

Question 10

A data scientist is using SageMaker to train a model. The training job is failing with a 'ResourceLimitExceeded' error. Which action should be taken to resolve this issue?

Accepted Answer

Request a service limit increase for the instance type.. The 'ResourceLimitExceeded' error in SageMaker indicates that the AWS account has reached the maximum number of allowed resources (e.g., instances, vCPUs, or storage) for a given instance type in the current region. Requesting a service limit increase via the AWS Service Quotas console or API directly resolves this by raising the cap for that specific instance type, allowing the training job to proceed.

Answer

Use a different AWS Region.

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Reduce the training dataset size.

Answer

Switch to a different instance type with lower resource requirements.

Question 11

A company is deploying a real-time inference endpoint using SageMaker. The model has a high memory footprint and requires GPU acceleration. Which instance type and configuration should be used to minimize cost while meeting latency requirements?

Accepted Answer

ml.p3.2xlarge with 1 GPU. (ml.p3.2xlarge with 1 GPU) is correct because it provides the required GPU acceleration for the high-memory-footprint model while using the smallest instance in the P3 family, which minimizes cost. The P3 instances use NVIDIA V100 GPUs with high memory bandwidth, suitable for real-time inference with low latency, and the 2xlarge size offers sufficient GPU memory without over-provisioning.

Answer

ml.g4dn.xlarge with 1 GPU

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ml.c5.xlarge with no GPU

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ml.p3.16xlarge with 8 GPUs

Question 12

A machine learning team is using AWS Glue to prepare data for training. They notice that the ETL job takes a long time to process large datasets. Which change is most likely to improve performance?

Accepted Answer

Increase the number of DPUs for the Glue job.. Increasing the number of DPUs (Data Processing Units) for the AWS Glue job allocates more distributed computing resources, which allows the job to process data in parallel across more executors. This directly reduces the runtime for large datasets by improving the parallelism of Spark transformations and actions.

Answer

Decrease the number of workers in the Glue job.

Answer

Disable Spark shuffle operations.

Answer

Reduce the dataset size by sampling.

Question 13

A company is using SageMaker Ground Truth to label images for a computer vision model. After launching the labeling job, they notice that the labeling throughput is lower than expected. What should they do to increase throughput?

Accepted Answer

Use a private workforce with more workers.. SageMaker Ground Truth labeling throughput is primarily limited by the number of workers available to process tasks. Using a private workforce allows you to directly control and scale the number of workers, which increases parallelism and overall throughput. Public or vendor workforces have fixed capacity and may not scale as quickly, so adding more private workers is the most effective way to boost throughput.

Answer

Change the labeling task to use a single annotator per image.

Answer

Reduce the number of workers assigned to each task.

Answer

Increase the time allowed for each labeling task.

Question 14

A data scientist is using SageMaker Debugger to monitor a training job. The training loss is not decreasing as expected. Which Debugger feature can help identify the issue?

Accepted Answer

Built-in rules to detect training anomalies. SageMaker Debugger's built-in rules are designed to automatically monitor training jobs for common issues such as vanishing gradients, overfitting, and loss not decreasing. When the training loss plateaus or fails to decrease, a rule like 'LossNotDecreasing' can trigger a CloudWatch alarm or stop the training job, providing immediate insight into the problem without manual inspection of tensors.

Answer

Automatic hyperparameter tuning

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Saving tensors every step

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Deploying a model endpoint for real-time monitoring

Question 15

A company is using Amazon Rekognition to detect objects in images stored in S3. They want to reduce costs by processing images only when they are uploaded. Which AWS service should be used to trigger Rekognition automatically?

Accepted Answer

AWS Lambda. AWS Lambda is the correct service because it can be triggered directly by S3 events (e.g., s3:ObjectCreated:Put) to invoke Amazon Rekognition's DetectLabels API on the newly uploaded image. This serverless architecture ensures processing occurs only on upload, eliminating idle costs and manual polling.

Answer

Amazon CloudWatch Events

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Amazon Simple Notification Service (SNS)

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AWS Step Functions