MLA-C01 Practice Test 9 — 10 Questions

Question 1

A company is deploying a real-time inference endpoint for a natural language processing model using Amazon SageMaker. The model requires GPU acceleration and must handle variable traffic patterns, including sudden spikes. The team wants to minimize costs while maintaining low latency during spikes. Which endpoint configuration strategy should they use?

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

Accepted Answer

Use a multi-model endpoint on a GPU instance with Auto Scaling based on invocation count.. A multi-model endpoint on a GPU instance with Auto Scaling based on invocation count allows multiple models to share a single GPU, maximizing utilization and reducing cost. Auto Scaling based on invocation count dynamically adjusts the number of instances to handle traffic spikes while maintaining low latency, as it scales out quickly when the invocation count exceeds a threshold.

Answer

Use a single large GPU instance with provisioned concurrency.

Answer

Use a serverless endpoint with GPU support.

Answer

Use a single GPU instance in multiple Availability Zones with an Application Load Balancer.

Question 2

A team is building a regression model on a dataset with missing values in multiple features. They decide to use a k-Nearest Neighbors (k-NN) imputer. The dataset has 100,000 rows and 50 features. Which step should the team take to ensure the imputation is efficient and accurate?

Accepted Answer

Standardize the features before applying k-NN imputation. Standardizing features before applying k-NN imputation is critical because k-NN relies on distance calculations (e.g., Euclidean distance). If features are on different scales (e.g., one feature ranges 0–1 and another 0–100,000), the distance metric will be dominated by the larger-scale feature, leading to biased neighbor selection and inaccurate imputation. Standardization (e.g., z-score scaling) ensures each feature contributes equally to the distance computation, improving both efficiency and accuracy.

Answer

Set k=1 to minimize bias

Answer

Use all 100,000 rows to find neighbors for each missing value

Answer

Use only the feature with missing values to find neighbors

Question 3

A data scientist is training a linear learner model using SageMaker and notices that the loss is not decreasing. They suspect the issue is exploding gradients. Which SageMaker Debugger rule should they enable to monitor this?

Accepted Answer

ExplodingGradients. The ExplodingGradients rule tracks gradient values and alerts if they exceed a threshold, which is the correct detection for exploding gradients.

Answer

LossNotDecreasing

Answer

VanishingGradients

Answer

Overfit

Question 4

A machine learning engineer is building a pipeline to ingest data from multiple sources into an Amazon S3 data lake. The data includes both batch files and real-time streams. The engineer needs to catalog the data for discovery and querying. Which TWO AWS services should the engineer use to ingest and catalog the data? (Select TWO.)

Accepted Answer

Amazon S3. S3 is the data lake storage; AWS Glue Data Catalog stores metadata for discovery and querying. AWS Glue ETL can ingest batch data; Kinesis Firehose ingests streaming data. Athena is for querying, not ingestion or cataloging; Redshift is a data warehouse.

Answer

Amazon Redshift

Answer

Amazon Athena

Answer

AWS Glue ETL

Question 5

A data scientist is using Amazon SageMaker Data Wrangler to create a data flow for a machine learning project. The source data is in Amazon S3 and contains PII (personally identifiable information) such as email addresses and credit card numbers. The data scientist needs to prepare the data for training while ensuring compliance with data privacy regulations. Which THREE actions should the data scientist take? (Select THREE.)

Accepted Answer

Use Data Wrangler to redact or remove PII columns from the dataset before training.. Amazon SageMaker Data Wrangler provides built-in transforms to redact or remove PII columns, which directly addresses compliance requirements by eliminating sensitive data from the training dataset. This is a straightforward and effective method to prevent PII from being used in model training, reducing the risk of data exposure.

Answer

Include the raw PII in the training dataset and rely on the model to not memorize it.

Answer

Use AWS Glue to copy the data to a separate bucket without any transformations.

Question 6

A company requires that all SageMaker notebook instances be created within a private VPC without internet access. Which configuration step is mandatory?

Accepted Answer

Configure VPC settings when creating the notebook instance, choosing a private subnet.. When creating a SageMaker notebook instance, you must explicitly configure the VPC settings and select a private subnet to ensure the instance is launched within a private VPC without internet access. This is mandatory because SageMaker notebook instances, by default, are created with internet access enabled unless you specify a VPC with no direct internet access. Selecting a private subnet ensures the instance uses only VPC endpoints or NAT gateways for outbound traffic, meeting the no-internet requirement.

Answer

Use a SageMaker Studio notebook instead.

Answer

Enable SageMaker direct internet access.

Answer

Assign a public IP to the notebook instance.

Question 7

Refer to the exhibit. A SageMaker training job failed. Based on the error message, which action should the engineer take?

Accepted Answer

Use a larger instance type. The error message indicates that the training job failed due to insufficient memory (an out-of-memory error). Using a larger instance type (Option B) provides more RAM, which directly resolves the memory exhaustion issue. SageMaker training jobs run on EC2 instances, and the instance type determines the available memory and compute resources.

Answer

Change the algorithm

Answer

Increase the volume size

Answer

Increase the instance count

Question 8

A machine learning engineer is building a pipeline to preprocess text data for a sentiment analysis model. The data consists of customer reviews. The engineer wants to convert the text into numerical features while preserving the semantic meaning of words. Which technique should be used?

Accepted Answer

Word embeddings (e.g., Word2Vec or GloVe). Word embeddings (like Word2Vec or GloVe) are dense vector representations that capture semantic relationships between words based on their context in a large corpus. For sentiment analysis, preserving semantic meaning (e.g., 'good' and 'excellent' having similar vectors) is critical, and embeddings directly encode this, unlike sparse or count-based methods.

Answer

One-hot encoding of each word

Answer

Bag-of-words with TF-IDF

Answer

Hashing vectorizer

Question 9

A data scientist wants to fine-tune a large language model for a question-answering task. They want to reduce memory usage during training by using a low-rank approximation of the weight updates. Which technique should they use?

Accepted Answer

LoRA. LoRA (Low-Rank Adaptation) adds low-rank matrices to model weights, significantly reducing memory footprint while achieving competitive performance. QLoRA adds quantization for further reduction.

Answer

Full fine-tuning

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Instruction tuning

Answer

RLHF

Question 10

A company is running a SageMaker endpoint serving multiple models. They need to monitor for data drift and model quality. Which THREE actions are necessary? (Choose three.)

Accepted Answer

Enable data capture on the endpoint. Enabling data capture on the SageMaker endpoint is a prerequisite for monitoring data drift and model quality. Data capture automatically records input requests and output responses from the endpoint, which SageMaker Model Monitor later analyzes against a baseline to detect drift. Without data capture, there is no data to compare against the baseline constraints.

Answer

Deploy a shadow endpoint for comparison

Answer

Use SageMaker Debugger for monitoring