MLA-C01 Practice Test 8 — 10 Questions

Question 1

A machine learning engineer is troubleshooting a model that is producing unexpectedly low accuracy in production. The engineer examines the model's training data and finds that the distribution of the target variable in production is significantly different from the training set. What type of drift is the model experiencing?

Accepted Answer

Concept drift. Option B is correct because a change in the target variable distribution is concept drift. Option A is wrong because covariate shift is input distribution change. Option C is wrong because prior probability shift is a type of concept drift, but not the best answer here. Option D is wrong because data drift is a general term.

Answer

Prior probability shift

Answer

Data drift

Answer

Covariate shift

Question 2

A team deploys a machine learning model using a SageMaker endpoint with an ML.T4 instance. After a week, they notice that the endpoint's CPU utilization is consistently below 10% and latency is low. However, the endpoint is incurring high costs. Which action should the team take to reduce costs while maintaining the ability to serve traffic?

Accepted Answer

Migrate to a SageMaker Serverless Inference endpoint. Option C is correct because a serverless inference endpoint scales to zero when not in use, reducing cost. Option A is wrong because multi-model endpoints still have always-on instances. Option B is wrong because reducing instances may cause throttling. Option D is wrong because asynchronous inference is for batch, not real-time.

Answer

Switch to a multi-model endpoint to share instances across models

Answer

Reduce the number of instances to one

Answer

Implement an asynchronous inference endpoint

Question 3

A company has a SageMaker endpoint that uses a trained model to classify images. The endpoint is experiencing high latency and the team suspects it is due to the model size. Which action can the team take to reduce latency without significantly impacting accuracy?

Accepted Answer

Use SageMaker Neo to compile the model for the target instance. SageMaker Neo compiles trained models into an optimized binary for the target hardware, applying techniques like operator fusion, memory layout optimization, and quantization. This reduces model size and inference latency while preserving accuracy, making it the correct choice for addressing high latency caused by model size.

Answer

Switch to a compute-optimized instance type

Answer

Reduce the batch size of inference requests

Answer

Convert the model to ONNX format

Question 4

A company uses Amazon Rekognition to moderate user-generated images. They want to set up a monitoring system that alerts the team if the number of inappropriate images flagged by the model exceeds a threshold. Which combination of AWS services should they use?

Accepted Answer

Amazon CloudWatch to publish custom metrics and create an alarm, and AWS Lambda to process images and publish metrics.. Option A is correct because Amazon CloudWatch can publish custom metrics (number of flagged images) and trigger alarms. AWS Lambda can process the images and publish the metric. Option B is incomplete because CloudWatch Logs alone cannot trigger alarms on custom metrics. Option C is incorrect because SNS alone does not monitor metrics. Option D is incorrect because AWS Config is for configuration tracking.

Answer

Amazon CloudWatch Logs to store inference logs and create a metric filter.

Answer

AWS Config to track resource changes and trigger an SNS notification.

Answer

Amazon Simple Notification Service (SNS) to send alerts when threshold is exceeded.

Question 5

A data engineer is preparing a large dataset of 10 TB for ML training on Amazon SageMaker. The data is stored in Amazon S3 as CSV files. To reduce training time and cost, the engineer wants to use a columnar format that is optimized for analytical queries. Which format should the engineer convert the data to?

Accepted Answer

Parquet. Parquet is a columnar storage format that is highly optimized for analytical queries and is natively supported by Amazon SageMaker for efficient data loading. By converting the 10 TB of CSV data to Parquet, the data engineer can reduce I/O and storage costs because columnar formats allow SageMaker to read only the columns needed for training, rather than scanning entire rows. This directly addresses the goal of reducing training time and cost for ML workloads.

Answer

XML

Answer

ORC

Answer

JSON Lines

Question 6

In SageMaker Data Wrangler, you have a flow that imports data from Amazon S3 and needs to join it with a table from Amazon Redshift. The data volumes are large (hundreds of GB). Which approach is most efficient within Data Wrangler?

Accepted Answer

Import the Redshift table directly using a Data Wrangler source step and apply a join transform. Option D is correct because SageMaker Data Wrangler natively supports Amazon Redshift as a source via a direct connection, allowing you to import the Redshift table as a source step and then apply a join transform within the same visual flow. This approach avoids unnecessary data movement or intermediate exports, which is critical for hundreds of GB of data, as it leverages Data Wrangler's optimized in-memory and Spark-based processing to perform the join efficiently.

Answer

Use Amazon Athena federated query to join in place and import the result

Answer

Export the Redshift table to S3 as Parquet, then import both datasets into Data Wrangler and join

Answer

Use AWS Glue to join the datasets and output to S3, then import the joined result into Data Wrangler

Question 7

A data scientist creates a feature group as shown in the exhibit. When ingesting data with an 'age' column of integer values, the ingestion fails. What is the most likely cause?

Accepted Answer

The `age` feature type should be `Integral`, not `String`.. Option B is correct because the feature group definition specifies the 'age' column as a `String` type, but the ingested data contains integer values. Amazon SageMaker Feature Store requires that the data types of ingested records match the schema defined in the feature group. When a mismatch occurs, such as providing an integer for a string field, the ingestion fails with a type conversion error.

Answer

The role does not have permissions to write to the feature store.

Answer

The `OnlineStoreConfig` must include a `SecurityConfig`.

Answer

The `EventTimeFeatureName` is incorrectly spelled.

Question 8

A data scientist is preparing a dataset for training a binary classification model. The dataset has 100,000 rows and 50 features. The target variable is imbalanced, with only 5% positive cases. Which technique should the data scientist apply to address the class imbalance BEFORE training?

Accepted Answer

Random oversampling of the minority class. Random oversampling of the minority class (Option B) directly addresses the class imbalance by duplicating examples from the positive class until the class distribution is more balanced. This prevents the binary classification model from being biased toward the majority class, which is critical when only 5% of the 100,000 rows are positive cases. Oversampling is applied before training to ensure the model sees sufficient minority examples during learning.

Answer

Principal Component Analysis (PCA) dimensionality reduction

Answer

Standard scaling of numerical features

Answer

One-hot encoding of categorical variables

Question 9

A company is building a real-time inference pipeline for an ML model. The raw data arrives in JSON format via Amazon Kinesis Data Streams. Before invoking the SageMaker endpoint, the data must be preprocessed to match the training data format. Which THREE steps should be included in the preprocessing function? (Select THREE)

Accepted Answer

Ensure that missing values are handled consistently with the training phase. Option A is correct because the preprocessing function must handle missing values identically to how they were handled during training to maintain data consistency. If the training phase used mean imputation for a numeric feature, the inference pipeline must apply the same mean value; otherwise, the model will receive unexpected input distributions, degrading prediction accuracy.

Answer

Convert the data to a CSV string for model input

Answer

Re-train the model periodically using new data

Question 10

A company is preparing a dataset with a categorical feature that has over 1000 unique values. They need to create features for a random forest model. Which feature engineering approach is most scalable and effective in AWS for high-cardinality categories?

Accepted Answer

Target encoding with smoothing using SageMaker Data Wrangler. Target encoding with smoothing in SageMaker Data Wrangler is the most scalable and effective approach because it replaces each high-cardinality category with the mean of the target variable, smoothed by a global prior to prevent overfitting. SageMaker Data Wrangler handles datasets with over 1000 unique values efficiently without exploding feature dimensions, unlike one-hot encoding, and avoids the ordinal bias of label encoding.

Answer

Hash encoding using Apache Spark on Amazon EMR

Answer

One-hot encoding using SageMaker Processing with scikit-learn

Answer

Label encoding using Pandas in a SageMaker notebook