MLS-C01 Mock Exam 72 — 25 Questions

Question 1

A data science team at a financial services company is building a fraud detection model using a dataset of credit card transactions. The dataset contains 10 million rows and 20 features, including transaction amount, merchant category, time since last transaction, and customer ID. The target variable 'is_fraud' is highly imbalanced: only 0.1% of transactions are fraudulent. The team is performing exploratory data analysis (EDA) on a sample of 100,000 rows. They compute the correlation matrix and find that 'transaction amount' has a correlation of 0.02 with 'is_fraud'. They also plot the distribution of 'transaction amount' and see that it is heavily right-skewed with a long tail. The team wants to understand the relationship between 'transaction amount' and fraud more deeply before feature engineering. They have access to AWS SageMaker and can run processing jobs. Which course of action is most appropriate?

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

Accepted Answer

Create bins for 'transaction amount' (e.g., 0-10, 10-50, 50-100, 100+) and compute the fraud rate per bin to detect any non-linear patterns. Binning the transaction amount and computing fraud rates per bin can reveal non-linear relationships that correlation might miss. Option A is wrong because concluding non-predictiveness based solely on correlation ignores potential non-linear patterns. Option B is premature since feature importance from a random forest model is typically used after feature engineering, not during initial EDA. Option D is a data transformation that addresses skewness but does not directly help understand the relationship with the target; it would be more appropriate as a preprocessing step.

Answer

Conclude that 'transaction amount' is not predictive because the correlation is near zero

Answer

Train a random forest model on the sample and use feature importance to assess the predictive power of 'transaction amount'

Answer

Apply a log transformation to 'transaction amount' to reduce skewness and re-run the correlation analysis

Question 2

A data engineering team is building a real-time fraud detection system. Transactions are ingested via Amazon Kinesis Data Streams, and a machine learning model (deployed on Amazon SageMaker) scores each transaction. The team needs to store the raw transactions and the model's predictions in Amazon S3 for later analysis. Which architecture should the team use?

Accepted Answer

Use Amazon Kinesis Data Analytics for Apache Flink to enrich records with SageMaker predictions, then output to Firehose for S3.. It uses Amazon Kinesis Data Analytics for Apache Flink to perform real-time enrichment by invoking the SageMaker endpoint for each transaction, then streams the enriched records to Kinesis Data Firehose for reliable, batched delivery to S3. This architecture handles the asynchronous nature of model inference without blocking the ingestion stream, and Firehose provides automatic retry and compression for S3 storage.

Answer

Use AWS Lambda to read from Kinesis, invoke SageMaker, and write directly to S3.

Answer

Use Amazon Kinesis Data Firehose with a transformation Lambda to call SageMaker.

Answer

Use AWS Lambda to invoke the SageMaker endpoint for each record, then write to S3 via Firehose.

Question 3

Refer to the exhibit. A data scientist checks the status of a SageMaker endpoint and sees the output above. The endpoint is receiving traffic, but the data scientist notices that the number of instances has not increased to the desired count. What is the most likely reason?

Accepted Answer

Automatic scaling is not configured for the endpoint. The endpoint is receiving traffic but not scaling out, which indicates that automatic scaling (Application Auto Scaling) has not been configured for the SageMaker endpoint. Without a scaling policy, the endpoint will only use the initial instance count, regardless of traffic load. The status shown does not indicate any update or quota issue, so the lack of scaling is the most likely cause.

Answer

The endpoint is performing a rolling update

Answer

The endpoint is currently being updated

Answer

The account has reached its instance limit

Question 4

A team uses AWS Glue ETL jobs to preprocess data for SageMaker training. The job runs successfully but the output data is empty. What is the most likely cause?

Accepted Answer

The filter transformation condition is too restrictive, removing all rows. A filter transformation in AWS Glue ETL jobs can remove all rows if the condition is too restrictive, resulting in an empty output dataset. This is a common logic error where the filter predicate (e.g., `df.filter("value > 100")`) matches no records, causing the DynamicFrame to be empty after transformation. The job succeeds because no runtime error occurs, but the output is empty.

Answer

There is a data type mismatch between source and target

Answer

The source data is partitioned and only a subset of partitions is read

Answer

The Glue job runs out of memory and fails silently

Question 5

A data scientist is exploring a dataset with mixed data types (numeric, categorical, text). The dataset has 5 million rows. The scientist wants to understand the relationships between variables and identify potential data quality issues. Which THREE tools are suitable for this analysis?

Accepted Answer

AWS Glue DataBrew. Options A, C, and D are correct. AWS Glue DataBrew can profile data, visualize distributions, and detect anomalies. Amazon SageMaker Data Wrangler provides interactive data preparation and visualization. Amazon Athena can be used to run SQL queries for data quality checks. Option B (AWS Data Pipeline) is wrong because it is for workflow orchestration, not EDA. Option E (Amazon Kinesis Data Analytics) is wrong because it is for streaming data, not batch EDA.

Answer

AWS Data Pipeline

Answer

Amazon Kinesis Data Analytics

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

Answer

AWS Glue

Question 7

A data scientist trains a gradient boosting model on a large dataset using SageMaker. The training completes successfully, but when deploying the model to a real-time endpoint, inference latency is too high. Which change is MOST likely to reduce latency without significant accuracy loss?

Accepted Answer

Prune the trees by removing nodes with low importance. Pruning trees by removing nodes with low importance reduces the model's complexity, which directly decreases inference latency because fewer decision paths need to be evaluated. In gradient boosting, this can be done with minimal accuracy loss if the removed nodes correspond to splits that contribute little to the overall prediction, as measured by feature importance or gain.

Answer

Use a larger instance type for the endpoint

Answer

Increase the number of trees in the ensemble

Answer

Use SageMaker Batch Transform instead of real-time

Question 8

A machine learning engineer is responsible for deploying a model that was trained using a custom algorithm in Amazon SageMaker. The engineer has built a Docker container that includes the inference code and has tested it locally. The engineer now wants to deploy the container to a SageMaker endpoint for real-time inference. The engineer has already created the model in SageMaker by specifying the image URI and the model artifacts location in S3. However, when the engineer tries to create an endpoint configuration, the operation fails with an error indicating that the model is not in an 'Active' state. What should the engineer do to resolve this issue?

Accepted Answer

Check the CloudWatch logs for the container to ensure the inference server starts correctly. When a model is not in 'Active' state after creation, it typically indicates that the container's health check failed. Checking CloudWatch logs for the container helps identify why the inference server is not starting correctly. Option B is incorrect because the issue is with the model's state, not its name. Option C is incorrect because deleting and recreating the model would not resolve the underlying health check problem without fixing the container. Option D is incorrect because using a different image URI would change the container but not address the health check failure if the root cause is in the inference code or configuration.

Answer

Create the endpoint configuration with a different model name

Answer

Delete and re-create the model, then wait for a few minutes

Answer

Re-create the model using a different image URI

Question 9

A data engineer is designing a data pipeline to process streaming data from Amazon Kinesis Data Streams and store the results in Amazon S3 in Parquet format. The data must be available for querying in Amazon Athena within minutes of arrival. Which THREE services should be used together? (Choose THREE.)

Accepted Answer

Amazon Kinesis Data Firehose. Amazon Kinesis Data Firehose can directly ingest streaming data from Kinesis Data Streams, convert it to Parquet, and deliver to S3 with low latency. Amazon Kinesis Data Analytics can process and analyze the stream in real-time (e.g., aggregations or filtering) before sending to Firehose. AWS Glue provides a data catalog for the S3 data, making it queryable by Athena. Together, these three services enable near-real-time querying of streaming data in Athena.

Answer

Amazon EMR

Answer

Amazon Redshift

Question 10

A data engineering team is using Apache Spark on Amazon EMR to process streaming data from Amazon Kinesis Data Streams. The Spark application uses structured streaming to read from Kinesis, perform transformations, and write to Amazon S3 in Parquet format. The team notices that the application is falling behind and the processing latency is increasing. The Kinesis stream has 5 shards, and the EMR cluster has 5 core nodes of type r5.xlarge. The Spark application is configured with 5 executors, each with 2 cores and 8 GB memory. The team wants to reduce processing latency. Which change would be most effective?

Accepted Answer

Increase the number of shards in the Kinesis stream to 10 and increase the number of core nodes to 10.. The number of shards (5) matches the number of executors (5), but each shard can be processed by a single executor. To increase parallelism, the team should increase the number of shards in the Kinesis stream and correspondingly increase the number of executors or cores. Alternatively, they can increase the number of cores per executor to allow parallel processing of multiple shards per executor.

Answer

Increase the executor memory to 16 GB.

Answer

Use a larger instance type for the core nodes, such as r5.4xlarge.

Answer

Change the output format from Parquet to CSV to reduce write time.

Question 11

A data scientist ran an AWS Glue ETL job that failed with the error shown. What is the most likely cause?

Accepted Answer

The DataFrame does not have a column named 'age'. The error message indicates that the column 'age' is not found in the DataFrame, which only contains columns [id, name, salary]. Option A is incorrect because the error is about a missing column, not a header mismatch. Option C is incorrect because schema evolution would add a column, not cause a missing column error. Option D is incorrect because there is no indication of a data type issue; the error is about column existence.

Answer

The CSV file has a header mismatch

Answer

The schema is evolving incorrectly

Answer

The data type of 'age' is incompatible

Question 12

Which THREE techniques help reduce overfitting in a neural network? (Select THREE.)

Accepted Answer

Dropout. Dropout randomly drops units during training, L2 regularization penalizes large weights, and early stopping halts training when validation error increases. Data augmentation can also help but is not listed. Batch normalization may help but primarily for training stability.

Answer

Increasing the number of layers

Answer

Using a larger batch size

Question 13

A company uses Amazon SageMaker to train a model and wants to track metrics like loss and accuracy in real-time. Which SageMaker feature should be used?

Accepted Answer

SageMaker metrics and CloudWatch dashboards. SageMaker's built-in metrics integration with CloudWatch allows real-time tracking of training metrics such as loss and accuracy. Option A (SageMaker Model Monitor) is for monitoring inference quality and data drift after deployment, not real-time training metrics. Option C (SageMaker Experiments) is for organizing, tracking, and comparing different experiment runs, but it does not provide real-time streaming of metrics during training. Option D (SageMaker Debugger) is used for debugging training issues like gradient anomalies, not for real-time metric tracking.

Answer

SageMaker Model Monitor

Answer

SageMaker Experiments

Answer

SageMaker Debugger

Question 14

An IAM policy is attached to a data scientist's role. The scientist is trying to list objects in the 'data-bucket' using Amazon Athena. The query fails with an access denied error. What is the MOST likely reason?

Accepted Answer

The query is trying to read data from the 'sensitive/' prefix.. The IAM policy likely includes a Deny statement for the 'sensitive/' prefix, causing access denied when Athena attempts to read data from that location. Option A is incorrect because s3:ListBucket is typically required and allowed; the error is access denied rather than a forbidden error. Option B is incorrect because a syntax error would usually produce an invalid policy error, not an access denied error during query execution. Option D is incorrect because if s3:GetObject were explicitly denied for all objects, the error would occur for all queries, but the issue is specific to the 'sensitive/' prefix.

Answer

The policy does not allow s3:ListBucket on the bucket.

Answer

The policy has a syntax error.

Answer

The s3:GetObject action is explicitly denied for all objects.

Question 15

A company is using Amazon SageMaker to train an XGBoost model. The training data contains missing values. Which TWO methods can XGBoost handle missing values internally?

Accepted Answer

Learn the best direction to go when a value is missing.. XGBoost can handle missing values internally using a sparsity-aware algorithm. Option C correctly states that XGBoost learns the best direction to go when a value is missing. Option D correctly states that XGBoost treats missing values as a separate category. Option A (surrogate splits) is not used by XGBoost; Option B (dropping rows) is not an internal method; Option E (imputing with mean) is not an internal method.

Answer

Use surrogate splits to handle missing values.

Answer

Drop rows with missing values.

Answer

Impute missing values with the mean of the feature.

Question 16

A data scientist is training a binary classification model on an imbalanced dataset where the positive class is rare. The model currently achieves 95% accuracy but only 10% recall on the positive class. Which metric should the data scientist prioritize to improve model performance?

Accepted Answer

F1 score. The F1 score is the harmonic mean of precision and recall, making it the best single metric to optimize when the positive class is rare and both false positives and false negatives are costly. With 95% accuracy but only 10% recall, the model is likely predicting the majority class almost exclusively, so improving recall without sacrificing precision is critical — the F1 score directly balances this trade-off.

Answer

AUC-ROC

Answer

Precision

Answer

Accuracy

Question 17

A company is using Amazon SageMaker to deploy a model for real-time predictions. The model requires access to a DynamoDB table to look up features. The SageMaker endpoint is configured with a VPC and subnet. However, the endpoint cannot connect to DynamoDB. What is the most likely reason?

Accepted Answer

The VPC does not have a VPC endpoint for DynamoDB or a NAT gateway. A SageMaker endpoint deployed in a VPC, by default, cannot access public AWS services like DynamoDB unless the VPC has a VPC endpoint for DynamoDB (Gateway endpoint) or a NAT gateway to route traffic through an internet gateway. Without either, the endpoint's private subnet has no route to DynamoDB's public endpoints, causing the connection failure.

Answer

The security group does not allow outbound traffic to DynamoDB

Answer

The IAM role for the endpoint does not have dynamodb:GetItem permission

Answer

The DynamoDB table is in a different AWS Region

Answer

The CloudWatch logs show no errors

Question 18

An ML engineer is troubleshooting why an automated CI/CD pipeline cannot deploy an updated model to an existing SageMaker endpoint. The pipeline uses the IAM role that has the attached policy shown in the exhibit. What is the MOST likely cause of the failure?

Accepted Answer

The pipeline tries to update an existing endpoint, but the sagemaker:UpdateEndpoint action is not allowed.. The pipeline is attempting to deploy an updated model to an existing SageMaker endpoint, which requires the sagemaker:UpdateEndpoint action. The IAM policy shown in the exhibit (not provided here but implied) does not include this action, so the API call fails with an access denied error. Without explicit permission to update the endpoint, the CI/CD pipeline cannot modify the deployed configuration.

Answer

The pipeline tries to create a new endpoint, but the sagemaker:CreateEndpoint action is denied.

Answer

The pipeline tries to delete the old endpoint, but the sagemaker:DeleteEndpoint action is denied by a Deny statement.

Answer

The pipeline attempts to invoke the endpoint, but the sagemaker:InvokeEndpoint action is denied.

Question 19

A company is using Amazon DynamoDB as a source for a machine learning pipeline. The data is exported nightly to Amazon S3 using DynamoDB Streams and an AWS Glue job. The Glue job reads the stream records, transforms them, and writes to S3 in Parquet format. The team notices that the Glue job is taking too long and consuming high DynamoDB read capacity. Which THREE actions would reduce the load on DynamoDB and improve performance? (Choose THREE.)

Accepted Answer

Use Amazon DynamoDB export to S3 (incremental) feature instead of Glue. DynamoDB's native export to S3 (incremental) feature directly exports data to S3 without consuming read capacity units (RCUs) or requiring a separate compute service like AWS Glue. This eliminates the bottleneck of Glue reading from DynamoDB Streams, which consumes RCUs and adds latency, thereby reducing load on DynamoDB and improving overall performance.

Answer

Increase the DynamoDB write capacity units to handle the stream writes

Answer

Increase the DynamoDB read capacity units to handle Glue's workload

Question 20

Which THREE factors should a data engineer consider when choosing between Amazon S3 and Amazon Redshift for storing large datasets used for machine learning? (Choose 3.)

Accepted Answer

Query performance and latency requirements. When choosing between Amazon S3 and Amazon Redshift for ML data storage, key considerations include: (A) Query performance and latency: Redshift offers low-latency SQL querying on structured data, while S3 provides higher latency for direct access, making performance needs critical. (C) Cost of storage vs. compute: S3 decouples storage and compute, allowing independent scaling; Redshift combines them, affecting cost. (D) Data format and compression: S3 supports any format, but Redshift works best with columnar formats like Parquet. (B) Encryption at rest and (E) Data retention policies are available in both, so they are not differentiating factors.

Answer

Encryption at rest capabilities

Answer

Data retention policies