MLS-C01 Practice Test 5 — 10 Questions

Question 1

An e-commerce company uses Amazon Kinesis Data Firehose to deliver clickstream data to Amazon S3. The data arrives at unpredictable rates, with occasional bursts. The company needs to ensure data is delivered within 60 seconds of ingestion, and the data must be partitioned by year/month/day/hour. Which configuration meets these requirements?

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

Accepted Answer

Set the buffer interval to 60 seconds and enable dynamic partitioning. Setting the buffer interval to 60 seconds ensures data is flushed to Amazon S3 within that time window, meeting the 60-second delivery requirement. Enabling dynamic partitioning allows Firehose to automatically partition data by year/month/day/hour based on the data's timestamp, without needing custom code or additional services.

Answer

Set the buffer size to 1 MB and disable dynamic partitioning

Answer

Use a Lambda function to process data and write to S3 with partitioning

Answer

Use AWS Glue streaming ETL to read from Firehose and write to S3

Question 2

A data engineer is designing a data pipeline that transforms raw JSON files (each 50-200 KB) in Amazon S3 into Parquet format using AWS Glue. The pipeline must minimize data processing costs and handle a high volume of small files (millions per day). The engineer configures a Glue ETL job with Spark, but the job is slow and expensive due to overhead of reading many small files. Which optimization should the engineer implement to reduce cost and improve performance?

Accepted Answer

Use S3 object grouping or batch operations to combine small files before Glue processing.. The primary performance bottleneck with many small files in S3 is the overhead of listing, opening, and reading each file individually in Spark. By grouping or batching small files into larger objects (e.g., using S3 Batch Operations or a pre-processing step), you reduce the number of input splits and task launches, which dramatically lowers the cost and runtime of the Glue ETL job. This directly addresses the root cause of the inefficiency rather than merely scaling resources.

Answer

Increase the worker type to G.2X for more memory per worker.

Answer

Increase the number of DPUs allocated to the Glue job.

Answer

Change the output format from Parquet to CSV to reduce compression overhead.

Question 3

A data scientist is training a binary classifier on an imbalanced dataset where the positive class represents 1% of the data. The model currently achieves 99% accuracy but a recall of only 10% on the positive class. Which metric combination should the data scientist prioritize to evaluate model improvements?

Accepted Answer

F1 score and AUC-ROC. With a highly imbalanced dataset (1% positive class), 99% accuracy is misleading because the model can achieve it by simply predicting the majority class. The low recall (10%) indicates the model fails to identify most positive instances. The F1 score balances precision and recall, providing a single metric for minority class performance, while AUC-ROC evaluates the model's ability to distinguish between classes across all thresholds, making it robust to class imbalance.

Answer

Precision and recall at 90% precision

Answer

Accuracy and RMSE

Answer

Precision and RMSE

Question 4

A data scientist needs to perform feature scaling for a dataset containing numerical features with different units (e.g., age in years and income in dollars). Which scaling method is most appropriate when the algorithm assumes data is normally distributed?

Accepted Answer

Standardization (Z-score normalization). Standardization (Z-score normalization) transforms features to have mean 0 and standard deviation 1, which is suitable for algorithms that assume normally distributed data. Option B (Log transformation) is used to reduce skewness, not for scaling when normality is already assumed. Option C (Min-Max scaling) scales to a fixed range (e.g., 0-1) but does not preserve normal distribution properties. Option D (Robust scaling) uses median and IQR, which is robust to outliers but does not assume normality.

Answer

Log transformation

Answer

Min-Max scaling

Answer

Robust scaling (using median and IQR)

Question 5

A data scientist is using AWS Glue to prepare training data. The job reads from an S3 bucket, performs transformations, and writes to another S3 bucket. The job is failing due to insufficient memory. Which solution should the data scientist use to fix this?

Accepted Answer

Increase the number of DPU (Data Processing Units) for the job.. The job is failing due to insufficient memory, which is a resource constraint. Increasing the number of DPU (Data Processing Units) allocates more memory and compute capacity to the AWS Glue job, directly addressing the out-of-memory error. This is the standard approach to scale Glue ETL jobs when they hit memory limits.

Answer

Use AWS Glue's job bookmark feature.

Answer

Use Amazon Athena instead of AWS Glue.

Answer

Use a columnar file format like Parquet.

Question 6

A data engineer runs the AWS CLI command shown in the exhibit to find large log files in S3. The command returns an empty list, but the engineer knows there are files larger than 1 MB in that prefix. What is the MOST likely issue?

Accepted Answer

The command does not paginate through all objects; only the first 1000 are returned. The `list-objects` command returns up to 1000 objects per call. If there are more than 1000 objects under the prefix, the command only examines the first 1000 objects. Since the engineer knows there are files larger than 1 MB, those files likely appear after the first 1000 objects. To find them, pagination is required (e.g., using `--page-size` or `--max-items` and `--starting-token`). Option A is incorrect because the JMESPath query is syntactically valid. Option C is incorrect because the engineer confirmed objects exist under the prefix. Option D is incorrect because `Size` is in bytes, so `1000000` correctly represents 1 MB.

Answer

The JMESPath query syntax is incorrect

Answer

The prefix is incorrect; there are no objects under that prefix

Answer

The Size value is in kilobytes, not bytes

Question 7

Refer to the exhibit. A data scientist creates a SageMaker model using the configuration above. When deploying the model to an endpoint, the endpoint status remains 'Creating' for a long time and then fails. What is the most likely cause?

Accepted Answer

The IAM role lacks permission to pull the Docker image from ECR. The image URI points to an ECR repository in account 382416733822, which is not the customer's account. SageMaker expects the image to be in the same account or accessible via cross-account permissions. This URI is likely the AWS account for built-in algorithms, but if the region or repository is incorrect, it may fail. The most likely issue is that the image does not exist in that account or the role lacks permissions to pull it.

Answer

The S3 model artifact does not exist

Answer

The environment variable SAGEMAKER_REGION is incorrect

Answer

The model name is already in use

Question 8

A data scientist needs to implement a recommendation system for an e-commerce website. Which Amazon service is specifically designed for building and deploying recommendation models?

Accepted Answer

Amazon Personalize. Amazon Personalize is a fully managed machine learning service that provides real-time personalized recommendations. It is purpose-built for recommendation systems. SageMaker is a general-purpose ML platform, but Personalize is specialized for recommendations.

Answer

Amazon SageMaker

Answer

Amazon Rekognition

Answer

Amazon Forecast

Question 9

A company runs an e-commerce platform that generates clickstream data in real-time. The data is ingested into Amazon Kinesis Data Streams (100 shards) and processed by AWS Lambda functions, which aggregate data in 1-minute windows and write the results to Amazon S3. The Lambda functions are triggered by the Kinesis stream using the event source mapping. Recently, the company noticed that some records are being processed multiple times, leading to duplicate data in S3. The Lambda function is idempotent, but the duplicates are causing downstream issues. The Lambda function's concurrency limit is 1000, and the batch size is 100. The average processing time per record is 200 ms. What is the most likely cause of the duplicates, and how should it be fixed?

Accepted Answer

Ensure the Lambda function is idempotent and uses the sequence number to deduplicate records.. Lambda functions process records from Kinesis in batches. If the function fails (e.g., due to timeout or error), the entire batch is retried, causing duplicates if some records were already partially processed. To avoid duplicates, the function should be idempotent and should not commit partial results. Option A is wrong because the concurrency is sufficient. Option C is wrong because increasing batch size increases the risk of partial failure. Option D is wrong because a FIFO queue does not integrate with Kinesis.

Answer

Increase the Lambda concurrency limit to 2000 to handle the load.

Answer

Decrease the batch size to 10 to reduce the impact of failures.

Answer

Use Amazon SQS FIFO queue as a buffer between Kinesis and Lambda to guarantee exactly-once processing.

Question 10

A data engineer needs to transform large CSV files stored in Amazon S3 into Parquet format before loading into Amazon Redshift. The transformation logic is complex and requires custom Python code. Which AWS service should be used to perform this transformation with minimal operational overhead?

Accepted Answer

AWS Glue. AWS Glue is the correct answer because it is a fully managed, serverless ETL service that can handle large CSV files, convert them to Parquet, and load into Amazon Redshift with minimal operational overhead. AWS Glue provides a built-in Spark environment and supports custom Python code via Spark jobs. Option B (AWS Lambda) has a 15-minute timeout and is not designed for large-scale data transformations. Option C (Amazon EMR) requires managing clusters, increasing operational overhead. Option D (AWS Data Pipeline) is a legacy service with less flexibility and is not optimized for complex transformations like CSV to Parquet.

Answer

AWS Lambda

Answer

Amazon EMR

Answer

AWS Data Pipeline