MLS-C01 Practice Test 9 — 10 Questions

Question 1

A company processes large streams of IoT sensor data using Amazon Kinesis Data Streams with 100 shards. Each sensor reading is about 1 KB. The data is consumed by an Amazon EMR cluster running Spark Streaming jobs. The team notices that the Spark Streaming job's processing time is gradually increasing, and the stream is falling behind. They suspect the issue is due to skewed data distribution across shards. Which approach should the team take to diagnose and resolve the issue?

Accepted Answer

Modify the producer to add a random prefix to the partition key, ensuring even distribution across all shards, and monitor the stream using CloudWatch.. Option B is correct because adding a random prefix to the partition key ensures that sensor data is evenly distributed across all 100 shards, eliminating hot shards that cause processing delays. This directly addresses the skewed data distribution issue without requiring infrastructure changes, and the team can monitor the improvement using CloudWatch metrics like IncomingBytes and ReadProvisionedThroughputExceeded.

Answer

Increase the number of shards to 200 to provide more parallelism.

Answer

Check Amazon CloudWatch metrics for Kinesis to identify hot shards, then manually redistribute the data by repartitioning in Spark.

Answer

Use the Kinesis Client Library (KCL) with a custom worker to rebalance the load across shards.

Question 2

A team is using Amazon SageMaker to train a model on a dataset that is 500 GB in size, stored as CSV files in S3. The training job takes 2 hours using a single ml.p3.2xlarge instance. The team wants to reduce training time to under 30 minutes. The model architecture supports distributed training. Which solution will achieve this goal with the LEAST amount of code changes?

Accepted Answer

Use multiple ml.p3.2xlarge instances with SageMaker's distributed data parallelism library, enabling automatic sharding of the training data.. Option C is correct because SageMaker's distributed data parallelism library automatically shards the training data across multiple ml.p3.2xlarge instances, enabling parallel gradient computation and reducing wall-clock training time from 2 hours to under 30 minutes without requiring manual code changes to the training script. The model architecture already supports distributed training, so the library handles the communication and synchronization (e.g., AllReduce) transparently.

Answer

Use managed spot training to reduce cost and then use cost savings to train with a larger instance.

Answer

Use a single ml.p3.16xlarge instance with more GPUs and memory.

Answer

Change the input mode to Pipe mode to stream data from S3 directly, reducing I/O wait time.

Question 3

A company is building a real-time anomaly detection system for network traffic logs. The logs are ingested via Amazon Kinesis Data Streams and processed with an Amazon SageMaker endpoint for inference. The team needs to ensure that the inference results are stored durably and can be replayed for model retraining. The system must handle at least 10,000 records per second with low latency. Which three AWS services should the team use to build this architecture? (Select THREE.)

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams is the correct ingestion layer because it provides durable, real-time data streaming with the ability to handle over 10,000 records per second. It acts as the source of truth for network traffic logs, enabling low-latency processing and replay for model retraining.

Answer

AWS Glue ETL

Answer

Amazon Kinesis Data Firehose

Question 4

A data engineer is designing a streaming pipeline using Amazon Kinesis Data Analytics for Apache Flink. The pipeline reads from a Kinesis data stream and writes to a S3 bucket. The job must recover quickly from failures without reprocessing large amounts of data. Which TWO configurations should be used? (Choose TWO)

Accepted Answer

Enable checkpointing with a state backend like RocksDB.. Option A is correct because enabling checkpointing with a state backend like RocksDB allows Apache Flink to periodically save the state of the streaming application to durable storage. In the event of a failure, Flink can restart from the last completed checkpoint, avoiding the need to reprocess large amounts of data from the beginning of the stream. RocksDB is specifically designed for large state and provides fast recovery by storing state on disk with memory caching, making it ideal for production streaming pipelines.

Answer

Use in-memory state backend for low latency.

Answer

Set the parallelism to the maximum number of shards.

Answer

Increase the retention period of the Kinesis stream to 365 days.

Question 5

Drag and drop the steps to create an Amazon SageMaker notebook instance in the correct order.

Question 6

Drag and drop the steps to set up Amazon SageMaker Ground Truth for a labeling job in the correct order.

Question 7

A data scientist is building a recommender system using Amazon SageMaker. The dataset contains user-item interactions with implicit feedback (clicks). Which THREE evaluation metrics are appropriate for this use case?

Accepted Answer

Precision@k. Precision@k is appropriate for implicit feedback (clicks) because it measures the proportion of relevant items among the top-k recommendations, focusing on the accuracy of the ranked list. In recommender systems with implicit feedback, where only positive interactions are observed, ranking metrics like Precision@k are standard as they evaluate the quality of the top recommendations without requiring explicit ratings.

Answer

Root Mean Squared Error (RMSE)

Answer

Area Under the ROC Curve (AUC-ROC)

Question 8

A team is training a deep learning model on SageMaker using a custom Docker container. The training job fails with 'OutOfMemoryError'. The instance type is ml.p3.2xlarge with 61 GB memory. Which change should increase available memory?

Accepted Answer

Set the 'shm-size' parameter in the SageMaker training container to a larger value.. The 'OutOfMemoryError' in a SageMaker training container often stems from insufficient shared memory (/dev/shm) for data-loading workers, especially with PyTorch or TensorFlow dataloaders that use multiprocessing. Increasing the 'shm-size' parameter allocates more shared memory to the container, resolving the error without altering the model or instance type.

Answer

Reduce the batch size to use less memory.

Answer

Use SageMaker distributed data parallelism to distribute the model across multiple instances.

Answer

Mount an Amazon FSx for Lustre file system to offload data.

Question 9

A data scientist is building a recommender system using collaborative filtering. The dataset is sparse (99% missing values). Which algorithm is best suited?

Accepted Answer

Matrix Factorization (e.g., SVD). Matrix factorization (e.g., SVD) is best suited for sparse collaborative filtering because it learns latent factors that capture underlying user-item interactions, effectively handling the 99% missing values by generalizing patterns rather than relying on explicit pairwise similarities. Unlike memory-based methods, it decomposes the sparse user-item matrix into lower-dimensional representations, enabling accurate predictions even when most entries are unobserved.

Answer

Random Forest

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K-Nearest Neighbors

Answer

Hidden Markov Model

Question 10

A data scientist is using SageMaker to train a TensorFlow model. The training script uses tf.data.Dataset to load data from S3. Training is slow because of I/O bottleneck. Which change should the data scientist make to improve I/O performance?

Accepted Answer

Use Pipe input mode for the training channel.. Option B is correct because Pipe input mode streams data directly from S3 into the training algorithm without writing to disk, eliminating the I/O bottleneck caused by downloading entire files. This is particularly effective with tf.data.Dataset, as the pipeline can consume data incrementally, reducing latency and improving throughput for large datasets.

Answer

Enable EBS optimization on the training instance.

Answer

Use SageMaker local mode for training.

Answer

Convert the dataset to RecordIO format.