MLS-C01 Practice Test 26 — 15 Questions

Question 1

A company has a large number of small CSV files (hundreds of thousands) in an S3 bucket. A data engineer needs to run a SQL query on this data using Amazon Athena. The queries are currently slow and expensive. Which two actions will improve query performance and reduce cost?

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

Accepted Answer

Partition the data by a commonly filtered column (e.g., date).. The correct answers are C and E. Partitioning the data by a commonly filtered column (e.g., date) reduces the amount of data scanned by Athena, improving performance and cost. Converting the data to Parquet or ORC columnar format further reduces the data scanned and improves compression and query speed. Option B (gzip compression) helps reduce storage and scan volume but is less impactful than partitioning and columnar format. Option A (increasing S3 request rate) does not directly improve Athena query performance. Option D (splitting into smaller files) can increase the overhead of reading many small files, potentially hurting performance.

Answer

Increase the S3 request rate per prefix to improve read throughput.

Answer

Compress the CSV files using gzip.

Answer

Increase the number of partitions by splitting files into smaller ones.

Question 2

A company is running a data pipeline that uses Amazon EMR with Spark to process 100 TB of data daily. The pipeline must complete within 6 hours. Currently, it takes 8 hours. Which optimization will most likely reduce the runtime?

Accepted Answer

Enable EMR Managed Scaling. EMR Managed Scaling automatically adjusts the number of core and task nodes based on workload, increasing parallelism and reducing runtime for large data pipelines. This is the most effective optimization when the current cluster is under-provisioned for the 100 TB workload. Option A is incorrect because consolidating input files reduces small file overhead but does not address a compute capacity bottleneck. Option C is incorrect because increasing memory per node (e.g., r5 instances) helps memory-bound tasks, but the primary bottleneck here is likely throughput and parallelism, not memory. Option D is incorrect because Spot Instances can be interrupted, which may cause delays and increase runtime, making them unsuitable for a time-sensitive pipeline.

Answer

Consolidate small input files into fewer larger files

Answer

Increase the memory of each node by using r5 instances

Answer

Use Spot Instances for all core nodes

Question 3

A data engineer is building a data pipeline that uses Amazon S3 to store raw data, AWS Lambda for transformation, and Amazon DynamoDB for serving. The Lambda function experiences high latency when writing to DynamoDB. Which action will most effectively reduce the latency?

Accepted Answer

Increase the DynamoDB write capacity units. Increasing write capacity units for DynamoDB directly reduces write latency by minimizing throttling. DAX (Option A) is a read cache and does not improve write latency. Option B is incorrect because using S3 would increase latency due to its different access pattern. Option C is incorrect because VPC gateway endpoint improves network connectivity but does not reduce write latency.

Answer

Enable DynamoDB Accelerator (DAX) for caching

Answer

Use Amazon S3 instead of DynamoDB

Answer

Configure a VPC gateway endpoint for DynamoDB

Question 4

A company needs to move 10 TB of data from an on-premises NAS to Amazon S3 over a 100 Mbps internet connection. The transfer must complete within 3 days. Which solution is the most appropriate?

Accepted Answer

Use AWS Snowball Edge to transfer the data. AWS Snowball Edge is a physical device that can transfer large data volumes much faster than over the internet. Option A is wrong: AWS DataSync over the internet at 100 Mbps would take approximately 10 days to transfer 10 TB, exceeding the 3-day requirement. Option B is wrong: S3 Transfer Acceleration optimizes the network path but still relies on internet bandwidth; even with a 200% speed improvement, it would take over 4.6 days. Option C is wrong: using AWS CLI to copy directly over the internet has the same bandwidth limitation and would also take about 10 days.

Answer

Use AWS DataSync to transfer over the internet

Answer

Enable S3 Transfer Acceleration on the bucket

Answer

Use AWS CLI to copy data directly over the internet

Question 5

A data engineer runs the AWS CLI command shown and notices a zero-byte file in the results. What is the most likely cause of this zero-byte file?

Accepted Answer

The file was created by a failed Spark task that wrote no data.. Zero-byte files often occur when an ETL job fails partway through writing, or when a task starts but writes no data. A completed write would have non-zero size. The other options are less likely: prefix typo wouldn't produce a file; correct permissions wouldn't cause zero bytes; compression would produce some output.

Answer

The S3 bucket has a lifecycle policy that deleted the content.

Answer

The file was written with the wrong prefix.

Answer

The file was compressed, reducing size to zero.

Question 6

A company is building a data pipeline that ingests data from on-premises databases into Amazon S3 using AWS Database Migration Service (AWS DMS). The company wants to capture continuous changes from the source database and replicate them to S3 in near-real time. Which AWS DMS configuration should the company use?

Accepted Answer

Create a CDC-only task with S3 as the target endpoint. Using a CDC-only task with S3 as the target endpoint replicates continuous changes to S3. Option A is wrong because a full-load task only migrates existing data. Option B is wrong because a full-load plus CDC task includes both, but the requirement is only changes. Option C is wrong because a validation task is for data validation, not replication.

Answer

Create a full-load task to copy the existing data

Answer

Create a full-load plus CDC task with S3 target

Answer

Create a validation task to compare source and target

Question 7

A company is streaming e-commerce events to Amazon Kinesis Data Streams. The data science team needs to join events from multiple shards in near real-time and then store the joined results in Amazon S3. Which solution would meet these requirements with the LEAST operational overhead?

Accepted Answer

Use Amazon Kinesis Data Analytics for Apache Flink to read from the Kinesis stream, perform a join operation using Flink SQL, and write the results to S3 using a sink connector.. Amazon Kinesis Data Analytics for Apache Flink can read from a Kinesis stream, perform stateful joins across shards using Flink SQL or the DataStream API, and write the results to Amazon S3 via a sink connector, all with minimal operational overhead. Option A is wrong because AWS Lambda functions process each shard independently; joining across shards would require managing external state (e.g., DynamoDB), increasing complexity and latency. Option B is wrong because Amazon Kinesis Data Firehose buffers data and writes to S3, but it cannot perform joins; using Athena to join after storage introduces batch-like delays. Option C is wrong because AWS Glue ETL jobs are batch-oriented and not designed for near real-time streaming; Glue Streaming ETL would still require significant configuration and is less optimized for stateful joins across shards.

Answer

Use AWS Lambda functions with Kinesis triggers to process each record, join across shards using a DynamoDB table for state, and write to S3.

Answer

Use Amazon Kinesis Data Firehose to buffer the data and write to S3, then use Amazon Athena to join the data after it is stored.

Answer

Use AWS Glue ETL jobs that read from the Kinesis stream via the Kinesis connector and write the joined results to S3.

Question 8

A company is using SageMaker to deploy a model for real-time inference. The model requires GPU for low latency. Which THREE configurations should the company consider for high availability and cost optimization? (Choose three.)

Accepted Answer

Use a multi-model endpoint to share GPU instances among multiple models.. A multi-model endpoint allows multiple models to be hosted on the same GPU-backed instance, sharing the GPU resources and reducing idle time. This improves cost efficiency by maximizing GPU utilization while still providing low-latency inference for each model. It is a recommended pattern for serving many models with GPU requirements without provisioning separate endpoints.

Answer

Use Spot instances for the endpoint.

Answer

Use SageMaker Batch Transform for inference.

Question 9

A data scientist is training a Random Forest model on Amazon SageMaker. The model performs well on the training set but poorly on the test set. Which technique should the data scientist use to address this issue?

Accepted Answer

Decrease the maximum depth of each tree. The model is overfitting, as indicated by high training performance and poor test performance. Decreasing the maximum depth of each tree limits the complexity of individual trees, reducing overfitting by preventing them from memorizing noise in the training data. This is a standard regularization technique for Random Forest models in Amazon SageMaker.

Answer

Increase the number of trees in the forest

Answer

Increase the learning rate

Answer

Increase the maximum depth of each tree

Question 10

Refer to the exhibit. An ML engineer creates a CloudFormation stack with this template. The stack creation succeeds, but when the engineer tries to invoke the endpoint, it returns a ModelError. The CloudWatch logs show that the container exited with error. What is the MOST likely cause?

Accepted Answer

The execution role does not have permissions to pull the Docker image from ECR.. The CloudFormation template likely does not grant the SageMaker execution role the necessary `ecr:GetDownloadUrlForLayer` and `ecr:BatchGetImage` permissions to pull the container image from Amazon ECR. Without these permissions, the SageMaker service cannot download the Docker image to the ML compute instances, causing the container to fail with a ModelError and exit error in CloudWatch logs.

Answer

The initial instance count is set to 2, which is insufficient for the model size.

Answer

The endpoint is not deployed in a VPC and cannot access the S3 bucket.

Answer

The EndpointConfig references the model but the model is not yet created.

Question 11

A data scientist is tuning a gradient boosting model using Amazon SageMaker Automatic Model Tuning (AMT). Which THREE hyperparameters should the scientist consider tuning to reduce overfitting? (Select THREE.)

Accepted Answer

Learning rate (eta). Learning rate (eta) controls the contribution of each tree to the ensemble. A lower learning rate forces the model to learn more slowly, requiring more trees but reducing the risk of overfitting by preventing any single tree from having too much influence on the final prediction.

Answer

Subsample ratio

Answer

Maximum depth (max_depth)

Question 12

A data scientist is performing exploratory data analysis on text data. They want to identify the most common terms and their frequencies. Which approach should they use?

Accepted Answer

Create a bag-of-words matrix and compute term frequencies.. A bag-of-words matrix counts the frequency of each term in the text, directly providing the most common terms and their frequencies. Option A is incorrect because sentiment analysis determines the emotional tone of text, not term frequencies. Option B is incorrect because Latent Dirichlet Allocation (LDA) is a topic modeling technique that assigns topics to documents, not term frequencies. Option D is incorrect because word2vec generates dense vector embeddings that capture semantic relationships, not raw term frequencies.

Answer

Perform sentiment analysis on the text.

Answer

Apply Latent Dirichlet Allocation (LDA) to extract topics.

Answer

Use word2vec to generate word embeddings.

Question 13

Which THREE of the following are appropriate data visualization techniques for exploring the relationship between two numerical variables?

Accepted Answer

Scatter plot. Scatter plot, hexbin plot, and pair plot are designed for bivariate numerical relationships. Bar chart is for categorical. Box plot is for numerical vs categorical.

Answer

Box plot

Answer

Bar chart

Question 14

A data scientist is analyzing a dataset with missing values. Which technique is most appropriate for imputing missing values in a numerical feature that follows a normal distribution?

Accepted Answer

Mean imputation. Mean imputation is suitable for normally distributed data as it preserves the mean. Median is robust to outliers, not normality. Mode is for categorical data. Standard deviation is not an imputation method. KNN imputation is non-parametric.

Answer

Standard deviation imputation

Answer

Mode imputation

Answer

Median imputation

Question 15

An Athena query SELECT COUNT(*) FROM table WHERE col1 IS NULL returns the value 5000. What does this value represent?

Accepted Answer

The number of rows where col1 is NULL. The exhibit shows the result of an Athena query that counts the number of rows where col1 is NULL. The value 5000 is that count. Therefore, Option B is correct. Option A is incorrect because the query does not count total rows; it filters for NULLs. Option C is incorrect because the query counts NULL rows, not non-NULL. Option D is incorrect because the query does not use DISTINCT to count distinct values.

Answer

The total number of rows in the table

Answer

The number of rows where col1 is not NULL

Answer

The number of distinct values in col1