MLS-C01 Data Engineering Practice Test 18 — 15 Questions

Question 1

A company needs to build a data lake on AWS for analytics. The data includes structured, semi-structured, and unstructured data. The solution must support schema-on-read, provide fine-grained access control, and be cost-effective for storing rarely accessed data. Which THREE services should be used? (Choose THREE)

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

Accepted Answer

AWS Glue Data Catalog for schema-on-read.. Amazon S3 (C) is the correct primary storage layer for a data lake because it natively stores structured, semi-structured, and unstructured data as objects, decoupling storage from compute and enabling schema-on-read through query engines like Athena and Redshift Spectrum. AWS Glue Data Catalog (A) is correct because it provides the centralized metadata repository that defines table schemas and partitions at read time, which is exactly what schema-on-read requires, and it integrates with Lake Formation for fine-grained access control at the table, column, and row level. S3 Lifecycle policies transitioning data to Glacier (E) are correct because they automatically move rarely accessed objects to lower-cost archival storage classes such as Glacier Instant Retrieval, Flexible Retrieval, or Deep Archive, satisfying the cost-effectiveness requirement. Amazon Redshift (B) is not appropriate as the foundational layer here because it is a provisioned data warehouse optimized for structured, schema-on-write workloads rather than a multi-format data lake store. Amazon EMR (D) is a valid processing option but is not required by the stated requirements, since schema-on-read, fine-grained access control, and low-cost archival storage are delivered by the S3, Glue, and lifecycle combination.

Answer

Amazon Redshift for data warehousing.

Answer

Amazon EMR for data processing.

Question 2

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

Accepted Answer

First, open Amazon SageMaker console. Next, select 'Notebook instances' from the left navigation. Then, click 'Create notebook instance'. After that, configure the notebook instance settings (name, instance type, IAM role, VPC). Finally, click 'Create notebook instance'.. Creating a notebook instance requires navigating the SageMaker console, configuring instance settings, IAM role, and VPC, then launching.

Answer

First, open Amazon SageMaker console. Then, click 'Create notebook instance' immediately. Next, configure the notebook instance settings. Finally, select 'Notebook instances' from the navigation.

Answer

First, configure the notebook instance settings (name, instance type, IAM role, VPC). Then, open Amazon SageMaker console. Next, click 'Create notebook instance'. Finally, create the notebook instance.

Answer

First, click 'Create notebook instance'. Then, open Amazon SageMaker console. Next, select 'Notebook instances'. Finally, configure settings and create.

Question 3

Drag and drop the steps to perform hyperparameter tuning using SageMaker Automatic Model Tuning in the correct order.

Accepted Answer

Define the objective metric, then define the hyperparameter search space, then configure resource limits, then create the tuning job, then monitor and select the best model.. Tuning involves defining search space, creating a tuning job, setting limits, executing, and selecting best model.

Answer

Define the hyperparameter search space, then configure resource limits, then define the objective metric, then create the tuning job, then monitor and select the best model.

Answer

Select the best model, then define the objective metric, then define the hyperparameter search space, then configure resource limits, then create the tuning job.

Answer

Create the tuning job, then define the objective metric, then define the hyperparameter search space, then configure resource limits, then monitor and select the best model.

Question 4

Drag and drop the steps to set up cross-validation in a SageMaker training job using the built-in XGBoost algorithm in the correct order.

Accepted Answer

Split the data into training and validation folds, then configure the SageMaker estimator with cross-validation parameters (e.g., num_round, eval_metric, num_cv_folds), then execute the training job (fit), then select the best model based on average CV metrics.. Cross-validation requires data splitting, job configuration with CV parameters, execution, and model selection.

Answer

Configure the SageMaker estimator with cross-validation parameters, then split the data into folds, then execute the training job, then select the best model.

Answer

Split the data into folds, then execute the training job, then configure the estimator, then select the best model.

Answer

Execute the training job, then split the data into folds, then configure the estimator, then select the best model.

Question 5

Match each AWS security service to its function in ML.

Accepted Answer

AWS KMS: Manages encryption keys for securing ML model data at rest and in transit. In ML, AWS KMS handles encryption keys, IAM manages access, CloudTrail provides audit logs. Common confusions arise because these services often work together, but each has a distinct primary function.

Answer

AWS KMS: Provides identity-based access control for ML resources

Answer

AWS IAM: Encrypts data during ML processing

Answer

AWS CloudTrail: Monitors and enforces compliance rules for ML

Question 6

Match each SageMaker optimization technique to its description.

Accepted Answer

Managed Spot Training: Uses EC2 Spot instances to reduce training cost. Managed Spot Training reduces cost via spot instances; Data Parallelism speeds training by distributing data; Automatic Model Tuning finds optimal hyperparameters; Compilation (Neo) optimizes for inference hardware. Common confusions include swapping definitions between these techniques.

Answer

Managed Spot Training: Optimizes model for inference on edge devices

Answer

SageMaker Data Parallelism: Automatically searches hyperparameter space to optimize model

Question 7

A data engineering team needs to process streaming data from thousands of IoT devices. They want to aggregate data in 1-minute windows and store results in an S3 data lake for downstream analytics. Which architecture should they use?

Accepted Answer

Use Kinesis Data Analytics for SQL-based windowed aggregations and send results to Kinesis Data Firehose for delivery to S3.. Kinesis Data Analytics for SQL Applications is purpose-built for real-time windowed aggregations on streaming data, such as 1-minute tumbling windows. It can directly consume from Kinesis Data Streams, perform the aggregation using standard SQL, and output the results to Kinesis Data Firehose, which reliably delivers the aggregated data to an S3 data lake with built-in buffering and compression.

Answer

Use AWS Glue ETL jobs running in streaming mode to read from Kinesis Data Streams, apply window aggregations, and write to S3.

Answer

Use Kinesis Data Streams with enhanced fan-out and multiple consumers to aggregate windows, then write to S3 via Firehose.

Answer

Use Kinesis Data Streams, trigger a Lambda function for 1-minute window aggregation using Python, and write results to S3.

Question 8

A data engineer needs to transform raw clickstream data (JSON files) stored in S3 into a partitioned Parquet dataset for querying with Athena. The transformation includes cleaning, deduplication, and enrichment. The pipeline should run daily. Which solution is MOST cost-effective and requires the least operational overhead?

Accepted Answer

Use an AWS Glue ETL job with a schedule trigger to perform the transformation and write to S3.. AWS Glue ETL jobs are serverless, require no cluster management, and can be easily scheduled for daily runs. Glue also integrates with the Data Catalog for partitioning. Option A (Amazon EMR) is not the most cost-effective or least operational overhead because it requires managing a cluster, even if it can be terminated after completion. Option C (AWS Lambda) is not suitable for large-scale clickstream data due to execution time limits and lack of built-in support for complex transformations like deduplication. Option D (Amazon Athena CTAS) is not appropriate because Athena is primarily a query engine, not a transformation tool; CTAS queries are good for converting data formats but lack the flexibility for cleaning and enrichment.

Answer

Launch an Amazon EMR cluster with Spark, transform the data, and terminate the cluster after completion.

Answer

Use AWS Lambda functions triggered by S3 events to transform each file incrementally.

Answer

Use Amazon Athena to run CTAS queries to convert and partition the data daily.

Question 9

A company is building a near-real-time dashboard using data from multiple sources. They need to aggregate millions of events per second with sub-second latency. The architecture must be fully managed and minimize operational overhead. Which service should they use for the aggregation layer?

Accepted Answer

Amazon Kinesis Data Analytics for Apache Flink.. Amazon Kinesis Data Analytics for Apache Flink is the correct choice because it provides a fully managed, serverless runtime for Apache Flink, which is designed for stateful stream processing at scale. It can aggregate millions of events per second with sub-second latency using exactly-once semantics and built-in checkpointing, meeting the near-real-time dashboard requirements without any infrastructure management.

Answer

AWS Lambda functions triggered by Kinesis Data Streams.

Answer

Amazon EMR with Spark Streaming.

Answer

Amazon Redshift with materialized views refreshed frequently.

Question 10

A data engineer needs to set up a data lake on S3 that supports both batch and streaming ingestion. The data must be queryable by Athena, Redshift Spectrum, and EMR. Which TWO configurations are essential? (Choose two.)

Accepted Answer

Store data in columnar formats like Parquet or ORC.. Columnar formats like Parquet and ORC are optimized for analytical queries, reducing I/O by reading only the necessary columns. This is essential for Athena, Redshift Spectrum, and EMR, which all benefit from the efficient compression and predicate pushdown capabilities of these formats, enabling faster query performance and lower costs.

Answer

Enable S3 Select on the target buckets.

Answer

Enable S3 versioning on all buckets.

Answer

Set up Kinesis Data Firehose for streaming ingestion.

Question 11

A team wants to move data from an on-premises Oracle database to Amazon S3 for analytics. The pipeline must run daily and handle incremental updates. Which THREE services should they use together? (Choose three.)

Accepted Answer

Amazon S3. Amazon S3 is the correct destination for storing the data because it provides a scalable, durable, and cost-effective object storage solution ideal for analytics workloads. The pipeline requires daily incremental updates, and S3 integrates seamlessly with AWS DMS for continuous replication and AWS Glue for ETL processing, making it the central storage layer for the analytics pipeline.

Answer

Amazon SageMaker

Answer

Amazon Athena

Question 12

An IAM policy attached to a SageMaker notebook role is shown. The data engineer tries to run an Athena query on a table in the 'my_database' Glue database. The query fails with an access denied error. What is the MOST likely cause?

Accepted Answer

The policy does not allow athena:StartQueryExecution on the Athena workgroup.. The IAM policy does not include the `athena:StartQueryExecution` action on the Athena workgroup, which is required to submit a query. Even if the role has permissions for Glue and S3, Athena will deny the request if the workgroup-level permission to start queries is missing, resulting in an access denied error.

Answer

The policy does not allow s3:PutObject on the query results location.

Answer

The policy does not allow glue:GetTable on the specific database.

Answer

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

Question 13

A data engineer runs the AWS CLI command above to inspect a file in S3. They need to determine if the file was modified after a Glue ETL job processed it. What additional information could they obtain from this command?

Accepted Answer

The object's last modified timestamp.. The AWS CLI command `aws s3api head-object --bucket my-bucket --key my-key` returns metadata about an S3 object without downloading it. The `LastModified` field in the response provides the exact timestamp of the last modification, which can be compared to the Glue ETL job's execution time to determine if the file was modified after processing. This is the only field that directly answers the question about modification timing.

Answer

The object's content type.

Answer

The object's storage class.

Answer

The object's ETag.

Question 14

A data engineer created a CloudFormation template for a Glue ETL job as shown. The job processes 500 GB of data and takes 90 minutes to complete. However, the job fails after 60 minutes. What is the MOST likely cause?

Accepted Answer

The Timeout property is set to 60 minutes, but the job requires more time.. The Glue ETL job has a `Timeout` property set to 60 minutes, but the job requires 90 minutes to complete. When the timeout is reached, AWS Glue forcibly terminates the job, causing it to fail. This is the most direct cause of the failure after exactly 60 minutes of execution.

Answer

The IAM role does not have sufficient permissions.

Answer

The ScriptLocation S3 bucket is in a different region.

Answer

The MaxRetries property is set to 0, so the job does not retry on failure.

Question 15

A data science team needs to process streaming data from thousands of IoT devices and perform real-time anomaly detection. The data must be persisted in Amazon S3 for batch processing later. Which combination of AWS services should be used to meet these requirements?

Accepted Answer

Amazon Kinesis Data Streams for ingestion, Amazon Kinesis Data Analytics for anomaly detection, and Amazon Kinesis Data Firehose to deliver data to Amazon S3.. Amazon Kinesis Data Streams provides durable, real-time ingestion for high-throughput IoT data. Kinesis Data Analytics can perform SQL-based anomaly detection on the stream, and Kinesis Data Firehose reliably delivers the processed or raw data to Amazon S3 for batch processing. This combination meets all requirements for streaming ingestion, real-time analytics, and persistent storage.

Answer

Amazon Kinesis Data Streams for ingestion, AWS Glue for anomaly detection, and Amazon S3 for storage.

Answer

AWS Lambda for both ingestion and anomaly detection, and Amazon S3 for storage.

Answer

Amazon Simple Queue Service (SQS) for ingestion, AWS Lambda for anomaly detection, and Amazon S3 for storage.