MLS-C01 Practice Test 43 — 15 Questions

Question 1

A company is designing a data pipeline to analyze customer behavior. The pipeline must handle real-time streaming data and batch data. The data must be stored in a data lake on Amazon S3 and also made available for interactive queries. Which THREE services should be combined to build this pipeline? (Choose THREE.)

Accepted Answer

Amazon Kinesis Data Streams. Amazon Kinesis Data Streams ingests real-time data. AWS Glue can perform ETL and catalog the data. Amazon Athena allows interactive querying on S3. Options B, D, and E are not needed or redundant.

Answer

Amazon Redshift

Answer

Amazon DynamoDB Streams

Question 2

A data engineering team is migrating on-premises Hadoop workloads to AWS. The workloads include batch processing using Apache Spark and interactive SQL queries. The data is stored in HDFS. Which TWO AWS services should be used to replace HDFS and provide a scalable, durable storage layer? (Choose TWO.)

Accepted Answer

Amazon EMR with EMRFS. Amazon S3 is the primary storage for data lakes, replacing HDFS. Amazon EMR with EMRFS can access S3 as if it were HDFS. Option B (Amazon FSx for Lustre) is a high-performance file system but not a direct HDFS replacement for durability. Option C (Amazon EBS) is block storage, not scalable. Option D (Amazon RDS) is relational database. Option E (Amazon DynamoDB) is NoSQL.

Answer

Amazon EBS

Answer

Amazon FSx for Lustre

Answer

Amazon RDS

Question 3

A data engineering team needs to ingest streaming data from thousands of IoT devices into a data lake on Amazon S3 for near-real-time analytics. The data must be partitioned by device ID and timestamp, and the team must minimize data loss during ingestion failures. Which solution is MOST appropriate?

Accepted Answer

Use Amazon Kinesis Data Firehose to write directly to S3 with dynamic partitioning.. Option B is correct because Kinesis Data Firehose can directly write to S3 with partitioning and automatic retries, minimizing data loss. Option A is wrong because Kinesis Data Streams requires a separate consumer to write to S3, adding complexity. Option C is wrong because Lambda has a 15-minute limit and may lose data if the function fails. Option D is wrong because S3 Transfer Acceleration is for speeding up uploads, not for streaming ingestion.

Answer

Use Amazon Kinesis Data Streams with a Lambda function that writes to S3.

Answer

Use Amazon S3 Transfer Acceleration with direct uploads from devices.

Answer

Use AWS Lambda to receive data via API Gateway and write to S3.

Question 4

A company runs a real-time fraud detection pipeline using Amazon Kinesis Data Analytics. The pipeline reads from a Kinesis data stream, performs sliding window aggregations, and writes results to a DynamoDB table. The application is experiencing high latency during peak hours. Which action would MOST effectively reduce latency?

Accepted Answer

Increase the number of shards in the Kinesis data stream.. Option A is correct because increasing the number of shards increases parallelism in both the stream and the Kinesis Data Analytics application. Option B is wrong because DynamoDB auto scaling helps with writes but not streaming latency. Option C is wrong because a larger window increases latency. Option D is wrong because decreasing parallelism reduces throughput.

Answer

Enable DynamoDB auto scaling to handle write spikes.

Answer

Decrease the parallelism level in the Kinesis Data Analytics application.

Answer

Increase the sliding window size to reduce computational frequency.

Question 5

Refer to the exhibit. A data engineer examines the output of 'aws glue get-job-run' for a failed job. The job run state is FAILED, but ErrorMessage is empty. The job ran for 3600 seconds (1 hour) before failing. What is the MOST likely cause of the failure?

Accepted Answer

The job ran out of memory due to insufficient DPU allocation.. Option D is correct because the job ran for exactly 1 hour and then failed with no error message, indicating it hit the timeout. The default timeout is 2880 minutes (48 hours), but the job may have a custom timeout set. However, the exhibit shows Timeout: 2880 minutes, which is not hit. Wait, ExecutionTime: 3600 seconds = 60 minutes. The Timeout is 2880 minutes, so not timeout. Another common cause is out-of-memory. But the question states 'empty error message' - often Glue jobs fail silently due to resource constraints like memory. Option D: The job ran out of memory because MaxCapacity is 10 DPUs, which may be insufficient for the data size. Option A is wrong because permissions would show Access Denied error. Option B is wrong because Python errors would appear in logs. Option C is wrong because no error message suggests it's not a connection timeout.

Answer

The JDBC connection to the source database timed out.

Answer

The IAM role does not have sufficient permissions to access S3.

Answer

The Python script has a syntax error.

Question 6

A machine learning team is preparing a dataset for model training. The data is stored in an Amazon S3 bucket with objects that are each approximately 100 MB in size. The team wants to use Amazon SageMaker for training. To optimize training performance, which data format and storage configuration should be used?

Accepted Answer

Store data as RecordIO-Protobuf files and use SageMaker Pipe input mode. Option B is correct because SageMaker Pipe input mode streams data directly from S3, avoiding disk I/O, and RecordIO-Protobuf is an optimized binary format. Option A is wrong because File mode copies data to disk, increasing latency. Option C is wrong because CSV is not as efficient as binary. Option D is wrong because File mode with CSV is not optimal.

Answer

Store data as RecordIO-Protobuf files and use SageMaker File input mode

Answer

Store data as CSV files and use SageMaker Pipe input mode

Answer

Store data as CSV files and use SageMaker File input mode

Question 7

A data scientist needs to run a one-time training job on a 5 TB dataset stored in Amazon S3. The training algorithm requires random access to individual records. Which SageMaker input mode and data format combination would be MOST appropriate?

Accepted Answer

Use File mode with RecordIO-Protobuf format. Option C is correct because File mode downloads the entire dataset to the instance's local disk, enabling random access to any record. Pipe mode does not support random access. Option A is wrong because Pipe mode streams data sequentially. Option B is wrong because Pipe mode does not support random access. Option D is wrong because Pipe mode is not suitable for random access.

Answer

Use Pipe mode with Parquet format

Answer

Use Pipe mode with RecordIO-Protobuf format

Answer

Use Pipe mode with CSV format

Question 8

An IAM policy is attached to a data engineering role that writes to an S3 bucket. The policy is shown in the exhibit. What is the effect of this policy?

Accepted Answer

The role must use SSE-KMS when writing objects; reading is allowed only if the object is encrypted with SSE-KMS. Option B is correct because the first statement allows GetObject and PutObject only when SSE-KMS is used; the second statement denies PutObject if SSE-KMS is not used, effectively enforcing SSE-KMS for PutObject. Option A is wrong because the policy allows PutObject with SSE-KMS. Option C is wrong because GetObject is allowed with SSE-KMS. Option D is wrong because the policy never denies GetObject.

Answer

The role can write objects with any encryption, but reading is restricted to SSE-KMS only

Answer

The role can read and write any object without encryption restrictions

Answer

The role can only read objects; writing is always denied

Question 9

A company uses AWS Glue ETL jobs to process data from an Amazon RDS for MySQL database into Amazon S3. The job runs daily and takes 6 hours to complete. The team wants to reduce runtime and cost. The source table has 50 million rows and is updated continuously. Which combination of changes would be MOST effective?

Accepted Answer

Use JDBC connections with pushdown predicates and increase the number of DPUs.. Option B is correct because JDBC connections with pushdown predicates reduce data transferred, and increasing DPUs can parallelize processing. Option A is wrong because increasing DPUs without pushdown may cause bottleneck on source. Option C is wrong because a single worker cannot process 50M rows quickly. Option D is wrong because triggers do not optimize runtime.

Answer

Use a single worker with a larger instance type.

Answer

Increase the number of DPUs and enable job bookmarking.

Answer

Change the job trigger from time-based to event-based.

Question 10

A company wants to use Amazon SageMaker to train a model on a dataset stored in Amazon S3. The dataset is 100 GB and consists of millions of small JSON files. What should the data engineering team do to optimize training performance?

Accepted Answer

Combine the small JSON files into larger Parquet files using a Spark job on Amazon EMR.. Option D is correct because combining small files into larger ones reduces S3 LIST overhead and improves I/O performance. Option A is wrong because Athena is not needed for training. Option B is wrong because EBS is ephemeral and not shared across instances. Option C is wrong because S3 Select is for server-side filtering, not for training performance.

Answer

Copy the data to an Amazon EBS volume attached to the training instance.

Answer

Use Amazon Athena to convert the data into a single CSV file.

Answer

Use S3 Select to filter data before training.

Question 11

A financial services company needs to build a data lake on Amazon S3 that meets regulatory requirements for data retention and encryption. Data must be encrypted at rest and in transit, and access must be audited. The data lake will be queried by Amazon Athena and Amazon Redshift Spectrum. Which combination of actions should be taken?

Accepted Answer

Enable S3 default encryption with SSE-KMS and enable AWS CloudTrail for S3 data events.. Option D is correct because server-side encryption with KMS (SSE-KMS) provides encryption at rest, and CloudTrail logs S3 API calls for auditing. Option A is wrong because SSE-S3 does not provide key management control. Option B is wrong because SSL/TLS is for in-transit encryption, not at rest. Option C is wrong because IAM does not provide encryption.

Answer

Use IAM policies to control access and enable S3 server access logging.

Answer

Use SSL/TLS for all connections and enable S3 versioning.

Answer

Enable S3 default encryption with SSE-S3 and use S3 access logs.

Question 12

A company uses AWS Glue Data Catalog to manage metadata for its data lake on Amazon S3. The data lake contains terabytes of data in CSV format. The data engineering team wants to improve query performance in Amazon Athena and reduce costs. Which actions should the team take? (Select THREE.)

Accepted Answer

Compress the data using Snappy or GZIP.. Option B is correct because converting to columnar formats like Parquet reduces data scanned and improves performance. Option C is correct because partitioning limits the amount of data scanned per query. Option D is correct because compressing data reduces storage cost and data scanned. Option A is wrong because CSV is not efficient. Option E is wrong because it does not directly address performance or cost for Athena.

Answer

Create views in Athena to simplify queries.

Answer

Convert the data to JSON format.

Question 13

A research lab stores large genomic datasets in Amazon S3 Glacier Deep Archive. They need to run a one-time analysis on a subset of 10 PB of data. The analysis will use an Amazon EMR cluster with Amazon S3 as the data source. What is the MOST cost-effective and performant way to make the data available for the EMR cluster?

Accepted Answer

Initiate a Bulk retrieval request and restore the data to S3 Standard for the duration of the analysis. Option D is correct because Bulk retrieval is the cheapest retrieval tier for Glacier Deep Archive, and restoring to S3 Standard allows the EMR cluster to read efficiently. Option A is wrong because reading directly from Glacier is not supported by EMR. Option B is wrong because Expedited is more expensive and not needed for a one-time batch job. Option C is wrong because restoring to S3 Standard-IA adds retrieval costs for data that is accessed once.

Answer

Restore the data to S3 Standard-IA and delete after the analysis

Answer

Configure the EMR cluster to read directly from Glacier Deep Archive using S3 Console

Answer

Initiate an Expedited retrieval request and use the temporary copy for the EMR cluster

Question 14

A company is building a data lake on Amazon S3 and wants to ensure that data is encrypted at rest using AWS KMS. Which TWO actions are required to achieve this? (Choose TWO.)

Accepted Answer

Configure the KMS key policy to allow the S3 service to use the key. Option B is correct because default bucket encryption with SSE-KMS ensures all objects are encrypted with KMS. Option D is correct because KMS key policy must allow the bucket to use the key. Option A is wrong because bucket policy can restrict access but does not enforce encryption. Option C is wrong because client-side encryption is not required if server-side encryption is used. Option E is wrong because encryption in transit (HTTPS) is separate from at-rest encryption.

Answer

Add a bucket policy that denies PutObject without encryption

Answer

Enable encryption in transit using HTTPS for all S3 API calls

Answer

Use client-side encryption on all data before uploading

Question 15

A company runs a data lake on Amazon S3 with partitions by year/month/day. A machine learning team needs to read daily data from the last 30 days for model retraining. The data format is Parquet. The team uses Amazon Athena to query the data, but the queries are slow and scanning too much data. The team has already optimized the file sizes and compression. What additional step can reduce the amount of data scanned?

Accepted Answer

Add more partition columns such as hour to reduce the scanned partitions.. Option A is correct because partitioning on additional columns like hour can further prune partitions if queries filter by time ranges. Option B is wrong because converting to JSON would increase data size. Option C is wrong because converting to CSV would also increase size. Option D is wrong because removing partitions would increase scanning.

Answer

Remove the partition structure and store data as single large files.

Answer

Convert the Parquet files to JSON format for better query performance.

Answer

Use CSV format with Gzip compression.