AWS Certified Machine Learning Specialty — Free Practice Questions

AWS Glue is the correct answer because this option requires the least amount of setup and maintenance since it is serverless, and it does not require management of the infrastructure. Refer to this link for supporting information. A, C, and D are all solutions that can solve the problem, but require more steps for configuration, and require higher operational overhead to run and maintain.

This works, but it means provisioning and paying for an EMR cluster to do what Glue does serverless. The question asks for the least operational overhead.

Data Pipeline is a legacy orchestration service, and pairing it with a self-managed Hive metastore adds two components Glue replaces with one.

Same objection as the other EMR option: a cluster you have to size, patch and pay for, where a managed crawler and ETL job would do.

Data Analytics runs SQL or Flink queries over a stream. It analyses data in flight rather than delivering it to a destination.

Kinesis Firehose is perfect for streaming data into AWS and sending it directly to its final destination - places like S3, Redshift, Elastisearch, and Splunk Instances.

Data Streams is the durable buffer that holds records for consumers to read. Getting them into S3, Redshift or OpenSearch still needs something else — that something is Firehose.

Purpose-built for video and other time-encoded media, not general-purpose delivery to data stores.

The reverse is true. The KPL adds aggregation, batching, retries and CloudWatch metrics on top of the raw API.

Throughput is not the deciding factor here. The KPL's aggregation typically gives higher throughput where it can be used at all.

Asynchrony is not the obstacle — Node.js is comfortable with async. The obstacle is that the KPL is a Java process.

The KPL must be installed as a Java application before it can be used with your Kinesis Data Streams. There are ways to process KPL serialized data within AWS Lambda, in Java, Node.js, and Python, but not if these answers mentions Lambda.

PutRecords is the right choice for the latency-sensitive stream, but using it for everything forgoes the KPL's aggregation, which is what makes high-volume ingestion efficient.

Backwards. The KPL buffers records before sending in order to aggregate them, which adds latency — exactly what mission-critical events cannot afford.

The KPL's RecordMaxBufferedTime introduces deliberate delay. Acceptable for bulk data, not for events that must arrive immediately.

The question is about sending data to Kinesis synchronously vs. asynchronously. PutRecords is a synchronous send function, so it must be used for the first event type (critical events). The Kinesis Producer Library (KPL) implements an asynchronous send function, so it can be used for the second event type. In this scenario, the reason to use the KPL over the PutRecords API call is because: KPL can incur an additional processing delay of up to RecordMaxBufferedTime within the library (user-configurable). Larger values of RecordMaxBufferedTime results in higher packing efficiencies and better performance. Applications that cannot tolerate this additional delay may need to use the AWS SDK directly. For more information about using the AWS SDK with Kinesis Data Streams, see Developing Producers Using the Amazon Kinesis Data Streams API with the AWS SDK for Java. For more information about RecordMaxBufferedTime and other user-configurable properties of the KPL, see Configuring the Kinesis Producer Library.

Visualizing the composition of our data is a great way to show what our data is made of.

A histogram is a good visualization type for displaying the single distribution of data. A scatter plot is a good visualization type for displaying multi-distribution data, as it easily shows data clusters, minimum and maximum values, and outliers. A box plot is a good visualization type for displaying multi-distribution data, as it easily shows the minimum, maximum, and mean values of data, as well as outliers.

Before we use corpus data in some Machine Learning processes like language translation, sentiment analysis, or spam filtering it is important we properly apply text processing to the data. Some of the important text processing that needs to be done is tokenization. This includes removing stop words — frequent words such as ”the”, ”is”, etc. that do not have specific meaning. Before we use corpus data in some Machine Learning processes like language translation, sentiment analysis, or spam filtering it is important we properly apply text processing to the data. Some of the important text processing that needs to be done is tokenization. This includes converting sentences to words, removing unnecessary punctuation, and removing stop words — frequent words such as ”the”, ”is”, etc. that do not have specific meaning.

Stemming is a rudimentary rule-based process of stripping the suffixes (“ing”, “ly”, “es”, “s” etc) from a word. Stop words are those words which will have no relevance to the context of the data for example is/am/are. Object Standardization is also one of the good ways to pre-process the text by removing things like acronyms, hashtags with attached words, and colloquial slang that typically are not recognized by search engines and models.