Pass the Amazon Web Services AWS Certified Data Engineer Data-Engineer-Associate Questions and answers with ExamsMirror

Amazon Redshift is a fully managed, petabyte-scale data warehouse service that enables fast and cost-effective analysis of large volumes of data. Amazon Redshift uses columnar storage, compression, and zone maps to optimize the storage and performance of data. However, over time, as data is inserted, updated, or deleted, the physical storage of data can become fragmented, resulting in wasted disk space and degraded query performance. To address this issue, Amazon Redshift provides the VACUUM command, which reclaims disk space and resorts rows in either a specified table or all tables in the current schema1.

The VACUUM command has four options: FULL, DELETE ONLY, SORT ONLY, and REINDEX. The option that best meets the requirements of the question is VACUUM REINDEX, which re-sorts the rows in a table that has an interleaved sort key and rewrites the table to a new location on disk. An interleaved sort key is a type of sort key that gives equal weight to each column in the sort key, and stores the rows in a way that optimizes the performance of queries that filter by multiple columns in the sort key. However, as data is added or changed, the interleaved sort order can become skewed, resulting in suboptimal query performance. The VACUUM REINDEX option restores the optimal interleaved sort order and reclaims disk space by removing deleted rows. This option also analyzes the sort key column and updates the table statistics, which are used by the query optimizer to generate the most efficient query execution plan23.

The other options are not optimal for the following reasons:

A. VACUUM FULL Orders. This option reclaims disk space by removing deleted rows and resorts the entire table. However, this option is not suitable for tables that have an interleaved sort key, as it does not restore the optimal interleaved sort order. Moreover, this option is the most resource-intensive and time-consuming, as it rewrites the entire table to a new location on disk.

B. VACUUM DELETE ONLY Orders. This option reclaims disk space by removing deleted rows, but does not resort the table. This option is not suitable for tables that have any sort key, as it does not improve the query performance by restoring the sort order. Moreover, this option does not analyze the sort key column and update the table statistics.

D. VACUUM SORT ONLY Orders. This option resorts the entire table, but does not reclaim disk space by removing deleted rows. This option is not suitable for tables that have an interleaved sort key, as it does not restore the optimal interleaved sort order. Moreover, this option does not analyze the sort key column and update the table statistics.

Creating an EventBridge rule that triggers a Lambda function on AWS Glue job failure events and then sends notifications via Amazon SNS is the most direct and operationally efficient method:

“Practice Quiz 10: A data engineer must monitor the data pipeline... Which solution will meet these requirements?

A. Inspect the job run monitoring section of the AWS Glue console.

Correct Answer: A.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Although this reference directly supports using AWS Glue’s monitoring features via EventBridge, it implies that solutions like A—which directly use EventBridge failure events for automation—are more optimal and less complex than constructing custom logs and metrics.

AWS Glue ETL is designed for scalable and serverless data processing, and it supports integrated quality enforcement using AWS Glue Data Quality, which makes it the most cost-effective and integrated option when combined with Amazon RDS for MySQL as the relational database.

“AWS Glue can perform data validation as part of the ETL process, ensuring data quality before storing the data in the target data store.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Using AWS Glue Data Quality directly in the ETL workflow is simpler and more cost-effective than separating transformation (Glue) and validation (DataBrew) into different services.

For exactly-once delivery and processing in Amazon Kinesis Data Streams, the best approach is to design the application so that it handles idempotency. By embedding a unique ID in each record, the application can identify and remove duplicate records during processing.

Exactly-Once Processing:

Kinesis Data Streams does not natively support exactly-once processing. Therefore, idempotency should be designed into the application, ensuring that each record has a unique identifier so that the same event is processed only once, even if it is ingested multiple times.

This pattern is widely used for achieving exactly-once semantics in distributed systems.

Lake Formation requires that S3 locations be registered before they can be accessed by AWS Glue crawlers, jobs, or queries. Registering the location associates it with the Lake Formation permissions framework.

“Before AWS Glue crawlers can access an S3 path managed by Lake Formation, the S3 location must be registered in Lake Formation.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

 Amazon Redshift Spectrum is a feature that allows you to run SQL queries directly against data in Amazon S3, without loading or transforming the data. Redshift Spectrum can query various data formats, such as CSV, JSON, ORC, Avro, and Parquet. However, not all data formats are equally efficient for querying. Some data formats, such as CSV and JSON, are row-oriented, meaning that they store data as a sequence of records, each with the same fields. Row-oriented formats are suitable for loading and exporting data, but they are not optimal for analytical queries that often access only a subset of columns. Row-oriented formats also do not support compression or encoding techniques that can reduce the data size and improve the query performance.

On the other hand, some data formats, such as ORC and Parquet, are column-oriented, meaning that they store data as a collection of columns, each with a specific data type. Column-oriented formats are ideal for analytical queries that often filter, aggregate, or join data by columns. Column-oriented formats also support compression and encoding techniques that can reduce the data size and improve the query performance. For example, Parquet supports dictionary encoding, which replaces repeated values with numeric codes, and run-length encoding, which replaces consecutive identical values with a single value and a count. Parquet also supports various compression algorithms, such as Snappy, GZIP, and ZSTD, that can further reduce the data size and improve the query performance.

Therefore, using a columnar storage file format, such as Parquet, will provide faster queries, as it allows Redshift Spectrum to scan only the relevant columns and skip the rest, reducing the amount of data read from S3. Additionally, partitioning the data based on the most common query predicates, such as date, time, region, etc., will provide faster queries, as it allows Redshift Spectrum to prune the partitions that do not match the query criteria, reducing the amount of data scanned from S3. Partitioning also improves the performance of joins and aggregations, as it reduces data skew and shuffling.

The other options are not as effective as using a columnar storage file format and partitioning the data. Using gzip compression to compress individual files to sizes that are between 1 GB and 5 GB will reduce the data size, but it will not improve the query performance significantly, as gzip is not a splittable compression algorithm and requires decompression before reading. Splitting the data into files that are less than 10 KB will increase the number of files and the metadata overhead, which will degrade the query performance. Using file formats that are not supported by Redshift Spectrum, such as XML, will not work, as Redshift Spectrum will not be able to read or parse the data. References:

Amazon Redshift Spectrum

Choosing the Right Data Format

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 4: Data Lakes and Data Warehouses, Section 4.3: Amazon Redshift Spectrum

Explanation: Amazon Macie is a fully managed data security and privacy service that uses machine learning to automatically discover, classify, and protect sensitive data in AWS, such as PII. By configuring Macie for automated sensitive data discovery, the company can minimize manual intervention while ensuring PII is identified before data is shared.

 AWS Glue is a fully managed service that provides a serverless data integration platform. It can automatically discover and categorize data from various sources, including SAP HANA, Microsoft SQL Server, MongoDB, Apache Kafka, and Amazon DynamoDB. It can also infer the schema of the data and store it in the AWS Glue Data Catalog, which is a central metadata repository. AWS Glue can then use the schema information to generate and run Apache Spark code to extract, transform, and load the data into an Amazon S3 bucket. AWS Glue can also monitor and optimize the performance and cost of the data pipeline, and handle any schema changes that may occur in the source data. AWS Glue can meet the SLA of loading the data into the S3 bucket within 15 minutes of data creation, as it can trigger the data pipeline based on events, schedules, or on-demand. AWS Glue has the least operational overhead among the options, as it does not require provisioning, configuring, or managing any servers or clusters. It also handles scaling, patching, and security automatically. References:

AWS Glue

[AWS Glue Data Catalog]

[AWS Glue Developer Guide]

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

AWS Step Functions natively emits state change events to Amazon EventBridge, which can trigger an Amazon SNS notification. This is the most direct and real-time way to alert on success/failure without relying on custom logging or polling.

“Step Functions automatically emits status changes that EventBridge can capture to trigger alerts or workflows. Use EventBridge to invoke an SNS topic for real-time alerts on job status.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

This provides real-time alerting and the least operational overhead.

S3 Intelligent-Tiering is a storage class that automatically moves objects between four access tiers based on the changing access patterns. The default access tier consists of two tiers: Frequent Access and Infrequent Access. Objects in the Frequent Access tier have the same performance and availability as S3 Standard, while objects in the Infrequent Access tier have the same performance and availability as S3 Standard-IA. S3 Intelligent-Tiering monitors the access patterns of each object and moves them between the tiers accordingly, without any operational overhead or retrieval fees. This solution can optimize S3 storage costs for data with unpredictable and variable access patterns, while ensuring millisecond latency for data retrieval. The other solutions are not optimal or relevant for this requirement. Using S3 Storage Lens standard metrics and activity metrics can provide insights into the storage usage and access patterns, but they do not automate the data movement between storage classes. Creating S3 Lifecycle policies for the S3 buckets can move objects to more cost-optimized storage classes, but they require manual configuration and maintenance, and they may incur retrieval fees for data that is accessed unexpectedly. Activating the Deep Archive Access tier for S3 Intelligent-Tiering can further reduce the storage costs for data that is rarely accessed, but it also increases the retrieval time to 12 hours, which does not meet the requirement of millisecond latency. References:

S3 Intelligent-Tiering

S3 Storage Lens

S3 Lifecycle policies

[AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide]