What's more, part of that PassSureExam Data-Engineer-Associate dumps now are free: https://drive.google.com/open?id=1_rJOYxz-x8-SQFcp2cHxjgBkp1kHrCX7
The Amazon Data-Engineer-Associate exam questions pdf is properly formatted to give candidates the asthenic and unformatted information they need to succeed in the Data-Engineer-Associate exam. In addition to the comprehensive material, a few basic and important questions are highlighted and discussed in the Data-Engineer-Associate Exam Material file. These questions are repeatedly seen in past AWS Certified Data Engineer - Associate (DEA-C01) exam papers. The AWS Certified Data Engineer - Associate (DEA-C01) practice questions are easy to access and can be downloaded anytime on your mobile, laptop, or MacBook.
| Section | Weight | Objectives |
|---|---|---|
| Data Ingestion and Transformation | 34% | - Orchestrate data pipelines
|
| Data Operations and Support | 22% | - Automate data pipelines
|
| Data Store Management | 26% | - Choose a data store
|
| Data Security and Governance | 18% | - Ensure data encryption
|
>> Amazon Data-Engineer-Associate Practice Online <<
Our Amazon Exam Questions greatly help AWS Certified Data Engineer - Associate (DEA-C01) (Data-Engineer-Associate) exam candidates in their preparation. Our AWS Certified Data Engineer - Associate (DEA-C01) (Data-Engineer-Associate) practice questions are designed and verified by prominent and qualified AWS Certified Data Engineer - Associate (DEA-C01) (Data-Engineer-Associate) exam dumps preparation experts. The qualified AWS Certified Data Engineer - Associate (DEA-C01) (Data-Engineer-Associate) exam questions preparation experts strive hard and put all their expertise to ensure the top standard and relevancy of Data-Engineer-Associate exam dumps topics.
NEW QUESTION # 111
A data engineer needs Amazon Athena queries to finish faster. The data engineer notices that all the files the Athena queries use are currently stored in uncompressed .csv format. The data engineer also notices that users perform most queries by selecting a specific column.
Which solution will MOST speed up the Athena query performance?
Answer: C
Explanation:
Amazon Athena is a serverless interactive query service that allows you to analyze data in Amazon S3 using standard SQL. Athena supports 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, changing the data format from CSV to Parquet and applying Snappy compression will most speed up the Athena query performance. Parquet is a column-oriented format that allows Athena to scan only the relevant columns and skip the rest, reducing the amount of data read from S3. Snappy is a compression algorithm that reduces the data size without compromising the query speed, as it is splittable and does not require decompression before reading. This solution will also reduce the cost of Athena queries, as Athena charges based on the amount of data scanned from S3.
The other options are not as effective as changing the data format to Parquet and applying Snappy compression. Changing the data format from CSV to JSON and applying Snappy compression will not improve the query performance significantly, as JSON is also a row-oriented format that does not support columnar access or encoding techniques. Compressing the CSV files by using Snappy compression will reduce the data size, but it will not improve the query performance significantly, as CSV is still a row-oriented format that does not support columnar access or encoding techniques. Compressing the CSV files by using gzjg compression will reduce the data size, but it will degrade the query performance, as gzjg is not a splittable compression algorithm and requires decompression before reading. References:
* Amazon Athena
* Choosing the Right Data Format
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 5: Data Analysis and Visualization, Section 5.1: Amazon Athena
NEW QUESTION # 112
A retail company is using an Amazon Redshift cluster to support real-time inventory management. The company has deployed an ML model on a real-time endpoint in Amazon SageMaker.
The company wants to make real-time inventory recommendations. The company also wants to make predictions about future inventory needs.
Which solutions will meet these requirements? (Select TWO.)
Answer: B,E
Explanation:
The company needs to use machine learning models for real-time inventory recommendations and future inventory predictions while leveraging both Amazon Redshift and Amazon SageMaker.
* Option A: Use Amazon Redshift ML to generate inventory recommendations.Amazon Redshift ML allows you to build, train, and deploy machine learning models directly from Redshift using SQL statements. It integrates with SageMaker to train models and run inference. This feature is useful for generating inventory recommendations directly from the data stored in Redshift.
* Option B: Use SQL to invoke a remote SageMaker endpoint for prediction.You can use SQL in Redshift to call a SageMaker endpoint for real-time inference. By invoking a SageMaker endpoint from within Redshift, the company can get real-time predictions on inventory, allowing for integration between the data warehouse and the machine learning model hosted in SageMaker.
* Option C (offline model training) and Option D (creating dashboards with SageMaker Autopilot) are not relevant to the real-time prediction and recommendation requirements.
* Option E (archiving inventory reports in Redshift) is not related to making predictions or recommendations.
References:
* Amazon Redshift ML Documentation
* Invoking SageMaker Endpoints from SQL
NEW QUESTION # 113
A company wants to use Apache Spark jobs that run on an Amazon EMR cluster to process streaming data.
The Spark jobs will transform and store the data in an Amazon S3 bucket. The company will use Amazon Athena to perform analysis.
The company needs to optimize the data format for analytical queries.
Which solutions will meet these requirements with the SHORTEST query times? (Select TWO.)
Answer: D,E
Explanation:
For high-performance analytics in Athena and EMR Spark, the columnar storage formats Parquet and ORC deliver the shortest query times. These formats minimize I/O, enable predicate pushdown, and support efficient compression and parallel reads.
When integrated with the AWS Glue Data Catalog, schema evolution is automatically tracked and managed across queries.
This combination (Parquet/ORC + Glue Catalog) is explicitly recommended by AWS for analytical workloads on S3 data lakes.
"For analytical workloads using Amazon EMR and Athena, store data in columnar formats such as Parquet or ORC. These formats provide optimized compression and query performance."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf
"Use the AWS Glue Data Catalog to manage table schemas and schema evolution for columnar data formats such as Parquet and ORC."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Therefore, using ORC or Parquet along with the Glue Data Catalog achieves the shortest Athena query times and seamless schema management.
NEW QUESTION # 114
A data engineer needs to maintain a central metadata repository that users access through Amazon EMR and Amazon Athena queries. The repository needs to provide the schema and properties of many tables. Some of the metadata is stored in Apache Hive. The data engineer needs to import the metadata from Hive into the central metadata repository.
Which solution will meet these requirements with the LEAST development effort?
Answer: A
Explanation:
The AWS Glue Data Catalog is an Apache Hive metastore-compatible catalog that provides a central metadata repository for various data sources and formats. You can use the AWS Glue Data Catalog as an external Hive metastore for Amazon EMR and Amazon Athena queries, and import metadata from existing Hive metastores into the Data Catalog. This solution requires the least development effort, as you can use AWS Glue crawlers to automatically discover and catalog the metadata from Hive, and use the AWS Glue console, AWS CLI, or Amazon EMR API to configure the Data Catalog as the Hive metastore. The other options are either more complex or require additional steps, such as setting up Apache Ranger for security, managing a Hive metastore on an EMR cluster or an RDS instance, or migrating the metadata manually.
References:
* Using the AWS Glue Data Catalog as the metastore for Hive (Section: Specifying AWS Glue Data Catalog as the metastore)
* Metadata Management: Hive Metastore vs AWS Glue (Section: AWS Glue Data Catalog)
* AWS Glue Data Catalog support for Spark SQL jobs (Section: Importing metadata from an existing Hive metastore)
* AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 131)
NEW QUESTION # 115
A data engineer configures a large number of AWS Glue jobs that all start up around the same time. All the jobs run for less than 1 hour in the same subnet of the same VPC. All the AWS Glue jobs run on a G.1X worker type.
Some of the jobs occasionally fail with the following error: "The specified subnet does not have enough free addresses to satisfy the request." What is the likely root cause of the error?
Answer: A
NEW QUESTION # 116
......
Are you still worried about the exam? Don’t worry! Our Data-Engineer-Associate exam torrent can help you overcome this stumbling block during your working or learning process. Under the instruction of our Data-Engineer-Associate test prep, you are able to finish your task in a very short time and pass the exam without mistakes to obtain the Amazon certificate. We will tailor services to different individuals and help them take part in their aimed exams after only 20-30 hours practice and training. Moreover for all your personal information, we will offer protection acts to avoid leakage and virus intrusion so as to guarantee the security of your privacy. What is most important is that when you make a payment for our Data-Engineer-Associate Quiz torrent, you will possess this product in 5-10 minutes and enjoy the pleasure and satisfaction of your study time.
New Data-Engineer-Associate Exam Pass4sure: https://www.passsureexam.com/Data-Engineer-Associate-pass4sure-exam-dumps.html
P.S. Free 2026 Amazon Data-Engineer-Associate dumps are available on Google Drive shared by PassSureExam: https://drive.google.com/open?id=1_rJOYxz-x8-SQFcp2cHxjgBkp1kHrCX7