Question # 1
A company has a data lake in Amazon S3. The company collects AWS CloudTrail logs for
multiple applications. The company stores the logs in the data lake, catalogs the logs in
AWS Glue, and partitions the logs based on the year. The company uses Amazon Athena
to analyze the logs.
Recently, customers reported that a query on one of the Athena tables did not return any data. A data engineer must resolve the issue.
Which combination of troubleshooting steps should the data engineer take? (Select TWO.) | | A. Confirm that Athena is pointing to the correct Amazon S3 location. | | B. Increase the query timeout duration. | | C. Use the MSCK REPAIR TABLE command. | | D. Restart Athena. | | E. Delete and recreate the problematic Athena table. |
A. Confirm that Athena is pointing to the correct Amazon S3 location.
C. Use the MSCK REPAIR TABLE command.
Explanation: The problem likely arises from Athena not being able to read from the correct
S3 location or missing partitions. The two most relevant troubleshooting steps involve
checking the S3 location and repairing the table metadata.
A. Confirm that Athena is pointing to the correct Amazon S3 location:
Reference:Amazon Athena Troubleshooting
C. Use the MSCK REPAIR TABLE command:
When new partitions are added to the S3 bucket without being reflected in the Glue Data
Catalog, Athena queries will not return data from those partitions. The MSCK REPAIR
TABLE command updates the Glue Data Catalog with the latest partitions.
Reference:MSCK REPAIR TABLE Command
Alternatives Considered:
B (Increase query timeout): Timeout issues are unrelated to missing data.
D (Restart Athena): Athena does not require restarting.
E (Delete and recreate table): This introduces unnecessary overhead when the issue can
be resolved by repairing the table and confirming the S3 location.
References:
Athena Query Fails to Return Data
Question # 2
A company saves customer data to an Amazon S3 bucket. The company uses server-side
encryption with AWS KMS keys (SSE-KMS) to encrypt the bucket. The dataset includes
personally identifiable information (PII) such as social security numbers and account
details.
Data that is tagged as PII must be masked before the company uses customer data for
analysis. Some users must have secure access to the PII data during the preprocessing
phase. The company needs a low-maintenance solution to mask and secure the PII data
throughout the entire engineering pipeline.
Which combination of solutions will meet these requirements? (Select TWO.) | | A. Use AWS Glue DataBrew to perform extract, transform, and load (ETL) tasks that mask
the PII data before analysis. | | B. Use Amazon GuardDuty to monitor access patterns for the PII data that is used in the
engineering pipeline. | | C. Configure an Amazon Made discovery job for the S3 bucket. | | D. Use AWS Identity and Access Management (IAM) to manage permissions and to control
access to the PII data. | | E. Write custom scripts in an application to mask the PII data and to control access. |
A. Use AWS Glue DataBrew to perform extract, transform, and load (ETL) tasks that mask
the PII data before analysis.
D. Use AWS Identity and Access Management (IAM) to manage permissions and to control
access to the PII data.
Explanation: To address the requirement ofmasking PII dataand ensuring secure access
throughout the data pipeline, the combination ofAWS Glue DataBrewandIAMprovides a
low-maintenance solution.
A. AWS Glue DataBrew for Masking:
Reference:AWS Glue DataBrew
D. AWS Identity and Access Management (IAM):
UsingIAM policiesallows fine-grained control over access to PII data, ensuring that only
authorized users can view or process sensitive data during the pipeline stages.
Reference:AWS IAM Best Practices
Alternatives Considered:
B (Amazon GuardDuty): GuardDuty is for threat detection and does not handle data
masking or access control for PII.
C (Amazon Macie): Macie can help discover sensitive data but does not handle the
masking of PII or access control.
E (Custom scripts): Custom scripting increases the operational burden compared to a
built-in solution like DataBrew.
References:
AWS Glue DataBrew for Data Masking
IAM Policies for PII Access Control
Question # 3
A company has used an Amazon Redshift table that is named Orders for 6 months. The
company performs weekly updates and deletes on the table. The table has an interleaved
sort key on a column that contains AWS Regions.
The company wants to reclaim disk space so that the company will not run out of storage
space. The company also wants to analyze the sort key column.
Which Amazon Redshift command will meet these requirements? | | A. VACUUM FULL Orders | | B. VACUUM DELETE ONLY Orders | | C. VACUUM REINDEX Orders | | D. VACUUM SORT ONLY Orders |
C. VACUUM REINDEX Orders
Explanation:
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.
References:
1: Amazon Redshift VACUUM
2: Amazon Redshift Interleaved Sorting
3: Amazon Redshift ANALYZE
Question # 4
A data engineer needs to schedule a workflow that runs a set of AWS Glue jobs every day.
The data engineer does not require the Glue jobs to run or finish at a specific time.
Which solution will run the Glue jobs in the MOST cost-effective way? | | A. Choose the FLEX execution class in the Glue job properties. | | B. Use the Spot Instance type in Glue job properties. | | C. Choose the STANDARD execution class in the Glue job properties. | | D. Choose the latest version in the GlueVersion field in the Glue job properties. |
A. Choose the FLEX execution class in the Glue job properties.
Explanation: The FLEX execution class allows you to run AWS Glue jobs on spare
compute capacity instead of dedicated hardware. This can reduce the cost of running nonurgent
or non-time sensitive data integration workloads, such as testing and one-time data
loads. The FLEX execution class is available for AWS Glue 3.0 Spark jobs. The other
options are not as cost-effective as FLEX, because they either use dedicated resources
(STANDARD) or do not affect the cost at all (Spot Instance type and GlueVersion).
References:
Introducing AWS Glue Flex jobs: Cost savings on ETL workloads
Serverless Data Integration – AWS Glue Pricing
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide
(Chapter 5, page 125)
Question # 5
A company uses Amazon S3 as a data lake. The company sets up a data warehouse by
using a multi-node Amazon Redshift cluster. The company organizes the data files in the
data lake based on the data source of each data file.
The company loads all the data files into one table in the Redshift cluster by using a
separate COPY command for each data file location. This approach takes a long time to
load all the data files into the table. The company must increase the speed of the data
ingestion. The company does not want to increase the cost of the process.
Which solution will meet these requirements? | | A. Use a provisioned Amazon EMR cluster to copy all the data files into one folder. Use a
COPY command to load the data into Amazon Redshift. | | B. Load all the data files in parallel into Amazon Aurora. Run an AWS Glue job to load the
data into Amazon Redshift. | | C. Use an AWS Glue job to copy all the data files into one folder. Use a COPY command to
load the data into Amazon Redshift. | | D. Create a manifest file that contains the data file locations. Use a COPY command to
load the data into Amazon Redshift. |
D. Create a manifest file that contains the data file locations. Use a COPY command to
load the data into Amazon Redshift.
Explanation: The company is facing performance issues loading data into Amazon
Redshift because it is issuing separate COPY commands for each data file location. The
most efficient way to increase the speed of data ingestion into Redshift without increasing
the cost is to use amanifest file.
Option D: Create a manifest file that contains the data file locations. Use a COPY
command to load the data into Amazon Redshift.A manifest file provides a list of
all the data files, allowing the COPY command to load all files in parallel from
different locations in Amazon S3. This significantly improves the loading speed
without adding costs, as it optimizes the data loading process in a single COPY
operation.
Other options (A, B, C) involve additional steps that would either increase the cost
(provisioning clusters, using Glue, etc.) or do not address the core issue of needing a
unified and efficient COPY process.
Question # 6
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? | | A. Change the data format from .csvto JSON format. Apply Snappy compression. | | B. Compress the .csv files by using Snappy compression. | | C. Change the data format from .csvto Apache Parquet. Apply Snappy compression. | | D. Compress the .csv files by using gzjg compression. |
C. Change the data format from .csvto Apache Parquet. Apply Snappy compression.
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. Roworiented
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 columnoriented
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 roworiented
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
Question # 7
A company uses Amazon S3 to store data and Amazon QuickSight to create visualizations.
The company has an S3 bucket in an AWS account named Hub-Account. The S3 bucket is
encrypted by an AWS Key Management Service (AWS KMS) key. The company's
QuickSight instance is in a separate account named BI-Account
The company updates the S3 bucket policy to grant access to the QuickSight service role.
The company wants to enable cross-account access to allow QuickSight to interact with the
S3 bucket.
Which combination of steps will meet this requirement? (Select TWO.) | | A. Use the existing AWS KMS key to encrypt connections from QuickSight to the S3
bucket. | | B. Add the 53 bucket as a resource that the QuickSight service role can access. | | C. Use AWS Resource Access Manager (AWS RAM) to share the S3 bucket with the Bl-
Account account. | | D. Add an IAM policy to the QuickSight service role to give QuickSight access to the KMS
key that encrypts the S3 bucket. | | E. Add the KMS key as a resource that the QuickSight service role can access. |
D. Add an IAM policy to the QuickSight service role to give QuickSight access to the KMS
key that encrypts the S3 bucket.
E. Add the KMS key as a resource that the QuickSight service role can access.
Explanation:
Problem Analysis:
Key Considerations:
Solution Analysis:
Implementation Steps:
json
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": { "AWS": "arn:aws:iam:::role/service-role/QuickSightRole" },
"Action": "s3:GetObject",
"Resource": "arn:aws:s3:::/*"
}
]
}
uk.co.certification.simulator.questionpool.PList@6987b2db
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": { "AWS": "arn:aws:iam:::role/service-role/QuickSightRole" },
"Action": [
"kms:Decrypt",
"kms:DescribeKey"
],
"Resource": "*"
}
]
}
uk.co.certification.simulator.questionpool.PList@27b44e91
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"s3:GetObject",
"kms:Decrypt"
],
"Resource": [
"arn:aws:s3:::/*",
"arn:aws:kms:::key/"
]
}
]
}
References:
Setting Up Cross-Account S3 Access
AWS KMS Key Policy Examples
Amazon QuickSight Cross-Account Access
Amazon Web Services Data-Engineer-Associate Exam Dumps
5 out of 5
Pass Your AWS Certified Data Engineer - Associate (DEA-C01) Exam in First Attempt With Data-Engineer-Associate Exam Dumps. Real AWS Certified Data Engineer Exam Questions As in Actual Exam!
— 152 Questions With Valid Answers
— Updation Date : 15-Apr-2025
— Free Data-Engineer-Associate Updates for 90 Days
— 98% AWS Certified Data Engineer - Associate (DEA-C01) Exam Passing Rate
PDF Only Price 49.99$
19.99$
Buy PDF
Speciality
Additional Information
Testimonials
Related Exams
- Number 1 Amazon Web Services AWS Certified Data Engineer study material online
- Regular Data-Engineer-Associate dumps updates for free.
- AWS Certified Data Engineer - Associate (DEA-C01) Practice exam questions with their answers and explaination.
- Our commitment to your success continues through your exam with 24/7 support.
- Free Data-Engineer-Associate exam dumps updates for 90 days
- 97% more cost effective than traditional training
- AWS Certified Data Engineer - Associate (DEA-C01) Practice test to boost your knowledge
- 100% correct AWS Certified Data Engineer questions answers compiled by senior IT professionals
Amazon Web Services Data-Engineer-Associate Braindumps
Realbraindumps.com is providing AWS Certified Data Engineer Data-Engineer-Associate braindumps which are accurate and of high-quality verified by the team of experts. The Amazon Web Services Data-Engineer-Associate dumps are comprised of AWS Certified Data Engineer - Associate (DEA-C01) questions answers available in printable PDF files and online practice test formats. Our best recommended and an economical package is AWS Certified Data Engineer PDF file + test engine discount package along with 3 months free updates of Data-Engineer-Associate exam questions. We have compiled AWS Certified Data Engineer exam dumps question answers pdf file for you so that you can easily prepare for your exam. Our Amazon Web Services braindumps will help you in exam. Obtaining valuable professional Amazon Web Services AWS Certified Data Engineer certifications with Data-Engineer-Associate exam questions answers will always be beneficial to IT professionals by enhancing their knowledge and boosting their career.
Yes, really its not as tougher as before. Websites like Realbraindumps.com are playing a significant role to make this possible in this competitive world to pass exams with help of AWS Certified Data Engineer Data-Engineer-Associate dumps questions. We are here to encourage your ambition and helping you in all possible ways. Our excellent and incomparable Amazon Web Services AWS Certified Data Engineer - Associate (DEA-C01) exam questions answers study material will help you to get through your certification Data-Engineer-Associate exam braindumps in the first attempt.
Pass Exam With Amazon Web Services AWS Certified Data Engineer Dumps. We at Realbraindumps are committed to provide you AWS Certified Data Engineer - Associate (DEA-C01) braindumps questions answers online. We recommend you to prepare from our study material and boost your knowledge. You can also get discount on our Amazon Web Services Data-Engineer-Associate dumps. Just talk with our support representatives and ask for special discount on AWS Certified Data Engineer exam braindumps. We have latest Data-Engineer-Associate exam dumps having all Amazon Web Services AWS Certified Data Engineer - Associate (DEA-C01) dumps questions written to the highest standards of technical accuracy and can be instantly downloaded and accessed by the candidates when once purchased. Practicing Online AWS Certified Data Engineer Data-Engineer-Associate braindumps will help you to get wholly prepared and familiar with the real exam condition. Free AWS Certified Data Engineer exam braindumps demos are available for your satisfaction before purchase order. The AWS Certified Data Engineer - Associate (DEA-C01) exam validates your expertise in building, deploying, and managing data pipelines on the AWS cloud. Earning this credential demonstrates to potential employers your ability to design scalable data solutions that leverage AWS services.
Here is what you need to know: - Target Audience: This exam is geared towards data engineers with 2-3 years of experience and 1-2 years of hands-on experience with AWS.
- Exam Format: You will face 65 questions (50 scored and 15 unscored) in a pass/fail format. AWS uses unscored questions to gauge the effectiveness of future exams.
Exam Content: The exam covers a broad range of topics, including:- We design and implement data pipelines using AWS services like Glue, Lambda, and Step Functions.
- Choosing the right data store (S3, DynamoDB, Redshift, etc.) based on data characteristics and access patterns.
- We are designing data models and ensuring data quality throughout the pipeline.
- We are monitoring and troubleshooting data pipelines for optimal performance and cost efficiency.
Preparing for the Exam:
To ace the exam, a comprehensive study plan is crucial. Here are some valuable resources:
Online communities: Join online forums and communities dedicated to AWS data engineering to connect with other aspiring data engineers and exchange study tips and resources.
Send us mail if you want to check Amazon Web Services Data-Engineer-Associate AWS Certified Data Engineer - Associate (DEA-C01) DEMO before your purchase and our support team will send you in email.
If you don't find your dumps here then you can request what you need and we shall provide it to you.
Bulk Packages
$50
- Get 3 Exams PDF
- Get $33 Discount
- Mention Exam Codes in Payment Description.
Buy 3 Exams PDF
$70
- Get 5 Exams PDF
- Get $65 Discount
- Mention Exam Codes in Payment Description.
Buy 5 Exams PDF
$100
- Get 5 Exams PDF + Test Engine
- Get $105 Discount
- Mention Exam Codes in Payment Description.
Buy 5 Exams PDF + Engine
 Jessica Doe
AWS Certified Data Engineer
We are providing Amazon Web Services Data-Engineer-Associate Braindumps with practice exam question answers. These will help you to prepare your AWS Certified Data Engineer - Associate (DEA-C01) exam. Buy AWS Certified Data Engineer Data-Engineer-Associate dumps and boost your knowledge.
What is the purpose of the AWS Certified Data Engineer - Associate (DEA-C01) Exam?
The exam is designed to validate skills in designing, building,
securing, and maintaining analytics solutions on AWS for individuals
with experience in data engineering roles.
What domains does the AWS Certified Data Engineer - Associate exam cover?
The exam covers various domains related to data engineering on AWS,
including data collection, storage, processing, and visualization,
utilizing services like Amazon S3, Amazon Redshift, Amazon DynamoDB,
Amazon EMR, AWS Glue, Amazon Kinesis, and more.
Are there any prerequisites for taking the AWS Certified Data Engineer - Associate exam?
While there are no mandatory prerequisites, candidates must have at
least two years of experience with AWS technology, proficiency in
programming languages, and familiarity with AWS security best practices.
What is the AWS Certified Data Engineer - Associate exam format?
The exam consists of multiple-choice and multiple-answer questions,
assessing candidates' ability to apply AWS data services to derive
insights from data.
How can candidates prepare for the AWS Certified Data Engineer - Associate exam?
Candidates can prepare using resources provided by AWS, such as
training courses, whitepapers, FAQs, and documentation. Practice exams
and study guides are also available to help understand the exam format.
How long is the AWS Certified Data Engineer - Associate certification valid?
The certification is valid for three years from the date of issuance.
How can professionals maintain their AWS Certified Data Engineer - Associate certification?
To maintain certification status, professionals must recertify by
either passing a recertification exam or advancing to a higher level of
certification.
Who can benefit from obtaining the AWS Certified Data Engineer - Associate certification?
Data engineers seeking to prove their skills in cloud data
engineering and advance their career opportunities can benefit from
obtaining this certification.
What critical AWS services are covered in the AWS Certified Data Engineer - Associate exam?
Services such as Amazon S3, Amazon Redshift, Amazon DynamoDB, Amazon
EMR, AWS Glue, and Amazon Kinesis are covered in the exam.
What skills does the AWS Certified Data Engineer - Associate certification demonstrate?
The certification demonstrates expertise in designing, building,
securing, and maintaining analytics solutions on AWS that are efficient,
cost-effective, and scalable.
|
