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What Is the DAS-C01 Certification Exam?
The DAS-C01 certification exam is a standardized assessment designed to measure a candidate's knowledge, competencies, and practical understanding within a defined professional field. It serves as the primary requirement for earning the AWS Certified Data Analytics, a credential that represents a recognized level of proficiency in its respective industry. Depending on the field, this may involve theoretical knowledge, applied problem-solving, regulatory understanding, or hands-on procedural competence.
The exam is typically developed and maintained by an accrediting body or professional organization that sets the standards for the AWS Certified Data Analytics. This ensures that anyone who earns the credential has met a consistent benchmark, regardless of where they studied or gained their experience. For many professionals, the DAS-C01 Certification Exam represents a formal checkpoint in their career, one that confirms readiness to take on greater responsibility within their chosen field.
Why the AWS Certified Data Analytics Certification Matters?
Certifications like the AWS Certified Data Analytics exist because industries need a reliable way to verify competence beyond a resume or a job title. Earning this credential signals to employers, clients, and colleagues that a professional has invested time in building a structured foundation of knowledge and has been evaluated against an established standard.
Beyond individual recognition, the AWS Certified Data Analytics certification often supports broader professional development. It can influence hiring decisions, contribute to internal advancement, or serve as a prerequisite for more specialized roles within the field. In many industries, certifications also help standardize expectations across organizations, making it easier for professionals to move between employers or sectors while carrying a credential that is widely understood and respected.
Who Should Take the DAS-C01 Exam?
The DAS-C01 exam is generally relevant to individuals who are either entering a field or looking to formalize skills they have already developed through experience. This can include early-career professionals seeking a credential to support their first steps into the industry, as well as experienced practitioners who want official recognition of knowledge gained on the job.
Students preparing to enter the workforce may also pursue the DAS-C01 exam as a way to strengthen their qualifications before graduating or applying for their first roles. In some fields, employers actively encourage or require staff to pursue this certification as part of ongoing professional development, particularly in industries where standards, safety, or compliance play a significant role in daily responsibilities.
Knowledge and Skills Evaluated in the AWS Certified Data Analytics - Specialty
The AWS Certified Data Analytics - Specialty is built to evaluate both foundational knowledge and the practical judgment needed to apply that knowledge in real situations. Candidates are generally expected to understand core principles and terminology relevant to their field, along with the reasoning behind established procedures, standards, or best practices.
Depending on the industry, this may include understanding regulatory requirements, following established protocols, applying analytical or technical methods, or exercising sound judgment in situations that require careful decision-making. Rather than testing isolated facts in a vacuum, the AWS Certified Data Analytics - Specialty tends to reward candidates who can connect concepts to realistic scenarios, reflecting the kind of thinking expected in day-to-day professional practice.
DAS-C01 Exam Preparation Resources
Preparing for the DAS-C01 certification exam becomes more effective when using high-quality and up-to-date study materials. MyCertsHub provides resources designed to help candidates build knowledge, practice consistently, and become familiar with the actual exam format.
How to Prepare for the DAS-C01 Certification Exam?
Effective preparation for the DAS-C01 certification exam usually begins with a clear understanding of the exam's objectives and structure. Reviewing official guidelines or documentation published by the certifying body provides the most accurate picture of what will be covered and how heavily different areas are weighted.
From there, many candidates benefit from building a structured study plan that breaks preparation into manageable sections over a set period of time. A well-organized DAS-C01 Study Guide can help sequence this material logically, especially for those approaching a topic for the first time. Consistent review, paired with realistic practice, tends to produce better retention than concentrated last-minute studying.
Practical experience, where applicable to the field, also plays an important role in preparation. Working through DAS-C01 Practice Questions and a DAS-C01 practice test can help candidates identify gaps in their understanding and become familiar with the format and pacing of the actual exam. In fields where hands-on skill is assessed, supplementing study with real-world practice or supervised experience often makes the difference between recognizing correct information and genuinely understanding it.
Benefits of Earning the AWS Certified Data Analytics Certification
Successfully earning the AWS Certified Data Analytics certification offers benefits that extend well beyond passing a single exam. It provides documented proof of competence that can be referenced on a resume, professional profile, or internal performance review, offering a clear, third-party validation of skill and knowledge.
The credential can also strengthen professional credibility when working with clients, patients, stakeholders, or colleagues who may not be positioned to evaluate technical or specialized knowledge directly. Over time, this recognition often contributes to expanded career opportunities, whether through new responsibilities, higher-level roles, or eligibility for additional certifications that build on this foundational credential.
Prepare for the DAS-C01 Exam with MyCertsHub
Preparing for the DAS-C01 exam is a process that benefits from organized, consistent effort rather than rushed, last-minute review. MyCertsHub is designed to support that process by offering study resources, practice materials, and educational content that help candidates understand what the AWS Certified Data Analytics - Specialty covers and how to approach their preparation thoughtfully.
Whether someone is just beginning to explore the AWS Certified Data Analytics or is in the final stages of reviewing material before their exam date, MyCertsHub aims to serve as a dependable resource throughout that journey. Every candidate's path to certification looks a little different, and the goal remains the same: to provide clear, genuinely useful information that supports real understanding of the subject matter.
Amazon DAS-C01 Sample Question Answers
Question # 1
A healthcare company ingests patient data from multiple data sources and stores it in anAmazon S3 staging bucket. An AWS Glue ETL job transforms the data, which is written toan S3-based data lake to be queried using Amazon Athena. The company wants to matchpatient records even when the records do not have a common unique identifier.Which solution meets this requirement?
A. Use Amazon Macie pattern matching as part of the ETLjob B. Train and use the AWS Glue PySpark filter class in the ETLjob C. Partition tables and use the ETL job to partition the data on patient name D. Train and use the AWS Glue FindMatches ML transform in the ETLjob
Answer: D
Question # 2
A company uses an Amazon EMR cluster with 50 nodes to process operational data andmake the data available for data analysts These jobs run nightly use Apache Hive with theApache Jez framework as a processing model and write results to Hadoop Distributed FileSystem (HDFS) In the last few weeks, jobs are failing and are producing the following errormessage"File could only be replicated to 0 nodes instead of 1"A data analytics specialist checks the DataNode logs the NameNode logs and networkconnectivity for potential issues that could have prevented HDFS from replicating data Thedata analytics specialist rules out these factors as causes for the issueWhich solution will prevent the jobs from failing'?
A. Monitor the HDFSUtilization metric. If the value crosses a user-defined threshold addtask nodes to the EMR cluster B. Monitor the HDFSUtilization metri.c If the value crosses a user-defined threshold addcore nodes to the EMR cluster C. Monitor the MemoryAllocatedMB metric. If the value crosses a user-defined threshold,add task nodes to the EMR cluster D. Monitor the MemoryAllocatedMB metric. If the value crosses a user-defined threshold,add core nodes to the EMR cluster.
Answer: C
Question # 3
A company is sending historical datasets to Amazon S3 for storage. A data engineer at thecompany wants to make these datasets available for analysis using Amazon Athena. Theengineer also wants to encrypt the Athena query results in an S3 results location by usingAWS solutions for encryption. The requirements for encrypting the query results are asfollows:Use custom keys for encryption of the primary dataset query results.Use generic encryption for all other query results.Provide an audit trail for the primary dataset queries that shows when the keys were usedand by whom.Which solution meets these requirements?
A. Use server-side encryption with S3 managed encryption keys (SSE-S3) for the primarydataset. Use SSE-S3 for the other datasets. B. Use server-side encryption with customer-provided encryption keys (SSE-C) for theprimary dataset. Use server-side encryption with S3 managed encryption keys (SSE-S3)for the other datasets. C. Use server-side encryption with AWS KMS managed customer master keys (SSE-KMSCMKs) for the primary dataset. Use server-side encryption with S3 managed encryptionkeys (SSE-S3) for the other datasets. D. Use client-side encryption with AWS Key Management Service (AWS KMS) customermanaged keys for the primary dataset. Use S3 client-side encryption with client-side keysfor the other datasets.
A bank operates in a regulated environment. The compliance requirements for the countryin which the bank operates say that customer data for each state should only be accessibleby the bank’s employees located in the same state. Bank employees in one state shouldNOT be able to access data for customers who have provided a home address in adifferent state.The bank’s marketing team has hired a data analyst to gather insights from customer datafor a new campaign being launched in certain states. Currently,data linking each customeraccount to its home state is stored in a tabular .csv file within a single Amazon S3 folder ina private S3 bucket. The total size of the S3 folder is 2 GB uncompressed. Due to thecountry’s compliance requirements, the marketing team is not able to access this folder.The data analyst is responsible for ensuring that the marketing team gets one-time accessto customer data for their campaign analytics project, while being subject to all thecompliance requirements and controls.Which solution should the data analyst implement to meet the desired requirements withthe LEAST amount of setup effort?
A. Re-arrange data in Amazon S3 to store customer data about each state in a different S3folder within the same bucket. Set up S3 bucket policies to provide marketing employeeswith appropriate data access under compliance controls. Delete the bucket policies afterthe project. B. Load tabular data from Amazon S3 to an Amazon EMR cluster using s3DistCp.Implement a custom Hadoop-based row-level security solution on the Hadoop DistributedFile System (HDFS) to provide marketing employees with appropriate data access undercompliance controls. Terminate the EMR cluster after the project. C. Load tabular data from Amazon S3 to Amazon Redshift with the COPY command. Usethe built-in row- level security feature in Amazon Redshift to provide marketing employeeswith appropriate data access under compliance controls. Delete the Amazon Redshifttables after the project. D. Load tabular data from Amazon S3 to Amazon QuickSight Enterprise edition by directlyimporting it as a data source. Use the built-in row-level security feature in AmazonQuickSight to provide marketing employees with appropriate data access undercompliance controls. Delete Amazon QuickSight data sources after the project is complete.
Answer: C
Question # 5
A company has multiple data workflows to ingest data from its operational databases intoits data lake on Amazon S3. The workflows use AWS Glue and Amazon EMR for dataprocessing and ETL. The company wants to enhance its architecture to provide automatedorchestration and minimize manual intervention Which solution should the company use tomanage the data workflows to meet these requirements?
A. AWS Glue workflows B. AWS Step Functions C. AWS Lambda D. AWS Batch
Answer: B
Explanation: This solution meets the requirements because: AWS Step Functions is a fully managed service that allows you to create and
orchestrate workflows that connect various AWS services, such as AWS Glue,
Amazon EMR, Amazon S3, and others1. You can use Step Functions to automate
your data workflows and handle complex logic, such as branching, parallel
processing, error handling, retries, and timeouts1.
AWS Step Functions provides a graphical interface that lets you design and
visualize your workflows as state machines, which are composed of a series of
steps or tasks1. You can use the AWS Step Functions console, the AWS CLI, or
the AWS SDKs to create and manage your state machines1.
AWS Step Functions integrates with AWS Glue and Amazon EMR to enable you
to run data processing and ETL jobs as part of your workflows23. You can use the
built-in connectors for these services to invoke them from your state
machines23. You can also use Step Functions to monitor the status of your jobs
and trigger actions based on the job outcomes23.
AWS Step Functions can help you minimize manual intervention by providing
features such as automatic retries, catch blocks, and fallback states, which allow
you to handle errors and failures gracefully in your workflows1. You can also use
Step Functions to trigger your workflows based on events, such as a new file in S3
or a CloudWatch alarm1.
Question # 6
A company has a fitness tracker application that generates data from subscribers. Thecompany needs real-time reporting on this data. The data is sent immediately, and theprocessing latency must be less than 1 second. The company wants to perform anomalydetection on the data as the data is collected. The company also requires a solution thatminimizes operational overhead.Which solution meets these requirements?
A. Amazon EMR cluster with Apache Spark streaming, Spark SQL, and Spark's machinelearning library (MLIib) B. Amazon Kinesis Data Firehose with Amazon S3 and Amazon Athena C. Amazon Kinesis Data Firehose with Amazon QuickSight D. Amazon Kinesis Data Streams with Amazon Kinesis Data Analytics
Answer: D
Question # 7
A company wants to ingest clickstream data from its website into an Amazon S3 bucket.The streaming data is in JSON format. The data in the S3 bucket must be partitioned byproduct_id.Which solution will meet these requirements MOST cost-effectively?
A. Create an Amazon Kinesis Data Firehose delivery stream to ingest the streaming datainto the S3 bucket. Enable dynamic partitioning. Specify the data field of productjd as onepartitioning key. B. Create an AWS Glue streaming job to partition the data by productjd before deliveringthe data to the S3 bucket. Create an Amazon Kinesis Data Firehose delivery stream.Specify the AWS Glue job as the destination of the delivery stream. C. Create an Amazon Kinesis Data Firehose delivery stream to ingest the streaming datainto the S3 bucket. Create an AWS Glue ETL job to read the data stream in the S3 bucket,partition the data by productjd, and write the data into another S3 bucket. D. Create an Amazon Kinesis Data Firehose delivery stream to ingest the streaming datainto the S3 bucket. Create an Amazon EMR cluster that includes a job to read the datastream in the S3 bucket, partition the data by productjd, and write the data into another S3bucket.
Answer: A
Question # 8
A machinery company wants to collect data from sensors. A data analytics specialist needsto implement a solution that aggregates the data in near-real time and saves the data to apersistent data store. The data must be stored in nested JSON format and must be queriedfrom the data store with a latency of single-digit milliseconds.Which solution will meet these requirements?
A. Use Amazon Kinesis Data Streams to receive the data from the sensors. Use AmazonKinesis Data Analytics to read the stream, aggregate the data, and send the data to anAWS Lambda function. Configure the Lambda function to store the data in AmazonDynamoDB. B. Use Amazon Kinesis Data Firehose to receive the data from the sensors. Use Amazon Kinesis Data Analytics to aggregate the data. Use an AWS Lambda function to read thedata from Kinesis Data Analytics and store the data in Amazon S3. C. Use Amazon Kinesis Data Firehose to receive the data from the sensors. Use an AWSLambda function to aggregate the data during capture. Store the data from Kinesis DataFirehose in Amazon DynamoDB. D. Use Amazon Kinesis Data Firehose to receive the data from the sensors. Use an AWSLambda function to aggregate the data during capture. Store the data in Amazon S3.
Answer: C
Explanation: This solution meets the requirements because:
Amazon Kinesis Data Firehose is a fully managed service that can capture,
transform, and load streaming data into AWS data stores, such as Amazon S3,
Amazon Redshift, Amazon Elasticsearch Service, and Amazon DynamoDB1. It
can receive data from sensors and other sources and deliver it to a destination
with near-real time latency.
AWS Lambda is a serverless compute service that can run code in response to
events and automatically manage the underlying compute resources2. It can be
used to perform custom transformations on the data during capture by Kinesis
Data Firehose3. It can aggregate the data according to the desired logic and
output format.
Amazon DynamoDB is a fully managed NoSQL database service that supports
key-value and document data models4. It can store nested JSON data as
document attributes and provide single-digit millisecond latency for queries. It can
be used as a persistent data store for the aggregated sensor data.
Question # 9
A company is building an analytical solution that includes Amazon S3 as data lake storageand Amazon Redshift for data warehousing. The company wants to use Amazon RedshiftSpectrum to query the data that is stored in Amazon S3.Which steps should the company take to improve performance when the company usesAmazon Redshift Spectrum to query the S3 data files? (Select THREE )Use gzip compression with individual file sizes of 1-5 GB
A. Use a columnar storage file format B. Partition the data based on the most common query predicates C. Split the data into KB-sized files. D. Keep all files about the same size. E. Use file formats that are not splittable
Answer: B,C,D
Question # 10
An IOT company is collecting data from multiple sensors and is streaming the data toAmazon Managed Streaming for Apache Kafka (Amazon MSK). Each sensor type hasits own topic, and each topic has the same number of partitions.The company is planning to turn on more sensors. However, the company wants toevaluate which sensor types are producing the most data sothat the company can scaleaccordingly. The company needs to know which sensor types have the largest values forthe following metrics: ByteslnPerSec and MessageslnPerSec.Which level of monitoring for Amazon MSK will meet these requirements?
A. DEFAULT level B. PER TOPIC PER BROKER level C. PER BROKER level D. PER TOPIC level
Answer: B
Question # 11
An airline has .csv-formatted data stored in Amazon S3 with an AWS Glue Data Catalog.Data analysts want to join this data with call center data stored in Amazon Redshift as partof a dally batch process. The Amazon Redshift cluster is already under a heavy load. Thesolution must be managed, serverless, well-functioning, and minimize the load on theexisting AmazonRedshift cluster. The solution should also require minimal effort anddevelopment activity.Which solution meets these requirements?
A. Unload the call center data from Amazon Redshift to Amazon S3 using an AWS Lambdafunction. Perform the join with AWS Glue ETL scripts. B. Export the call center data from Amazon Redshift using a Python shell in AWS Glue.Perform the join with AWS Glue ETL scripts. C. Create an external table using Amazon Redshift Spectrum for the call center data andperform the join with Amazon Redshift. D. Export the call center data from Amazon Redshift to Amazon EMR using Apache Sqoop.Perform the join with Apache Hive.
A company plans to store quarterly financial statements in a dedicated Amazon S3 bucket.The financial statements must not be modified or deleted after they are saved to the S3bucket.Which solution will meet these requirements?
A. Create the S3 bucket with S3 Object Lock in governance mode. B. Create the S3 bucket with MFA delete enabled. C. Create the S3 bucket with S3 Object Lock in compliance mode. D. Create S3 buckets in two AWS Regions. Use S3 Cross-Region Replication (CRR)between the buckets.
Answer: A
Explanation:
This solution meets the requirements because:
S3 Object Lock is a feature in Amazon S3 that allows users and businesses to
store files in a highly secure, tamper-proof way. It’s used for situations in which
businesses must be able to prove that data has not been modified or destroyed
after it was written, and it relies on a model known as write once, read many
(WORM)1.
S3 Object Lock provides two ways to manage object retention: retention periods
and legal holds. A retention period specifies a fixed period of time during which an
object remains locked. A legal hold provides the same protection as a retention
period, but it has no expiration date2.
S3 Object Lock has two retention modes: governance mode and compliance
mode. Governance mode allows users with specific IAM permissions to overwrite
or delete an object version before its retention period expires. Compliance mode
prevents anyone, including the root user of the account that owns the bucket, from
overwriting or deleting an object version or altering its lock settings until the
retention period expires2.
By creating the S3 bucket with S3 Object Lock in compliance mode, the company
can ensure that the quarterly financial statements are stored in a WORM model
and cannot be modified or deleted by anyone until the retention period expires or the legal hold is removed. This can help meet regulatory requirements that require
WORM storage, or to add another layer of protection against object changes and
deletion2.
Question # 13
A financial services firm is processing a stream of real-time data from an application byusing Apache Kafka and Kafka MirrorMaker. These tools run on premises and stream datato Amazon Managed Streaming for Apache Kafka (Amazon MSK) in the us-east-1 Region.An Apache Flink consumer running on Amazon EMR enriches the data in real time andtransfers the output files to an Amazon S3 bucket. The company wants to ensure that thestreaming application is highly available across AWS Regions with an RTO of less than 2minutes.Which solution meets these requirements?
A. Launch another Amazon MSK and Apache Flink cluster in the us-west-1 Region that isthe same size as the originalcluster in the us-east-1 Region. Simultaneously publish and process the data in bothRegions. In the event of a disaster that impacts one of the Regions, switch to the other Region. B. Set up Cross-Region Replication from the Amazon S3 bucket in the us-east-1 Region tothe us-west-1 Region. In the event of a disaster, immediately create Amazon MSK andApache Flink clusters in the us-west-1 Region and start publishing data to this Region. C. Add an AWS Lambda function in the us-east-1 Region to read from Amazon MSK andwrite to a global Amazon D. DynamoDB table in on-demand capacity mode. Export the data from DynamoDB toAmazon S3 in the us-west-1 Region. In the event of a disaster that impacts the us-east-1Region, immediately create Amazon MSK and Apache Flink clusters in the us-west-1Region and start publishing data to this Region. E. Set up Cross-Region Replication from the Amazon S3 bucket in the us-east-1 Region tothe us-west-1 Region. In the event of a disaster, immediately create Amazon MSK andApache Flink clusters in the us-west-1 Region and start publishing data to this Region.Store 7 days of data in on-premises Kafka clusters and recover the data missed during therecovery time from the on-premises cluster.
Answer: A
Question # 14
A company ingests a large set of sensor data in nested JSON format from different sourcesand stores it in an Amazon S3 bucket. The sensor data must be joined with performancedata currently stored in an Amazon Redshift cluster.A business analyst with basic SQL skills must build dashboards and analyze this data inAmazon QuickSight. A data engineer needs to build a solution to prepare the data for useby the business analyst. The data engineer does not know the structure of the JSON file.The company requires a solution with the least possible implementation effort.Which combination of steps will create a solution that meets these requirements? (SelectTHREE.)
A. Use an AWS Glue ETL job to convert the data into Apache Parquet format and write toAmazon S3. B. Use an AWS Glue crawler to catalog the data. C. Use an AWS Glue ETL job with the ApplyMapping class to un-nest the data and write toAmazon Redshift tables. D. Use an AWS Glue ETL job with the Regionalize class to un-nest the data and write toAmazon Redshift tables. E. Use QuickSight to create an Amazon Athena data source to read the Apache Parquetfiles in Amazon S3. F. Use QuickSight to create an Amazon Redshift data source to read the native AmazonRedshift tables.
Answer: B,D,F
Question # 15
An analytics team uses Amazon OpenSearch Service for an analytics API to be used bydata analysts. The OpenSearch Service cluster is configured with three master nodes. Theanalytics team uses Amazon Managed Streaming for Apache Kafka (Amazon MSK) and acustomized data pipeline to ingest and store 2 months of data in an OpenSearch Servicecluster. The cluster stopped responding, which is regularly causing timeout requests. Theanalytics team discovers the cluster is handling too many bulk indexing requests.Which actions would improve the performance of the OpenSearch Service cluster? (SelectTWO.)
A. Reduce the number of API bulk requests on the OpenSearch Service cluster and reducethe size of each bulk request. B. Scale out the OpenSearch Service cluster by increasing the number of nodes. C. Reduce the number of API bulk requests on the OpenSearch Service cluster, butincrease the size of each bulk request. D. Increase the number of master nodes for the OpenSearch Service cluster. E. Scale down the pipeline component that is used to ingest the data into the OpenSearchService cluster.
Answer: A,B
Question # 16
A global pharmaceutical company receives test results for new drugs from various testingfacilities worldwide. The results are sent in millions of 1 KB-sized JSON objects to anAmazon S3 bucket owned by the company. Thedata engineering team needs to processthose files, convert them into Apache Parquet format, and load them into Amazon Redshiftfor data analysts to perform dashboard reporting. The engineering team uses AWS Glue toprocess the objects, AWS Step Functions for process orchestration, and AmazonCloudWatch for job scheduling.More testing facilities were recently added, and the time to process files is increasing.What will MOST efficiently decrease the data processing time?
A. Use AWS Lambda to group the small files into larger files. Write the files back toAmazon S3. Process the files using AWS Glue and load them into Amazon Redshift tables. B. Use the AWS Glue dynamic frame file grouping option while ingesting the raw input files.Process the files and load them into Amazon Redshift tables. C. Use the Amazon Redshift COPY command to move the files from Amazon S3 intoAmazon Redshift tables directly. Process the files in Amazon Redshift. D. Use Amazon EMR instead of AWS Glue to group the small input files. Process the filesin Amazon EMR and load them into Amazon Redshift tables.
A company with a video streaming website wants to analyze user behavior to makerecommendations to users in real time Clickstream data is being sent to Amazon Kinesis Data Streams and reference data is stored in Amazon S3 The company wants a solutionthat can use standard SQL quenes The solution must also provide a way to look up precalculatedreference data while making recommendationsWhich solution meets these requirements?
A. Use an AWS Glue Python shell job to process incoming data from Kinesis Data StreamsUse the Boto3 library to write data to Amazon Redshift B. Use AWS Glue streaming and Scale to process incoming data from Kinesis DataStreams Use the AWS Glue connector to write data to Amazon Redshift C. Use Amazon Kinesis Data Analytics to create an in-application table based upon thereference data Process incoming data from Kinesis Data Streams Use a data stream towrite results to Amazon Redshift D. Use Amazon Kinesis Data Analytics to create an in-application table based upon thereference data Process incoming data from Kinesis Data Streams Use an Amazon KinesisData Firehose delivery stream to write results to Amazon Redshift
Answer: D
Question # 18
A manufacturing company is storing data from its operational systems in Amazon S3. Thecompany's business analysts need to perform one-time queries of the data in Amazon S3with Amazon Athena. The company needs to access the Athena service from the onpremisesnetwork by using a JDBC connection. The company has created a VPC. Securitypolicies mandate that requests to AWS services cannot traverse the internet.Which combination of steps should a data analytics specialist take to meet theserequirements? (Select TWO.)
A. Establish an AWS Direct Connect connection between the on-premises network and theVPC. B. Configure the JDBC connection to connect to Athena through Amazon API Gateway. C. Configure the JDBC connection to use a gateway VPC endpoint for Amazon S3. D. Configure the JDBC connection to use an interface VPC endpoint for Athena. E. Deploy Athena within a private subnet.
Answer: A,D
Explanation:
AWS Direct Connect is a service that establishes a dedicated network connection
between your on-premises network and AWS1. It can help you reduce network
costs, increase bandwidth throughput, and provide a more consistent network
experience than internet-based connections1. It can also help you meet the
security policy that requires requests to AWS services not to traverse the internet.
An interface VPC endpoint is a type of VPC endpoint that enables you to privately
connect your VPC to supported AWS services and VPC endpoint services
powered by AWS PrivateLink2. It is represented by one or more Elastic Network
Interfaces (ENIs) with private IP addresses in your VPC subnets2. It can also help
you meet the security policy that requires requests to AWS services not to traverse
the internet.
Amazon Athena now provides an interface VPC endpoint that allows you to
Question # 19
A data analyst is using AWS Glue to organize, cleanse, validate, and format a 200 GBdataset. The data analyst triggered the job to run with the Standard worker type. After 3hours, the AWS Glue job status is still RUNNING. Logs from the job run show no errorcodes. The data analyst wants to improve the job execution time without overprovisioning.Which actions should the data analyst take?
A. Enable job bookmarks in AWS Glue to estimate the number of data processing units(DPUs). Based on the profiled metrics, increase the value of the executor-cores jobparameter. B. Enable job metrics in AWS Glue to estimate the number of data processing units(DPUs). Based on theprofiled metrics, increase the value of the maximum capacity job parameter. C. Enable job metrics in AWS Glue to estimate the number of data processing units(DPUs). Based on the profiled metrics, increase the value of thespark.yarn.executor.memoryOverhead job parameter. D. Enable job bookmarks in AWS Glue to estimate the number of data processing units(DPUs). Based on the profiled metrics, increase the value of the num-executors jobparameter.
A human resources company maintains a 10-node Amazon Redshift cluster to run analytics queries on the company’s data. The Amazon Redshift cluster contains a product table and a transactions table, and both tables have a product_sku column. The tables are over 100 GB in size. The majority of queries run on both tables.Which distribution style should the company use for the two tables to achieve optimal query performance?
A. An EVEN distribution style for both tables B. A KEY distribution style for both tables C. An ALL distribution style for the product table and an EVEN distribution style for the transactions table D. An EVEN distribution style for the product table and an KEY distribution style for the transactions table
Answer: B
Question # 21
A large ride-sharing company has thousands of drivers globally serving millions of unique customers every day. The company has decided to migrate an existing data mart to Amazon Redshift. The existing schema includes the following tables. A trips fact table for information on completed rides. A drivers dimension table for driver profiles. A customers fact table holding customer profile information. The company analyzes trip details by date and destination to examine profitability by region. The drivers data rarely changes. The customers data frequently changes. What table design provides optimal query performance?
A. Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the drivers and customers tables. B. Use DISTSTYLE EVEN for the trips table and sort by date. Use DISTSTYLE ALL for the drivers table. Use DISTSTYLE EVEN for the customers table. C. Use DISTSTYLE KEY (destination) for the trips table and sort by date. Use DISTSTYLE ALL for the drivers table. Use DISTSTYLE EVEN for the customers table. D. Use DISTSTYLE EVEN for the drivers table and sort by date. Use DISTSTYLE ALL for both fact tables.
An analytics software as a service (SaaS) provider wants to offer its customers business intelligence <BI) reporting capabilities that are self-service The provider is using AmazonQuickSight to build these reports The data for the reports resides in a multi-tenant database, but each customer should only be able to access their own data The provider wants to give customers two user role options • Read-only users for individuals who only need to view dashboards • Power users for individuals who are allowed to create and share new dashboards withother users Which QuickSight feature allows the provider to meet these requirements'?
A. Embedded dashboards B. Table calculations C. Isolated namespaces D. SPICE
Answer: A
Question # 23
A software company wants to use instrumentation data to detect and resolve errors to improve application recovery time. The company requires API usage anomalies, like error rate and response time spikes, to be detected in near-real time (NRT) The company also requires that data analysts have access to dashboards for log analysis in NRT Which solution meets these requirements'?
A. Use Amazon Kinesis Data Firehose as the data transport layer for logging data Use Amazon Kinesis Data Analytics to uncover the NRT API usage anomalies Use Kinesis Data Firehose to deliver log data to Amazon OpenSearch Service (Amazon Elasticsearch Service) for search, log analytics, and application monitoring Use OpenSearch Dashboards (Kibana) in Amazon OpenSearch Service (Amazon Elasticsearch Service) for the dashboards. B. Use Amazon Kinesis Data Analytics as the data transport layer for logging data. Use Amazon Kinesis Data Streams to uncover NRT monitoring metrics. Use Amazon Kinesis Data Firehose to deliver log data to Amazon OpenSearch Service (Amazon Elasticsearch Service) for search, log analytics, and application monitoring Use Amazon QuickSight for the dashboards C. Use Amazon Kinesis Data Analytics as the data transport layer for logging data and to uncover NRT monitoring metrics Use Amazon Kinesis Data Firehose to deliver log data to Amazon OpenSearch Service (Amazon Elasticsearch Service) for search, log analytics, and application monitoring Use OpenSearch Dashboards (Kibana) in Amazon OpenSearch Service (Amazon Elasticsearch Service) for the dashboards D. Use Amazon Kinesis Data Firehose as the data transport layer for logging data Use Amazon Kinesis Data Analytics to uncover NRT monitoring metrics Use Amazon Kinesis Data Streams to deliver log data to Amazon OpenSearch Service (Amazon Elasticsearch Service) for search, log analytics, and application monitoring Use Amazon QuickSight for the dashboards.
Answer: C
Question # 24
An advertising company has a data lake that is built on Amazon S3. The company uses AWS Glue Data Catalog to maintain the metadata. The data lake is several years old and its overall size has increased exponentially as additional data sources and metadata are stored in the data lake. The data lake administrator wants to implement a mechanism to simplify permissions management between Amazon S3 and the Data Catalog to keep them in sync Which solution will simplify permissions management with minimal development effort?
A. Set AWS Identity and Access Management (1AM) permissions tor AWS Glue B. Use AWS Lake Formation permissions C. Manage AWS Glue and S3 permissions by using bucket policies D. Use Amazon Cognito user pools.
Answer: B
Question # 25
A utility company wants to visualize data for energy usage on a daily basis in Amazon QuickSight A data analytics specialist at the company has built a data pipeline to collect and ingest the data into Amazon S3 Each day the data is stored in an individual csv file in an S3 bucket This is an example of the naming structure 20210707_datacsv 20210708_datacsv To allow for data querying in QuickSight through Amazon Athena the specialist used an AWS Glue crawler to create a table with the path "s3 //powertransformer/20210707_data csv" However when the data is queried, it returns zero rows How can this issue be resolved?
A. Modify the IAM policy for the AWS Glue crawler to access Amazon S3. B. Ingest the files again. C. Store the files in Apache Parquet format. D. Update the table path to "s3://powertransformer/".
Answer: D
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