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NEW QUESTION 33 An operations team notices that a few AWS Glue jobs for a given ETL application are failing. The AWS Glue jobs read a large number of small JSON files from an Amazon S3 bucket and write the data to a different S3 bucket in Apache Parquet format with no major transformations. Upon initial investigation, a data engineer notices the following error message in the History tab on the AWS Glue console: "Command Failed with Exit Code 1." Upon further investigation, the data engineer notices that the driver memory profile of the failed jobs crosses the safe threshold of 50% usage quickly and reaches 90-95% soon after. The average memory usage across all executors continues to be less than 4%. The data engineer also notices the following error while examining the related Amazon CloudWatch Logs. What should the data engineer do to solve the failure in the MOST cost-effective way?

• A. Modify maximum capacity to increase the total maximum data processing units (DPUs) used.
• B. Increase the fetch size setting by using AWS Glue dynamics frame.
• C. Change the worker type from Standard to G.2X.
• D. Modify the AWS Glue ETL code to use the 'groupFiles': 'inPartition' feature.

Answer: D Explanation: https://docs.aws.amazon.com/glue/latest/dg/monitor-profile-debug-oom-abnormalities.html#monitor-debug-oom-fix   NEW QUESTION 34 A company receives data from its vendor in JSON format with a timestamp in the file name. The vendor uploads the data to an Amazon S3 bucket, and the data is registered into the company's data lake for analysis and reporting. The company has configured an S3 Lifecycle policy to archive all files to S3 Glacier after 5 days. The company wants to ensure that its AWS Glue crawler catalogs data only from S3 Standard storage and ignores the archived files. A data analytics specialist must implement a solution to achieve this goal without changing the current S3 bucket configuration. Which solution meets these requirements?

• A. Use the excludeStorageClasses property in the AWS Glue Data Catalog table to exclude files on S3 Glacier storage
• B. Schedule an automation job that uses AWS Lambda to move files from the original S3 bucket to a new S3 bucket for S3 Glacier storage.
• C. Use the exclude patterns feature of AWS Glue to identify the S3 Glacier files for the crawler to exclude.
• D. Use the include patterns feature of AWS Glue to identify the S3 Standard files for the crawler to include.

Answer: C   NEW QUESTION 35 A hospital uses wearable medical sensor devices to collect data from patients. The hospital is architecting a near-real-time solution that can ingest the data securely at scale. The solution should also be able to remove the patient's protected health information (PHI) from the streaming data and store the data in durable storage. Which solution meets these requirements with the least operational overhead?

• A. Ingest the data using Amazon Kinesis Data Firehose to write the data to Amazon S3. Have Amazon S3 trigger an AWS Lambda function that parses the sensor data to remove all PHI in Amazon S3.
• B. Ingest the data using Amazon Kinesis Data Streams, which invokes an AWS Lambda function using Kinesis Client Library (KCL) to remove all PHI. Write the data in Amazon S3.
• C. Ingest the data using Amazon Kinesis Data Streams to write the data to Amazon S3. Have the data stream launch an AWS Lambda function that parses the sensor data and removes all PHI in Amazon S3.
• D. Ingest the data using Amazon Kinesis Data Firehose to write the data to Amazon S3. Implement a transformation AWS Lambda function that parses the sensor data to remove all PHI.

Answer: D Explanation: Explanation https://aws.amazon.com/blogs/big-data/persist-streaming-data-to-amazon-s3-using-amazon-kinesis-firehose-and-   NEW QUESTION 36 A telecommunications company is looking for an anomaly-detection solution to identify fraudulent calls. The company currently uses Amazon Kinesis to stream voice call records in a JSON format from its on-premises database to Amazon S3. The existing dataset contains voice call records with 200 columns. To detect fraudulent calls, the solution would need to look at 5 of these columns only. The company is interested in a cost-effective solution using AWS that requires minimal effort and experience in anomaly-detection algorithms. Which solution meets these requirements?

• A. Use Kinesis Data Firehose to detect anomalies on a data stream from Kinesis by running SQL queries, which compute an anomaly score for all calls and store the output in Amazon RDS. Use Amazon Athena to build a dataset and Amazon QuickSight to visualize the results.
• B. Use Kinesis Data Analytics to detect anomalies on a data stream from Kinesis by running SQL queries, which compute an anomaly score for all calls. Connect Amazon QuickSight to Kinesis Data Analytics to visualize the anomaly scores.
• C. Use an AWS Glue job to transform the data from JSON to Apache Parquet. Use AWS Glue crawlers to discover the schema and build the AWS Glue Data Catalog. Use Amazon Athena to create a table with a subset of columns. Use Amazon QuickSight to visualize the data and then use Amazon QuickSight machine learning-powered anomaly detection.
• D. Use an AWS Glue job to transform the data from JSON to Apache Parquet. Use AWS Glue crawlers to discover the schema and build the AWS Glue Data Catalog. Use Amazon SageMaker to build an anomaly detection model that can detect fraudulent calls by ingesting data from Amazon S3.

Answer: C   NEW QUESTION 37 A company has developed several AWS Glue jobs to validate and transform its data from Amazon S3 and load it into Amazon RDS for MySQL in batches once every day. The ETL jobs read the S3 data using a DynamicFrame. Currently, the ETL developers are experiencing challenges in processing only the incremental data on every run, as the AWS Glue job processes all the S3 input data on each run. Which approach would allow the developers to solve the issue with minimal coding effort?

• A. Have the ETL jobs read the data from Amazon S3 using a DataFrame.
• B. Create custom logic on the ETL jobs to track the processed S3 objects.
• C. Enable job bookmarks on the AWS Glue jobs.
• D. Have the ETL jobs delete the processed objects or data from Amazon S3 after each run.

Answer: D   NEW QUESTION 38 ...... P.S. Free 2022 Amazon AWS-Certified-Data-Analytics-Specialty dumps are available on Google Drive shared by itPass4sure: https://drive.google.com/open?id=1peCZCFD3TBa4w7iYuaIyGBejpKSXYWYH